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6.6 Scripting Reference

Saves a copy of the current project. There are three required parameters and one optional parameter for this command. The first parameter is the path and the directory where the project is to be saved. The second parameter is the name of the project copy. The third parameter indicates if the open project should remain the current project or switch to the project copy.

The options for the third parameter are:

0: Keep the current project open.
1: Switch to the copy of the project.

The optional parameter specifies if password protection should be kept for the project copy, if the original project had a password associated with it. If password protection is kept, then the password for the project copy will be the same as for the original project. This option is specified by the following:

0: Do not save password protection in the copy of the project.
1 (default): Keep the password protection for the copy of the project.

Closing a Project

Syntax

ghi.closeProject()

Example

ghi.closeProject()

This command will close the current project without saving the state of the project. In order to save the project state before closing, first use the saveProject command and then the closeProject command.

Exiting the Program

Syntax

ghi.exit()

Example

file path list = ghi.chooseFile(file extension mask, dialog title, [Optional Parameter])

Example

myFilePaths = ghi.chooseFile(“*.txt”, “Choose A File Please”, ghi.const.ChooseSingleFile)

This method will display a dialog window for browsing and selecting a file or multiple files. If a file or multiple files are selected then a tuple with the complete path to all files is returned. If the dialog is canceled then an empty tuple is returned. There are two required parameters. The first parameter defines a file extension mask. For example, if the file extension mask is “*.txt” then the dialog will only display files with the .txt extension. The second parameter is the title to be displayed in the dialog’s title bar.

The third argument is optional.

Optional third parameter: Specifies the number of files to choose, or indicates a file should be created.
- ghi.const.ChooseSingleFile (default): Only a single existing file may be selected.
- ghi.const.ChooseMultipleFiles: Allow multiple selection of existing files.
- ghi.const.ChooseSaveAs: Dialog is a “Save As” dialog. The file need not exist.

Choosing a Directory

Syntax

directory path list = ghi.chooseDirectory(dialog caption text, start directory)

Example

myDirPath = ghi.chooseDirectory(“Select a directory to save the file”, “c:/ProjectFiles/”)

This command presents a dialog allowing directory selection.

The return value is the path of the selected directory.

The parameters for this command are as follows:

caption: the string to be used as the selection dialog caption
startDir: (optional) the directory that the selection dialog will be opened to initially.

Opening a Web URL

Syntax

ghi.openUrl(url path)

Example

ghi.openUrl( http://www.goldenhelix.com)

Opens the specified URL in the default web browser.

Commands for Importing Data

Importing a Text File

Syntax

new spreadsheet object = ghi.importText(path and file name of text file, name for dataset, [Optional Parameters])

Example

mySS = ghi.importText(“/data/myTextFile.csv”, “My Data”, rowLabelColumn = 1, delimiter = “,”, missingEncoding = “?”, readGenetic = 1, alleleDelimiter = “_”, skipNumLines = 0)

This command may be used to import a text based file which uses any one-character delimiter. A spreadsheet generated from the data file is returned and may be assigned to a variable.

The optional parameters for this command specified by keyword arguments. The keyword arguments and their defaults are detailed below. Note: Not all keyword arguments need to be used.

rowLabelColumn:
- if not specified, default is to generate generic row labels
- 1: or any number greater than 0 and less than the number of columns in the data file
delimiter:
- “,”: for comma-delimited files (default setting)
- “ ”: for space-delimited files
- “\t”: for tab-delimited files
- or any other one character delimiter
missingEncoding:
- “?”: –question-mark (default setting)
- or any other one character string signifying a missing value
readGenetic:
- 0: read all data of the form “A_B” as categorical data
- 1: read all data of the for “A_B” as genetic data
alleleDelimiter:
- “_”: –underscore (default setting)
- “/”: –slash
- any other one character string corresponding to the allele delimiter
skipNumLines:: skips the specified number of lines before reading the file.
- 0: (default setting)
- any other number between 1 and the total number of rows in the dataset

These optional parameters are the same ones for the following functions detailed below:

ghi.importTextPedigree(…)
ghi.importTextPhenotype(…)

Importing Various Third-Party File Formats

Syntax

new spreadsheet object = ghi.importData(path and file name of file, [Optional Parameters])

Example

mySS = ghi.importData(“/data/myFile.xls”, columnHeaderMode = ghi.const.HeaderAutoDetect, alleleDelimiter = “_”, rowLabelColumn = 1,
workSheet = 1)

Corresponding to the Import Third Party Files dialog, this command allows for importing various file formats into the project.

The optional parameters for this command are specified by keyword arguments. The keyword arguments and their defaults are detailed below. Note: Not all keyword arguments need to be used.

columnHeaderMode:
- ghi.const.HeaderAutoDetect: Autodetect if the first row contains column names (default setting)
- ghi.const.HeaderFirstRow: Use the first row as column names
- ghi.const.HeaderGenerate: Generate generic column names and use the first row as data
alleleDelimiter:
- “_”: –underscore (default setting)
- “/”: –slash
- any other one character string corresponding to the allele delimiter
rowLabelColumn:
- if not specified, default is to generate generic row labels
- 1 or any number greater than 0 and less than the number of columns in the data file
workSheet:
- 1 (default if applicable)
- any other number between 1 and the total number of worksheets if this option is applicable to the file type.

Importing PED Files

Syntax

new spreadsheet object = ghi.importPED(dataset name, path and file name of PED file, path and file name of MAP file, [Optional Parameters])

Example

mySS = ghi.importPED(“My PED Dataset”, “/data/myPEDFile.ped”, “/data/myPEDFile.map”, missingPhenotype = -9, missingGenotype = “?”)

PED files can be imported using the ghi.importPED(…) scripting command. This command takes three string parameters for the dataset name, the PED and MAP file paths, as well as two optional arguments to specify missing values for phenotypes and genotypes.

The optional parameters for this command specified by keyword arguments, these arguments and their defaults are detailed below. Note: Not all keyword arguments need to be used.

missingPhenotype:
- -9 (default setting)
- or any other integer value signifying a missing value
missingGenotype:
- “0” – zero string (default setting)
- or any other one character string signifying a missing value

Importing TPED Files

Syntax

new spreadsheet object = ghi.importTPED(dataset name, path and file name of TPED file, path and file name of TFAM file, [Optional Parameters])

Example

mySS = ghi.importTPED(“My TPED Dataset”, “/data/myTPEDFile.tped”, “/data/myTPEDFile.tfam”, missingPhenotype = -9, missingGenotype = “0”)

TPED files can be imported using the ghi.importTPED(…) scripting command. This command takes three string parameters for the dataset name, the TPED and TFAM file paths, as well as two optional arguments to specify missing values for phenotypes and genotypes.

The optional parameters for this command specified by keyword arguments, these arguments and their defaults are detailed below. Note: Not all keyword arguments need to be used.

missingPhenotype:
- -9 (default setting)
- or any other integer value signifying a missing value
missingGenotype:
- “0” – zero string (default setting)
- or any other one character string signifying a missing value

This command requires the following parameters in this order:

Input file list: This list should contain the paths and names for all CHP files that are to be imported. This must be a Python List.
Dataset Name: A name for the dataset created on import of the CHP files.

The optional parameters for this command are specified by keyword arguments. The keyword arguments and their defaults are detailed below.

libraryPath:
- Allows the path to a directory containing Affymetrix library files to be specified.
confidenceScore:
- Specify a value to use as a confidence score upper limit threshold if other than the default.

Importing Affymetrix CEL Files

Syntax

new spreadsheet object = ghi.importCEL(list of paths and file names of CEL files, reference sheet node ID, reference status column number, marker map name from marker maps folder, [Optional Parameters])

Example

mySS = ghi.importCEL([“/data/sample1.cel”, “/data/sample2.cel”, “/data/sample3.cel”], 45, 3,
“/MyMarkerMap.dsm”, affyLibDir = “/Golden Helix SVS/AffyLibraryFiles”,
tempDir = “c:/Temp”, dropReferences = 0)

Using the ghi.importCEL(…) command, a dataset and spreadsheet containing the normalized copy number intensity values contained in the input CEL files will be created. The resulting dataset and corresponding spreadsheet will be available for immediate use in SVS.

This command requires the following parameters in this order:

Input file list: This list should contain the paths and names for all CEL files that are to be imported. This must be a Python List.
Reference Status Sheet Node ID: An integer representing the node ID of the spreadsheet object that will contain the reference column.
Reference Status Column: An integer that represents the column number of the actual reference status column within the reference status spreadsheet.
Marker Map Name : The path and file name of the marker map DSM file to be used.

The optional parameters for this command are specified by keyword arguments. The keyword arguments and their defaults are detailed below. Note: Not all keyword arguments need to be used.

affyLibDir:
- Allows the path to a directory containing Affymetrix library files to be specified.
tempDir:
- Allows a directory to use for temporary file creation to be specified.
mappingSheetId:
- An integer representing the node ID for the NSP/STY mapping spreadsheet (For Affy 500k CEL files).
dropReferences:
- 0 (default): If references should be kept in the output spreadsheet.
- 1: If references should be dropped in the output spreadsheet.
importIntermediate
- 0 (default): Do not import intermediate files.
- 1: Import intermediate files.
datasetName
- allows specification of the new dataset name
- (default): “Affy CEL Dataset”

Note:

If the affyLibDir keyword argument is omitted, the appropriate CDF files must be in the AffyLibraryFiles directory of your SVS directory.
The temporary directory used by the import process must be on a local disk. If your project is on a network drive, be sure to specify tempDir as a directory on your local disk.
If you are using Affymetrix 500k data, and are including both NSP and STY CEL files, you must choose an NSP/STY mapping spreadsheet in order for the import to be successful.

mmResult = ghi.convertAffyAnnotations([“/markerMaps/Mapping250K_Nsp.na24.annot.csv”,
“/markerMaps/Mapping250K_Sty.na24.annot.csv”])

This command converts Affymetrix Annotation files from the text file format available from direct download from the Affymetrix website to a DSM marker map file. This is useful for converting older annotation versions that are not available from the Golden Helix, Inc. Affymetrix Annotation Download utility in SVS.

True is returned if the marker map is successfully converted.

The required parameter is as follows:

List of file paths of the names of the text files to convert and/or merge if multiple files are specified.

Converting a Text or MAP File into a Genetic Marker Map DSM File

Syntax

ghi.convertMarkerMap(path and file name of marker map, Marker Map name, SNP ID column number, Chromosome column number, Position column number, [Optional Parameters])

Example

myMM = ghi.convertMarkerMap(“/data/myTextMarkerMap.csv”,“MarkerMap_v1”, 1, 3, 2, delimiter = “,”, missingEncoding = “?”, skipNumLines = 0, optColumns = {“RS ID”:4, “cytoband”:5, “gene”:6, “Allele A/B”:7})

This command allows a marker map stored in a text file or a MAP file to be converted to the DSM (Dataset Storage Marker map) format. All marker maps in SVS versions 7.0 or greater need to be converted to this format to be used as a marker map. There are five required parameters for this command, and two optional parameter dictionaries, which provide the ability to import additional marker map columns as well as change the text file import parameters.

The required parameters, in order, are as follows:

Path and file name of text file or MAP file to convert. This file will have one of the following file extensions: .txt, .csv, or .map.
Marker map name to easily identify the marker map from the list in the Marker Maps folder.
SNP or Probe ID integer column number in the text file that is being converted.
Chromosome integer column number in the text file that is being converted.
Position integer column number in the text file that is being converted.

There are three text file import parameters that can be specified, their keyword arguments and defaults are detailed below. Note: Not all keyword arguments need to be used.

delimiter:
- “,”: for comma-delimited files (default setting)
- “ ”: for space-delimited files
- “\t”: for tab-delimited files
- or any other one character delimiter
missingEncoding:
- “?”: –question-mark (default setting)
- or any other one character string signifying a missing value
skipNumLines:
- 0: (default setting)
- any other number between 1 and the total number of rows in the dataset

To specify additional marker map columns to import, a Python dictionary must be used with the keyword argument optColumns. A dictionary is indicated with curly braces and and assignment of a parameter is indicated with a colon (:). Any number of additional columns or fields can be imported using the dictionary, all fields separated with a comma. For example, if it is desired that the fields RS ID, cytoband, gene, and Allele A/B be included in the marker map, then these fields would be specified as follows. See the example above for the full syntax.

optColumns = {‘‘RS ID’’:4, ‘‘cytoband’’:5, ‘‘gene’’:6, ‘‘Allele A/B’’:7}

Importing a Genetic Marker Map DSM File as a Spreadsheet

Syntax

new spreadsheet object = ghi.importMapAsSpreadsheet(marker map DSM file name)

Example

myMMspreadsheet = ghi.importMapAsSpreadsheet(“/Golden Helix SVS/MarkerMaps/myMarkerMap.dsm”)

myNodeName = myNode.getName()

The name of a navigator node can be obtained using this command with any Python object that corresponds to a navigator node.

Get the Node Options

Get the Parent of the Specified Node

Syntax

new node object = node object.getParent()

Example

newObject = myNode.getParent()

To get an object representing the parent of the specified node, use the above command. An object representing the parent node will be returned.

Commands For User Input

Prompting the User for a Marker Map

Syntax

marker map name = ghi.chooseMarkerMap()

Example

Example

myUserInput = ghi.promptDialog([{”name”:”string”,”label”:”Enter string:”, ”type”:”string”, ”tooltip”:”Any string will do”,’ ”default”:”Hello World!”},
{”name”:”myInt”,”label”:”Enter Integer:”, ”type”:”integer”, ”min”:0, ”max”:100, ”default”:50},
{”name”:”value”, ”label”:”Enter double”, ”type”:”double”, ”min”:-1, ”default”:100.0001},
{”name”:”method”, ”label”:”Select method”, ”type”:”combobox”, ”list”:[”method 1”, ”method 2”, ”method 3”]}],
title=”User Input”,width=400)

The example above displays a dialog box with four input fields, three text boxes and a drop down list. The first text box is for a string, the second for an integer, and the third for a real valued number. The drop down list allows the user to specify a method.

The following optional keywords can be specified with the promptDialog command after the list of input fields.

scrollableLayout=<boolean> If True, the layout for the input dialog will be placed in a scrollable frame. This is useful if you have too many inputs to fit on a normal height screen.
width=<int> and height:<int> Specify minimum sizes for the input dialog. This will not shrink the dialog to be smaller than is required to display all the widgets when not using a scrollable layout but can be used to make the dialog wider or taller than it otherwise would be.
title=<string> Provide a window title for the input dialog.
okText=<int> Provide an alternative text for the OK button on the input dialog. For example, you may want the button to say Run or Next>> if you intend to have further dialogs of user input.

The input fields of the promptDialog() function are specified in a list of dictionaries, each with some required and optional attributes. Every item requires a type field. Items with a type that produces an output require a name field, which will be used as the key in the results dictionary to store the output for that item. For example, to access the integer value you would use the command myUserInput[‘‘myInt’’] since the name attribute is defined as “myInt”.

There are a number of data entry methods available. Most items require a label attribute for providing a user prompt. Items with an explicit label left of the input widget allow the setting the “checkable” attribute to True to make that label into a checkbox. If not checked, the input widget is disabled the item returns None for its value. The checkbox is checked by default but can be changed with the “checked” attribute.

Each entry may have an optional tooltip attribute, which is a message that appears when the user hovers the mouse over the field. Labels are listed at the left of the data field.You can make a input a optional field by setting its required field to False. Most input types also allow specifying a default value. The “type” attribute defines which type of data entry field is to be constructed.

The available types and there specific behavior and attributes are as follows:

integer: prompts user for an integer. Does error checking to ensure that a valid integer has been entered. Required attribute: “label”: the input prompt. Aliases: int.
Optional attributes:
- “default”:<integer> specifies a default value shown in the dialog window.
- “min”:<integer> specifies that integer must be greater than or equal to the specified minimum value.
- “max”:<integer> specifies that integer must be less than or equal to the specified maximum value.
- “checkable”<boolean> allows the input to be required if it is checked, otherwise not required.
  - “checked”<boolean> default status if “checkable”:True.
double: prompts user for a double precision (64-bit) number. Does error checking to see that a valid double has been entered. Required attribute: “label” the input prompt, Aliases: float, real
Optional attributes:
- “default”:<double> specifies a default value shown in the dialog window.
- “min”:<double> specifies that user-entered double must be greater than or equal to the specified minimum value.
- “max”:<double> specifies that user-entered double must be less than or equal to the specified maximum value.
- “checkable”<boolean> allows the input to be required if it is checked, otherwise not required.
  - “checked”<boolean> default status if “checkable”:True.
string: prompts user for a string. If a required input, checks that the input string is not blank. Required attribute: “label” the input prompt.
Optional attributes:
- “default”:<string> specifies a default value shown in the dialog window.
- “checkable”<boolean> allows the input to be required if it is checked, otherwise not required.
  - “checked”<boolean> default status if “checkable”:True.
checkbox: provides a checkbox with the text provided by the label as the prompt. The check box state of check correlates to a value of True and unchecked to False. Required attribute: “label” the input prompt, Aliases: check.
Optional attributes:
- “default”:<boolean> the checked state.
combobox: takes an additional non-optional attribute, “list”, which contains a list of strings to form a list of choices for the user to choose from. For example, the dictionary entry “list”:[“item 1”, “item 2”] specifies a combobox with two possible values to choose from, “item 1” and “item 2”. The first item is specified by default. Returns the selected string from the list. Required attribute: “label” the input prompt, Aliases: combo.
Optional attributes:
- “default”:<boolean> the string value of the item to be selected.
- “checkable”<boolean> allows the input to be required if it is checked, otherwise not required.
  - “checked”<boolean> default status if “checkable”:True.
radio: Similar to combobox but presents each option as a radio button. Requires “list”, which contains a list of strings to form a list of choices for the user to choose from. For example, the dictionary entry “list”:[“item 1”, “item 2”] specifies a two radio buttons to choose from, “item 1” and “item 2”. The first item is specified by default. Returns the selected string from the list. Required attribute: “label” the input prompt, Aliases: combo.
Optional attributes:
- “default”:<boolean> the string value of the item to be selected.
- “orientation”:<int> Either ghi.const.OrientationVertical or ghi.const.OrientationHorizontal to specify the layout of the buttons (vertical as the default).
- “checkable”<boolean> allows the input to be required if it is checked, otherwise not required.
  - “checked”<boolean> default status if “checkable”:True.
dir: Prompts the user for a directory. A Browse button will pop up the operating systems directory chooser. Required attribute: “label” the input prompt, Aliases: directory.
Optional attributes:
- “default”:<string> the full path to an existing file.
fileopen: Prompts the user for a an existing file. A Browse button will pop up the operating systems file open chooser. Required attribute: “label” the input prompt.
Optional attributes:
- “default”:<string> the full path to an existing file.
- “filter”:<string> The filter string for the open file dialog. Multiple filters can be separated by a double semicolon. For example “Python File (*.py);;All Files (*)” would provide two filters in the dialog, defaulting on only showing files ending in .py.
filesave: Prompts the user for a new file. A Browse button will pop up the operating systems file save as chooser. Required attribute: “label” the input prompt.
Optional attributes:
- “default”:<string> the full path to a file.
- “filter”:<string> The filter string for the open file dialog. Mutliple filters can be separated by a double semicolon. For example “Python File (*.py);;All Files (*)” would provide two filters in the dialog, defaulting on only showing files ending in .py.
files: Prompts the user for multiple existing files. A Browse button will pop up the operating systems file open chooser. Returns the list of selected files. Required attribute: “label” the input prompt.
Optional attributes:
- “filter”:<string> The filter string for the open file dialog. Mutliple filters can be separated by a double semicolon. For example “Python File (*.py);;All Files (*)” would provide two filters in the dialog, defaulting on only showing files ending in .py.
markermap Prompts the user for a marker map from the marker maps folder. Required attribute: “label” the input prompt.
spreadsheet Prompts the user for a spreadsheet from the open project (requires a project be open). Returns the selected spreadsheet’s ID. Required attribute: “label” the input prompt, Alias: ss.
Optional attributes:
- “default”:<int> The ID of a spreadsheet.
- “mapped”:<boolean> If True, only allow for mapped spreadsheets to be selected.
spreadsheets Prompts the user for multiple spreadsheets from the open project (requires a project be open). Returns the list of selected spreadsheets’ IDs. Required attribute: “label” the input prompt.
Optional attributes:
- “mapped”:<boolean> If True, only allow for mapped spreadsheets to be selected.
column: Prompts the user for a column from a specified spreadsheet. A select button will pop up a column chooser dialog. Returns the selected column’s index. Required attributes: “spreadsheet”:<int—string> ID of the spreadsheet or the name of the previous item of type “spreadsheet” to select the spreadsheet from which columns are chosen. “label” the input prompt, Alias: col.
Optional attributes:
- “default”:<int> a valid column index.
- “types”:<int list> Changes the column type filter in the column chooser dialog to only allowing selection of column from the given types list. Items should be one of ghi.const.Type*. For example “types”:[ghi.const.TypeReal, ghi.const.TypeInteger] would only allow the selection of real and integer columns.
- “filter”<int> A filter on the columns used from the chosen spreadsheet. This uses the same filter logically ORed options as spreadsheet.dataModel(). For example, to only allow Active data use “filter”:ghi.const.FilterActive
- “allowLabel”<boolean> indicates whether or not the row labels are an appropriate input and thus can be selected by the user.
columns: Prompts the user for a list of columns from a specified spreadsheet. A select button will pop up a column chooser dialog. Returns the selected columns’ indexes. Required attributes: “spreadsheet”:<int—string> ID of the spreadsheet or the name of the previous item of type “spreadsheet” to select the spreadsheet from which columns are chosen. “label” the input prompt, Alias: cols.
Optional attributes:
- “types”:<int list> Changes the column type filter in the column chooser dialog to only allowing selection of column from the given types list. Items should be one of ghi.const.Type*. For example “types”:[ghi.const.TypeReal, ghi.const.TypeInteger] would only allow the selection of real and integer columns.
- “filter”<int> A filter on the columns used from the chosen spreadsheet. This uses the same filter logically ORed options as spreadsheet.dataModel(). For example, to only allow Active data use “filter”:ghi.const.FilterActive.
- “allowLabel”<boolean> indicates whether or not the row labels are an appropriate input and thus can be selected by the user.
markermapfield: Prompts the user for a marker map field from the marker map from a provided spreadsheet. A select button will pop up a field chooser dialog. Returns the selected field’s index. Required attributes: “spreadsheet”:<int—string> ID of the spreadsheet or the name of the previous item of type “spreadsheet” to select the spreadsheet from which columns are chosen. “label” the input prompt, Alias: mapfield.
Optional attributes:
- “default”:<int> a valid map field index.
- “types”:<int list> Changes the field type filter in the field chooser dialog to only allowing selection of map fields from the given types list. Items should be one of ghi.const.Type*. For example “types”:[ghi.const.TypeInteger] would only allow the selection of integer fields.
- “allowLabel”<boolean> indicates whether or not the row labels are an appropriate input and thus can be selected by the user.
coordsys: Prompts the user to select a coordinate system (species and build combination) from the system provided list built from available genome maps. The default coordinate system will be the global or project default. Returns the coordinate system string for use when building Genome Browser tracks.
- “default”:<string> coordinate system string.
track: Prompts the user for a Genome Browser track from the project, user data folder or system folder. Returns the selected track’s URL. Required attribute: “label” the input string, Alias: annotation.
Optional attributes:
- “default”:<string> a track URL.
- “source”:<one of “local”,”network” or “all”> Selects the source of the Genome Browser track. Local indicates tracks that have been entirely copied to a local IDF file. Network indicates tracks that are streamed on demand from data.goldenhelix.com. All provides both source options in the selection dialog. Defaults to “local”.
- “trackType”:<string> Filters the track selector lists to only contain tracks of the given type. Example types are “Interval”, “Cytoband”, “Gene”, “Probe”, “Allele Sequence”, and “Intensity”. Defaults to no track type filter.
idf: Prompts the user to select a new or existing IDF file. Defaulting to the user app data folder, tracks can be appended to existing IDF files or placed on new IDF files. This prompt may be desirable for scripts that need to write track information to a IDF file. Required attribute: “label” the input string.
Optional attributes:
- “default”:<string> full path to an IDF file.
prompt: displays a label only. This type requires no user input and produces no output. It does not require a name attribute.

There are some types that do not provide input and do not contribute to the returned output list, but allow for the dialogs to have a richer layout and organization. The name, default and required attributes are not used for these types, although label usually is.

These types are containers for other types and have a few attributes in common in common.

First, they require an items attribute, which is a list of dictionary items like the one provided to promptDialog. These are the items that will be inside the container. Containers can contain container items so multiple levels of items can be constructed for a maximum flexibility in displaying items to the user.

Second, containers have an optional layout option that is one either “vertical”, “horizontal” or “columns” where the default is “vertical”. These are described below.

vertical: All items are laid out vertically, one on top of the other.
horizontal: All items are laid out horizontally, one after the other.
columns: Items are laid out in vertical columns. This requires that the items attribute is a list of list of items instead of just a list of items. The other list defines the columns. For example, “items”:[ [item1, item2], [item3, item4, item5], [item6] ] would produce a three column layout with item 1 and 2 in the firs, 3, 4 and 5 in the second and 6 in the third.

Below a list of container types are described:

group: places a number of following items into a group box with label as the label. Similar to items with an explicit label widget, this container takes the “checkable” and “checked” attributes. When unchecked, all items in the group are disabled and return None for their values.
tab: places a number of following items into a tab with label as the label. Multiple tabs will share a tab widget if an additional tab item follows a tab item the number of items it specified.
widget: A blank container widget. Does not require a label. Useful for simply specifying a different layout for a couple items.

If the user cancels, an empty list is returned. It is wise to first check if the list is empty before trying to access its elements. If there is an error in syntax, a Python exception is returned with a description of the error. If the user hits OK, and there is any error in the input, the user is told by the dialog what the problem is, which may be remedied from within the dialog. If there are no errors, a dictionary of the user inputs is returned.

Commands for Downloading Golden Helix, Inc. Hosted Data Files

The Python interface can be used to query data files that are available from download on the Golden Helix, Inc. Data server.

Fetch a List of Categories or File Names

Syntax

python list = ghi.dataFetchListing(category)

Example

categoryList = ghi.dataFetchListing()

Example

fileList = ghi.dataFetchListing(‘MarkerMaps’)

This command fetches listings from our data repository of optional resources. The category parameter is optional.

When category is not set, the list of valid available categories is returned. If a category is provided, a list is returned where each entry is a list of information for a available file in that category.

filePath = ghi.dataAutoDownload(‘RefData’, ‘AffySNP6_YRI_SNP_ABRefs.dsf’,1,‘e:/Tmp’)

This command looks up a file on our data repository server (see ghi.dataFetchUrl) and then downloads that to the application data folder if it is not already downloaded. In either case the full path of the local copy of the file is returned.

The parameters are as follows:

Category: The repository category
File Name: The repository file name
Prompt Override: (optional) Prompts if the file exists to override it rather than not downloading files that exist.
Manual Path: (optional) Specify a manual destination path for downloads.

Commands for Importing Data as Genome Browser Annotation Files

The Python interface can be used to create new annotation tracks from text files and/or spreadsheets. The resulting tracks can be visualized in any Genomic mode Plot Viewer. Care should be taken to match the species and build written to each annotation track with the source of its data.

Creating an Annotation Track from 2Bit Data

Syntax

ghi.genomebrowser.importTrackFrom2Bit(input file, output file, title, [Optional Parameters])

Example

ghi.genomebrowser.importTrackFrom2Bit(’mySequence.2bit’, ’MySequence.idf’, ’My Sequence Track’, coordSysId = ’GRCh_37,Chromosome,Homo sapiens’)

This command creates an Allele Sequence type annotation track from the input sequence provided in the binary 2bit format.

This command takes three required parameters:

input file: the name of the 2bit input file
output file: the name of the IDF file to write to. If the specified file exists, the new annotation track will be added to the file.
title: the name of the new annotation track.

This command also supports some optional parameters:

coordSysId: sets the coordinate system association used by the output annotation track. The coordinate system id links an annotation track to a species and build. Although any string can be used, for best results, the proper format should be applied. The proper format is ’authority,type,species’ where:
- authority: is the entity responsible for the build often including a build version identifier
- type: is the coordinate system type, usually ’Chromosome’
- species: is the species name Many coordinate system ids of this format can be found here:
  http://www.dasregistry.org/das1/coordinatesystem The coordinate system id defaults to the current project or global default, usually ’NCBI_36,Chromosome,Homo sapiens’
sequenceBlockSize: specifies the maximum size of the sequence blocks written to the output annotation track. Except in extremely rare circumstances this parameter should never be adjusted.
- 16384 (default) 2¹⁴ base pairs. Sequence data will be broken into blocks of this size to balance performance and file size

Creating an Annotation Track from Fasta Data

Syntax

ghi.genomebrowser.importTrackFromFasta(input file, output file, title, [Optional Parameters])

Example

ghi.genomebrowser.importTrackFromFasta(’mySequence.fa’, ’MySequence.idf’, ’My Sequence Track’, coordSysId = ’GRCh_37,Chromosome,Homo sapiens’)

This command creates an Allele Sequence type annotation track from the input sequence provided in the fasta text format.

This command takes three required parameters:

input file: the name of the fasta input file
output file: the name of the IDF file to write to. If the specified file exists, the new annotation track will be added to the file.
title: the name of the new annotation track.

This command also supports some optional parameters:

coordSysId: sets the coordinate system association used by the output annotation track. The coordinate system id links an annotation track to a species and build. Although any string can be used, for best results, the proper format should be applied. The proper format is ’authority,type,species’ where:
- authority: is the entity responsible for the build often including a build version identifier
- type: is the coordinate system type, usually ’Chromosome’
- species: is the species name Many coordinate system ids of this format can be found here:
  http://www.dasregistry.org/das1/coordinatesystem The coordinate system id defaults to the current project or global default, usually ’NCBI_36,Chromosome,Homo sapiens’
sequenceBlockSize: specifies the maximum size of the sequence blocks written to the output annotation track. Except in extremely rare circumstances this parameter should never be adjusted.
- 16384 (default) 2¹⁴ base pairs. Sequence data will be broken into blocks of this size to balance performance and file size

Creating an Annotation Track from Wiggle Data

Syntax

ghi.genomebrowser.importTrackFromWiggle(input file, output file, title, [Optional Parameters])

Example

ghi.genomebrowser.importTrackFromWiggle(’myValues.wig’, ’Values.idf’, ’My Intensity Track’,
coordSysId = ’GRCh_37,Chromosome,Homo sapiens’)

This command creates an Intensity type annotation track from the real values provided in wiggle format.

This command takes three required parameters:

input file: the name of the wiggle input file
output file: the name of the IDF file to write to. If the specified file exists, the new annotation track will be added to the file.
title: the name of the new annotation track.

This command also supports an optional parameter:

coordSysId: sets the coordinate system association used by the output annotation track. The coordinate system id links an annotation track to a species and build. Although any string can be used, for best results, the proper format should be applied. The proper format is ’authority,type,species’ where:
- authority: is the entity responsible for the build often including a build version identifier
- type: is the coordinate system type, usually ’Chromosome’
- species: is the species name Many coordinate system ids of this format can be found here:
  http://www.dasregistry.org/das1/coordinatesystem The coordinate system id defaults to the current project or global default, usually ’NCBI_36,Chromosome,Homo sapiens’

Creating an Annotation Track from Delimited Text Data

Syntax

ghi.genomebrowser.importTrackFromDelimitedText(input file, output file, title, [Optional Parameters])

Example

ghi.genomebrowser.importTrackFromDelimitedText(’myData.csv’, ’MyData.idf’, ’My Track’,
coordSysId = ’GRCh_37,Chromosome,Homo sapiens’, schemaType = ’Interval’, halfOpen = False, headerLine = 0, delimiter = ’,’, subDelimiter = ’;’)

This command creates an annotation track from the input file provided in any column delimited text format. The input file should include data columns specifying: Chromosome, Start, and Stop or Chromosome and Position.

This command takes three required parameters:

input file: the name of the delimited text input file
output file: the name of the IDF file to write to. If the specified file exists, the new annotation track will be added to the file.
title: the name of the new annotation track.

This command also supports some optional parameters:

coordSysId: sets the coordinate system association used by the output annotation track. The coordinate system id links an annotation track to a species and build. Although any string can be used, for best results, the proper format should be applied. The proper format is ’authority,type,species’ where:
- authority: is the entity responsible for the build often including a build version identifier
- type: is the coordinate system type, usually ’Chromosome’
- species: is the species name Many coordinate system ids of this format can be found here:
  http://www.dasregistry.org/das1/coordinatesystem The coordinate system id defaults to the current project or global default, usually ’NCBI_36,Chromosome,Homo sapiens’
schemaType: the schema type defines the way the output annotation track can be used. It affects the way data is imported and is used during auto-configuration when inputs are set. Possible values are:
- ’Allele Sequence’ a DNA sequence track
- ’Cytoband’ a cytoband track with styling based on stain
- ’Gene’ a gene track including exons and codon alignments
- ’Intensity’ a track composed of real valued intervals
- ’Interval’ (default) a versatile general purpose track
- ’Probe’ a SNP probe marker track
maxScanFields: specifies the maximum number of input columns to make accessible for field mappings. A limit can improve performance in scanning and importing from inputs with very high column counts.
- 0 (default) no limit
- N limit available number of columns to N
maxAutoFields: specifies the maximum number of automatic field mappings to create based on input columns A limit can improve performance in scanning and importing from inputs with very high column counts.
- 0 (default) no limit
- N limit automatic field mappings to N
titleCaseFieldNames: specifies whether the names of automatic field mappings are converted to title case.
- 0 set field names to column names
- 1 (default) capitalize the first letter of each word
guessFieldTypes: specifies whether the data types of input fields are guessed during auto-configuration while setting inputs.
- 0 assume all input columns are ’String’ type
- 1 (default) guess the type of each input column
reuseFieldsForConstraints: specifies whether existing field mappings are adjusted to meet field constraints or that creation of new field mappings is always enforced.
- 0 create a new field mapping for every constraint
- 1 (default) attempt to use existing mappings to satisfy constraints
ignoreConstraints: specifies whether schema type derived field mapping constraints are respected. If constraints are ignored, the output annotation track is much more likely to be unusable.
- 0 (default) respect constraints and throw errors when attempting an import which is improperly configured
- 1 ignore constraints, silently complying with any field mappings that have been overlooked
halfOpen: specifies whether input interval data is provided using half-open coordinates or not. Half-open coordinates, sometimes referred to as zero-based, precisely define the outside edges of an interval. The smallest possible value is zero and the largest is equal to the size of the containing segment. A zero width interval may be specified using half-open coordinates. The alternative, indexed coordinates, are often referred to as one-based. Indexed coordinates specify base pair positions within a segment starting from one. The largest possible value is equal to the size of the containing segment. The smallest interval which can be specified using indexed coordinates is of width one.
- 0 input data is provided as indexed intervals or one-based positions
- 1 (default) input data is provided as half-open intervals or zero-based positions
headerLine: specifies the one-based line number in the input file which defines its input column names. All preceding lines will be ignored. This does not apply to spreadsheets as their column names are provided directly.
- -1 (default) specifies no header, or that the header is located using a different method
- N the file header is on line N
header: specifies a pattern used to match the line in the input file which defines its input column names. The first line matched by the pattern will be designated as the header. All text matched by the pattern will be removed from the header. Any remaining text is used to define the input column names.
- ” does not match. Specifies no header, or that the header is located using a different method
- ’ˆ#’ (default) a Perl compatible regular expression that matches lines that begin with ’#’
comment: specifies a pattern used to match comment lines in the input file. Comment lines are not data and are ignored.
- ” (default) does not match. The input file contains no comment lines
delimiter: specifies a pattern used to separate column values on each line.
- ’∖t’ (default) the tab character delimits field values
subDelimiter: specifies a pattern used to separate list values within a field value.
- ’,’ (default) the comma character delimits list values

Creating an Annotation Track from Spreadsheet Data

Syntax

ghi.genomebrowser.importTrackFromSpreadsheet(node id, output file, title, [Optional Parameters])

Example

ghi.genomebrowser.importTrackFromSpreadsheet(4, ’MySS.idf’, ’My Spreadsheet Track’,
coordSysId = ’GRCh_37,Chromosome,Homo sapiens’, schemaType = ’Interval’, halfOpen = False, headerLine = 0, subDelimiter = ”)

This command creates an annotation track from the input spreadsheet. The spreadsheet should include a row marker map with position data or columns specifying: Chromosome, Start, and Stop or Chromosome and Position.

This command takes three required parameters:

node id: the project node id of the input spreadsheet.
output file: the name of the IDF file to write to. If the specified file exists, the new annotation track will be added to the file.
title: the name of the new annotation track.

This command also supports some optional parameters:

coordSysId: sets the coordinate system association used by the output annotation track. The coordinate system id links an annotation track to a species and build. Although any string can be used, for best results, the proper format should be applied. The proper format is ’authority,type,species’ where:
- authority: is the entity responsible for the build often including a build version identifier
- type: is the coordinate system type, usually ’Chromosome’
- species: is the species name Many coordinate system ids of this format can be found here:
  http://www.dasregistry.org/das1/coordinatesystem The coordinate system id defaults to the current project or global default, usually ’NCBI_36,Chromosome,Homo sapiens’
schemaType: the schema type defines the way the output annotation track can be used. It affects the way data is imported and is used during auto-configuration when inputs are set. Possible values are:
- ’Allele Sequence’ a DNA sequence track
- ’Cytoband’ a cytoband track with styling based on stain
- ’Gene’ a gene track including exons and codon alignments
- ’Intensity’ a track composed of real valued intervals
- ’Interval’ (default) a versatile general purpose track
- ’Probe’ a SNP probe marker track
maxScanFields: specifies the maximum number of input columns to make accessible for field mappings. A limit can improve performance in scanning and importing from inputs with very high column counts.
- 0 (default) no limit
- N limit available number of columns to N
maxAutoFields: specifies the maximum number of automatic field mappings to create based on input columns A limit can improve performance in scanning and importing from inputs with very high column counts.
- 0 (default) no limit
- N limit automatic field mappings to N
titleCaseFieldNames: specifies whether the names of automatic field mappings are converted to title case.
- 0 set field names to column names
- 1 (default) capitalize the first letter of each word
reuseFieldsForConstraints: specifies whether existing field mappings are adjusted to meet field constraints or that creation of new field mappings is always enforced.
- 0 create a new field mapping for every constraint
- 1 (default) attempt to use existing mappings to satisfy constraints
ignoreConstraints: specifies whether schema type derived field mapping constraints are respected. If constraints are ignored, the output annotation track is much more likely to be unusable.
- 0 (default) respect constraints and throw errors when attempting an import which is improperly configured
- 1 ignore constraints, silently complying with any field mappings that have been overlooked
halfOpen: specifies whether input interval data is provided using half-open coordinates or not. Half-open coordinates, sometimes referred to as zero-based, precisely define the outside edges of an interval. The smallest possible value is zero and the largest is equal to the size of the containing segment. A zero width interval may be specified using half-open coordinates. The alternative, indexed coordinates, are often referred to as one-based. Indexed coordinates specify base pair positions within a segment starting from one. The largest possible value is equal to the size of the containing segment. The smallest interval which can be specified using indexed coordinates is of width one.
- 0 input data is provided as indexed intervals or one-based positions
- 1 (default) input data is provided as half-open intervals or zero-based positions
subDelimiter: specifies a pattern used to separate list values within a field value.
- ’,’ (default) the comma character delimits list values
skipInactiveRows: specifies whether inactive rows are included in the import operation.
- 0 all rows are imported
- 1 (default) inactive rows are not imported

Syntax

importer object.setInputFile(file name, [auto-configure])

Example

myTextImporter.setInputFile(’c:/data/myData.csv’)

This command sets the input file for the importer by file name.

The parameters for this command are as follows:

file name: the file name of the input file to import
auto-configure: (optional) specifies whether field mappings and styles are automatically assigned based on detected file column names and types
- 0 require manual setup of field mappings and styles
- 1 (default) attempt to automatically map expected fields and create default styles based on schema type. Some expected fields may remain unmapped if no likely candidates are discovered. These will have to be filled in manually

Setting Multiple Files as Inputs to an Annotation Track Importer Object

Syntax

importer object.setInputFiles(file name list, [auto-configure])

Example

myTextImporter.setInputFiles([’c:/data/myData1.csv’, ’c:/data/myData2.csv’])

This command sets a list of input files for the importer by file name. Import will work best if all input files share the same format and data columns.

The parameters for this command are as follows:

fileList: list of input file names to import
autoConfigure: (optional) specifies whether field mappings and styles are automatically assigned based on detected file column names and types
- 0 require manual setup of field mappings and styles
- 1 (default) attempt to generate a list of field mappings based on the input files and schema type. Some constraint fields may remain unmapped if no likely candidates are discovered. These will have to be filled in manually

The parameters for this command are as follows:

schemaType: (optional) specifies the schema type for which the default constraint list will be loaded
- ’Allele Sequence’
- ’Cytoband’
- ’Gene’
- ’Intensity’
- ’Interval’
- ’Probe’
When this parameter is not specified, the current schema type determines which default styles are loaded.

Mapping an Annotation Track Importer Object’s Fields to Constraints

Syntax

importer object.applyConstraints()

Example

myTextImporter.applyConstraints()

This command can be called to attempt a mapping of all the field constraints in the importer’s constraint list to fields in its field list. Some field constraints may remain unmapped if no likely candidates are discovered. Each will result in a new field being defined without a mapping. These mappings will have to be filled in manually before the import operation can succeed.

Defining the Field Constraint List of an Annotation Track Importer Object

Syntax

importer object.setConstraintList(constraint list)

Example

myTextImporter.setConstraintList([{’match’:[’data’], ’name’:’Value’, ’type’:’Real’}])

This command allows the importer’s field constraint list to be set manually. It may be useful to obtain the current field constraint list using the constraintList() command, make a few modifications, and then re-supply it to the importer using this command.

Field constraints are only utilized by some import formats.

The parameters for this command are as follows:

constraintList: a list of dict items. Each item defines a field constraint. The order of the items in the list determines the order in which the constraints are resolved in application or testing for satisfaction. Each item may contain the following keys:
- match: list - specifies a list of patterns to attempt to match against source columns while applying constraints. Each item in the list is a string which represents a Perl compatible regular expression. The order of the patterns in the list defines the order in which a match to a source column is attempted.
- defaultName: string - specifies the default name of the constrained field. This will be the name of the constraint generated field if no matching input column is found. Otherwise the field name will be unchanged. When defaultName is specified, name should not be.
- name: string - specifies the name of the constrained field in the output. Any matching input column’s field name found will be changed to match this value. When name is specified, defaultName should not be.
- type: string - specifies the schema field type that the constrained field should match. If the type of the matched input column can be coerced into this type, it will still match, and be assigned this field type.
  - ’Boolean’ tristate including True, False, and Unknown
  - ’Byte’ any single character
  - ’Integer’ an integer value
  - ’Real’ a floating point value
  - ’Double Real’ a double precision floating point value
  - ’String’ (default) a string
  - ’Boolean List’ a list of ’Boolean’ values
  - ’Byte List’ a list of ’Byte’ values
  - ’Integer List’ a list of ’Integer’ values
  - ’Real List’ a list of ’Real’ values
  - ’Double Real List’ a list of ’Double Real’ values
  - ’String List’ a list of ’String’ values
- compress: bool - specifies whether the constrained field’s data should be compressed Possible values are:
  - 0 (default) do not compress output data
  - 1 compress output data for this field using gzip compression. Not all field types support compression. compression is inefficient unless the input data is a very long string or list
- transformList: list - specifies a list of find and replace operations to perform on the constrained field’s values before writing them to the output. Each item in the list is a dict which defines a transform operation. The order of the items in the list defines the order in which the transforms are applied to the field data. Each item may contain the following keys:
  - find: string - specifies a pattern to find in the input data.
  - replace: string - specifies the string used to replace the text matching the find string if it is found.
- required: bool - specifies whether this constraint must be met in order for import to succeed Possible values are:
  - 0 (default) this constraint need not necessarily be met
  - 1 this constraint must be met
- inSchema: bool - specifies whether the constrained field will be included in the output schema. Possible values are:
  - 0 do not include this field in the output schema. Defining a field mapping which is not part of the output schema may be useful if its value will be used during the import operation, but will not be desired in the resulting annotation track. The ’Chromosome’, ’Start’ and ’Stop’ fields are common examples, since the interval data they provide is required to define features, but would be redundant to include in the schema
  - 1 (default) include this field in the output schema
- fixedIndex: int - specifies the one-based schema index the constrained field must occupy in order to satisfy this constraint. Possible values are:
  - 0 (default) any index will work
  - N the specified index is the only valid index
- reorder: bool - specifies whether the constrained field is pulled toward the start of the schema when a match is found. Possible values are:
  - 0 a field matching this constraint is not relocated and will remain at its original location in the field list. Note that its absolute index may still be adjusted due to other constraints reordering fields
  - 1 (default) a field matching this constraint is moved to the next available position at the beginning of the field list

The parameters for this command are as follows:

transform: the find/replace operation to apply to the field names. This dict may contain the following keys:
- find: string - specifies a perl compatible regular expression to match against each field name
- replace: string - specifies the string used to replace the text matching the find pattern if it is found.

Defining the Field List of an Annotation Track Importer Object

Syntax

importer object.setFieldList(field list)

Example

myTextImporter.setFieldList([{’name’:’Log R’, ’mapping’:’%logr%’, ’type’:’Real’}])

This command allows the importer’s field list to be set manually. It may be useful to obtain the current field list using the fieldList() command, make a few modifications, and then re-supply it to the importer using this command.

Field mappings are only utilized by some import formats.

The parameters for this command are as follows:

fieldList: a list of dict items. Each item defines a field mapping. The order of the items in the list determines the order of the data written to the output of the import operation. Each item may contain the following keys:
- name: string - specifies the name of the field in the output a field name is required.
- mapping: string - specifies a template for the field data as written to the output. The template is a string literal which includes variables for replacement. The variables must be of the form ’%col{idx}%’ where ’col’ is the name of an input column and ’idx’ may optionally be used to disambiguate input columns with the same name. ’idx’ is a zero-based integer indicating the position of the column within any input spreadsheet or file. For spreadsheets with included row marker map data, the first column of the marker map is designated as having index 0. The row label index is equal to the number of columns in the row marker map and the index of the first data column is equal to the number of columns in the row marker map plus one. For example, a typical mapping might be ’%chr%’. In this case, the string ’%chr%’ will be replaced with the data in the input column named ’chr’ for each row imported. A more complicated example would be ’%chr%:%pos%’. In this case the output data for rows in chromosome 1 might include ’1:10000’, ’1:10005’ and so on.
- type: string - specifies the type to convert the input data into. Possible values are:
  - ’Boolean’ tristate including True, False, and Unknown
  - ’Byte’ any single character
  - ’Integer’ an integer value
  - ’Real’ a floating point value
  - ’Double Real’ a double precision floating point value
  - ’String’ (default) a string
  - ’Boolean List’ a list of ’Boolean’ values
  - ’Byte List’ a list of ’Byte’ values
  - ’Integer List’ a list of ’Integer’ values
  - ’Real List’ a list of ’Real’ values
  - ’Double Real List’ a list of ’Double Real’ values
  - ’String List’ a list of ’String’ values
- compress: bool - specifies whether to compress the data Possible values are:
  - 0 (default) do not compress output data
  - 1 compress output data for this field using gzip compression. Not all field types support compression. compression is inefficient unless the input data is a very long string or list
- inSchema: bool - specifies whether this field is included in the output schema. Possible values are:
  - 0 do not include this field in the output schema. Defining a field mapping which is not part of the output schema may be useful if its value will be used during the import operation, but will not be desired in the resulting annotation track. The ’Chromosome’, ’Start’ and ’Stop’ fields are common examples, since the interval data they provide is required to define features, but would be redundant to include in the schema
  - 1 (default) include this field in the output schema
- transformList: list - specifies a list of find and replace operations to perform on the field values before writing them to the output. Each item in the list is a dict which defines a transform operation. The order of the items in the list defines the order in which the transforms are applied to the field data. Each item may contain the following keys:
  - find: string - specifies a pattern to find in the input data.
  - replace: string - specifies the string used to replace the text matching the find string if it is found.

Resetting the Style Map State of an Annotation Track Importer Object

Syntax

importer object.resetStyleMapState()

Example

Example

myTextImporter.setStyleField(4)

myTextImporter.setStyleField(’Category’)

Sets the field used to map features to styles for this importer.

The parameters for this command are as follows:

field index: specifies the index of a field in the importer field list whose data is used to map imported features to styles
field name: specifies the name of a field in the importer field list whose data is used to map imported features to styles

Obtaining the Style Rule List from an Annotation Track Importer Object

Syntax

style rule list = importer object.styleRuleList()

Example

Syntax

importer object.setStyleWheel(style list)

Example

myImporter.setStyleWheel([{’name’:’Style A’, ’bgcolor’:’#FF0000’}, {’name’:’Style B’, ’bgcolor’:’#0000FF’}])

This command allows the importer’s style wheel to be set. It may be useful to obtain the current style wheel using the styleWheel() command, make a few modifications, and then re-supply it to the importer using this command.

The style wheel is used by the auto categorization feature. If the style wheel is empty, a builtin set of GHI plot colors is used as the style wheel.

The parameters for this command are as follows:

style list: a list of dict items. Each item defines a style. The order of the items in the list determines the order in which they are assigned to new categories. See setStyleList(...) for information on the format of each item in the list.

The parameters for this command are as follows:

file: the output file name

Obtaining the Output Track Name from an Annotation Track Importer Object

Syntax

title = importer object.trackTitle()

Example

title = myImporter.trackTitle()

This command returns the specified title which will be written to the output annotation track.

Defining the Output Track Name for an Annotation Track Importer Object

Syntax

importer object.setTrackTitle(title)

Example

myImporter.setTrackTitle(’My Track’)

This command sets the title of the output annotation track.

The parameters for this command are as follows:

title: the name of the output track

Obtaining the Coordinate System Identifier from an Annotation Track Importer Object

Syntax

coordinate system id = importer object.coordSysId()

Example

coords = myImporter.coordSysId()

This command returns the coordinate system id which will be associated to the output annotation track. See setCoordSysId(...) for information about the coordinate system id format.

Setting the Coordinate System Identifier for an Annotation Track Importer Object

Syntax

importer object.setCoordSysId(coordinate system id)

Example

myImporter.setCoordSysId(’GRCh_37,Chromosome,Homo sapiens’)

Sets the coordinate system association used by the output annotation track. The coordinate system id is used by SVS to associate each annotation track with its appropriate species and build. Although any string can be used, for best results, the proper format should be applied. The proper format is ’authority,type,species’ where:

authority: is the entity responsible for the specified build often including a build version identifier, such as: ’NCBI_36’ in the case of hg18.
type: is the coordinate system type, usually ’Chromosome’
species: is the species name, such as: ’Homo sapiens’ in the case of hg18 or other human builds.

Many coordinate system ids of this format can be found in this xml list:
http://www.dasregistry.org/das1/coordinatesystem

The parameters for this command are as follows:

coordinate system id: the coordinate system id to associate with the output track. If unset, the importer will use the current project or global default, usually ’NCBI_36,Chromosome,Homo sapiens’

Example

myImporter.write()

myImporter.write(’mytrack.idf’, ’My Track’, ’GRCh_37,Chromosome,Homo sapiens’)

Initiates the import process, writing all imported data to the specified output annotation track. This command should be called after all desired importer configuration has been completed.

The parameters for this command are as follows:

output file: sets the output file name
title: sets the output annotation track title
coordinate system id: (optional) sets the coordinate system id associated with the output annotation track. See setCoordSysId(...) for information about the coordinate system id format

0 the import was not canceled by the user
1 the import was canceled by the user

Commands for Accessing Genome Browser Annotation Files

The Python interface can be used to query annotation tracks from either local files or network files to retrieve information such as genes or sequences. The following commands are used to obtain a genome browser object and query the files.

Get a List of Local Annotation Sources

Syntax

python list of lists = ghi.genomebrowser.localSourceList()

Example

myList = ghi.genomebrowser.localSourceList()

This command returns a list of the tracks found in the project annotation directory. Each item of the list is itself a list corresponding to one track. Information included in each list for a track is:

track name
a short description including a short track name and source
coordinate system including build, data type and species
track type such as ‘cyto’, ‘gene’, etc.

Get a List of Annotation Sources from a URL

Syntax

python list of lists = ghi.genomebrowser.sourceListForUrl(name of IDF file or DAS server URL)

Example

myList = ghi.genomebrowser.sourceListForUrl(‘cytobands_ucsc(NCBI_36).idf’)

Example

myList = ghi.genomebrowser.sourceListForUrl(‘http://data.goldenhelix.com/das’)

This command returns a list of annotation tracks or URLs from the specified source. Each item in the returned list of lists corresponds to one track. Information included for each track is:

track name
a short description including a short track name and source
coordinate system including build, data type and species
track type such as ‘generic’, ‘probe’, etc.

Get Item Keys for the Specified Source

Syntax

python list = ghi.genomebrowser.itemKeysForSource(annotation source)

Example

myList = ghi.genomebrowser.itemKeysForSource(‘cytobands_ucsc(NCBI_36).idf:1’)

Example

myList = ghi.genomebrowser.sourceListForUrl(‘http://data.goldenhelix.com/das/Cytobands-UCSC_GRCh_37_Homo_sapiens’)

This command returns a list of data field names (keys) available from the annotation source.

The one parameter for this command is:

url: the name of an IDF file in the project annotations directory such as ‘cytobands_ucsc(NCBI_36).idf:1’ or a DAS source URL, such as ‘http://data.goldenhelix.com/das/Cytobands-UCSC_GRCh_37_Homo_sapiens’.

Get Information for an Item in a Specified Range

Syntax

python list = ghi.genomebrowser.featureItemInRange(url, item key, chromosome,start position,stop position, show progress)

Example

myList = ghi.genomebrowser.featureItemInRange(‘refSeq_genes_ucsc(NCBI_36).idf:1’, ‘Name’,‘1’, 64402613, 64402614, 1)

This command returns a list of values for the specified field and genomic range (chromosome, start position and end position).

This command requires the following parameters in this order:

URL: The name of an IDF file in the project annotations directory such as ‘cytobands_ucsc(NCBI_36).idf:1’ or a DAS source URL, such as ‘http://data.goldenhelix.com/das/Cytobands-UCSC_GRCh_37_Homo_sapiens’.
Item key: A data field name available from the selected track.
Chromosome: Chromosome number for genomic region for the query.
Start position: Start position number for genomic region
End position: End position number for genomic region

The last parameter is optional:

Show progress: (Optional) Controls the presence of the status bar.
- 0: Do not show a status bar
- 1 (default): Show a status bar

NOTE:

Positions are specified in half-open zero based coordinates, i.e. for position 1, specify start = 0 and end = 1.

Get Information for an Item in a List of Ranges

Syntax

python list = ghi.genomebrowser.featureItemInRanges(url, item key, ranges, show progress)

Example

myList = ghi.genomebrowser.featureItemInRange(‘refSeq_genes_ucsc(NCBI_36).idf:1’, ‘Name’, myRangesList, 1)

This command returns a list of values for the specified field and list of genomic range (chromosome, start position and end position).

This command requires the following parameters in this order:

URL: The name of an IDF file in the project annotations directory such as ‘cytobands_ucsc(NCBI_36).idf:1’ or a DAS source URL, such as ‘http://data.goldenhelix.com/das/Cytobands-UCSC_GRCh_37_Homo_sapiens’.
Item key: A data field name available from the selected track.
Ranges: A list of 3-tuples: [Chr, Start, Stop] in that order representing a list of genomic ranges for the query. The ranges list should not contain overlapping intervals.

The last parameter is optional:

Show progress: (Optional) Controls the presence of the status bar.
- 0: Do not show a status bar
- 1 (default): Show a status bar

NOTE:

Positions are specified in half-open zero based coordinates, i.e. for position 1, specify start = 0 and end = 1.

Get Information for a List of Items in a Specified Range

Syntax

python list = ghi.genomebrowser.featureListInRange(url, item keys, chromosome,start position,stop position, show progress)

Example

myList = ghi.genomebrowser.featureListInRange(‘refSeq_genes_ucsc(NCBI_36).idf:1’, [‘Name’,‘ExonCount’],‘1’, 64402613, 64402614, 1)

This command returns a list of values for the specified list of fields and genomic range (chromosome, start position and end position).

This command requires the following parameters in this order:

URL: The name of an IDF file in the project annotations directory such as ‘cytobands_ucsc(NCBI_36).idf:1’ or a DAS source URL, such as ‘http://data.goldenhelix.com/das/Cytobands-UCSC_GRCh_37_Homo_sapiens’.
Item keys: A list of data field names available from the selected track.
Chromosome: Chromosome number for genomic region for the query.
Start position: Start position number for genomic region
End position: End position number for genomic region

The last parameter is optional:

Show progress: (Optional) Controls the presence of the status bar.
- 0: Do not show a status bar
- 1 (default): Show a status bar

NOTE:

Positions are specified in half-open zero based coordinates, i.e. for position 1, specify start = 0 and end = 1.

Get Information for a List of Items in a List of Ranges

Syntax

python list = ghi.genomebrowser.featureListInRange(url, item keys, ranges, show progress)

Example

myList = ghi.genomebrowser.featureListInRange(’refSeq_genes_ucsc(NCBI_36).idf:1’, [‘Name’,‘ExonCount’],myRangesList, 1)

This command returns a list of values for the specified list of fields and list of genomic ranges (chromosome, start position and end position).

This command requires the following parameters in this order:

URL: The name of an IDF file in the project annotations directory such as ‘cytobands_ucsc(NCBI_36).idf:1’ or a DAS source URL, such as ‘http://data.goldenhelix.com/das/Cytobands-UCSC_GRCh_37_Homo_sapiens’.
Item keys: A list of data field names available from the selected track.
Ranges: A list of 3-tuples: [Chr, Start, Stop] in that order representing a list of genomic ranges for the query. The ranges list should not contain overlapping intervals.

The last parameter is optional:

Show progress: (Optional) Controls the presence of the status bar.
- 0: Do not show a status bar
- 1 (default): Show a status bar

NOTE:

Positions are specified in half-open zero based coordinates, i.e. for position 1, specify start = 0 and end = 1.

Get Information for All Items in a Specified Range

Syntax

python list = ghi.genomebrowser.featureDictInRange(url, chromosome,start position,stop position, show progress)

Example

myList = ghi.genomebrowser.featureDictInRange(’cytobands_ucsc(NCBI_36).idf:1’,‘1’, 64402613, 64402614, 1)

This command returns a list of dictionaries where the keys of the dictionaries are the fields for the specified annotation track for the specified genomic range (chromosome, start position and end position).

Example output:
[{‘Stain’:‘gpos50’, ‘Cytoband’:‘p26.1’},{‘Stain’:‘gpos50’,‘Cytoband’:‘p26.1’}]

This command requires the following parameters in this order:

URL: The name of an IDF file in the project annotations directory such as ‘cytobands_ucsc(NCBI_36).idf:1’ or a DAS source URL, such as ‘http://data.goldenhelix.com/das/Cytobands-UCSC_GRCh_37_Homo_sapiens’.
Chromosome: Chromosome number for genomic region for the query.
Start position: Start position number for genomic region
End position: End position number for genomic region

The last parameter is optional:

Show progress: (Optional) Controls the presence of the status bar.
- 0: Do not show a status bar
- 1 (default): Show a status bar

NOTE:

Positions are specified in half-open zero based coordinates, i.e. for position 1, specify start = 0 and end = 1.

Get Information for All Items in a List of Ranges

Syntax

python list = ghi.genomebrowser.featureDictInRange(url, ranges, show progress)

Example

myList = ghi.genomebrowser.featureDictInRange(’cytobands_ucsc(NCBI_36).idf:1’, myRangesList, 1)

This command returns a list of dictionaries where the keys of the dictionaries are the fields for the specified annotation track for the specified list of genomic ranges (chromosome, start position and end position).

Example output:
[{‘Stain’:‘gpos50’, ‘Cytoband’:‘p26.1’},{‘Stain’:‘gpos50’,‘Cytoband’:‘p26.1’}]

This command requires the following parameters in this order:

URL: The name of an IDF file in the project annotations directory such as ‘cytobands_ucsc(NCBI_36).idf:1’ or a DAS source URL, such as ‘http://data.goldenhelix.com/das/Cytobands-UCSC_GRCh_37_Homo_sapiens’.
Ranges: A list of 3-tuples: [Chr, Start, Stop] in that order representing a list of genomic ranges for the query. The ranges list should not contain overlapping intervals.

The last parameter is optional:

Show progress: (Optional) Controls the presence of the status bar.
- 0: Do not show a status bar
- 1 (default): Show a status bar

NOTE:

Positions are specified in half-open zero based coordinates, i.e. for position 1, specify start = 0 and end = 1.

track reader.read(’1’, 10000, 20000, [‘Name’, ‘Value’])

track reader.read([[’1’,10000,20000],[’1’,30000,40000]], [‘Name’, ‘Value’])

This command initializes the track reader to read either the entire track (when calling read() with no parameters) or to read features that overlap with the specified region(s).

The region list parameter is a list of regions, where each region contains at least a chromosome identifier, but can also contain start and stop positions, i.e. [[’1’],[’2’, 300, 400]] will read all of chromosome ’1’, followed by any features in chromosome ’2’ that overlap the interval (300,400).

The optional field list parameter is an ordered list of names of fields specifying the structure of features returned by next(). To read a whole track while specifying the feature structure use readFields() (see Reading an Annotation Track).

Select Fields to Read From an Annotation Track

Syntax

track reader.readFields(field name list)

Example

myReader.readFields([‘Name’,‘Value’])

This command initializes the reader to read the whole track (equivalent to calling read()), and orders the data in the features returned by next() by name.

Examine the Schema Description of an Annotation Track

Syntax

schema string = track reader.schema()

Example

text = myReader.schema()

Output

text == u’Name=s∖tValue=f’

A schema is a tab delimited set of field descriptions which specify the name and data type of the field. A descriptor such as ’Value=f’ specifies a single field, called ’Value’ that is single precision real. The available data types are:

? - Boolean
b - Byte
s - String (null terminated)
i - Integer (4 bytes)
f - Single precision real (4 bytes)
f8 - Double precision real (8 bytes)

An example schema describing two fields, one called ‘Name’, of a string, and the other called ‘Value’, of a single precision real number would be the string: ‘Name=s∖tValue=f’. A field can also be described as a list type, using the ‘@’ symbol prior to the dat type, i.e. ’Values=@f’. Field compression can be enabled by prefixing the type with ‘z’, i.e. ‘Values=z@f’. Note that for fields with less than several kilobytes of data, this feature may actually increase the output file size.

There are a variety of types defined which represent certain expectations in the data format exclusively for the sake of visualization. The formats are:

Allele Sequence - ‘Data=s’ or ’Data=zs’
Cytoband - ‘Cytoband=s∖tStain=s’
Gene - ‘Name=s∖tStrand=s∖tCodon Start=i∖tCodon Stop=i∖tExon Starts=@i∖tExon Stops=@i’
Interval - Anything, but the first value is used for the name
Intensity - ‘Value=f’ or ‘Value=f8’ or ‘Value=i’
Probe - ‘Name=s∖tObserved=s’, Observed field contents should be of the form ’A/C’

Set the filter for a Track Reader

Syntax

new variable = track reader.setFilter(filter text)

Example

myReader.setFilter(‘Value < 0.5’)

This command replaces any existing filters with the filter provided. Returns true if the filter was parsed correctly.

A filter is a clause of the format ‘[field name] [comparison] [value]’, i.e.‘Name contains BRC’, or ‘Value < 0.5’. Valid comparisons are: ‘contains’, ‘equals’ (=,==,eq), ‘less than’ (<,lt), ‘greater than’ (>,gt), ‘lt eq’ (<=,lteq), ‘gt eq’ (>=,gteq).

Each clause can be combined with another clause via ‘OR’ or ‘AND’, i.e. ‘Value > 0.4 AND Value < 0.6’, ‘Name contains BRC OR Value < 0.5’.

text = myReader.uuid()

Output

text == u’{b754a7f7-74ad-44ce-9591-5429ff127f86}’

This query returns the Universally Unique Identifier (UUID) for the track. This field is useful within the application to identify multiple instances of the same track.

Stream Features to an Annotation Track Writer

Syntax

track reader.write(track writer)

Example

myReader.write(myWriter)

This command is a convenience function that is equivalent to:

while myReader.hasNext(): writer.writeFeature(myReader.next())

Writing to an Annotation Track

Create A New Annotation Track

Syntax

new writer object = ghi.genomebrowser.createTrack(file name, title, type, coordSysId, schemaDesc, uuid)

Example

myReader = ghi.genomebrowser.createTrack(‘conservationTrack.idf’, ‘Mammalian Conservation’, ‘Intensity’, ‘NCBI_36,Chromosome,Homo sapiens’, ‘Conservation=f’, ‘{b754a7f7-74ad-44ce-9591-5429ff127f86}’)

This command creates a new annotation track in a specific IDF file, creating the file if it does not exist.

The file name may be either a relative or an absolute file name. A relative file name will be understood to be in the annotations folder of the active project. Calling this function with a relative name when a project is not open will result in an error. Absolute filenames (such as ’C:/temp/testFile.idf’) can be used as well. For convenience, certain file locations can be described using macros:

‘%PROJECTPATH%’ - References the annotation folder in the active project directory.
‘%DATAPATH%’ - References the annotation folder in the user data directory.
‘%SYSTEMPATH%’ - References the genome maps folder in the application’s installation directory.

To reference a file in the system directory, for example: ‘%SYSTEMPATH%/cytobands_ucsc(NCBI_36).idf’.

The title of the track is the name to be displayed when tracks are listed.

The schema parameter is a tab delimited set of field descriptions which specify the name and data type of the field. See Reading an Annotation Track for more detail.

The optional coordSysId parameter defines the coordinate system identifier. See Reading an Annotation Track. By default the coordSysId is ‘NCBI_36,Chromosome,Homo sapiens’.

The optional uuid parameter enables overriding the creation of a new Universally Unique Identifier for the new track. By assigning this parameter, two tracks can be identified as being equivalent. Most use of this parameter is internal to the application, and it can be safely ignored.

Opening An Existing Track For Writing

Syntax

track writer object = ghi.genomebrowser.openForWrite(file name)

Example

myWriter = ghi.genomebrowser.openForWrite(‘outputFile.idf’)

This command will return a writer for an existing track, allowing new features to be added to the track.

The file name can be either a relative or absolute file name. See Writing to an Annotation Track for more on track file names.

Write a Feature to an Annotation Track

Syntax

track writer.writeFeature(chromosome, start, stop, value, style id)

track writer.writeFeature(chromosome, start, stop, value list, style id)

Example

myWriter.writeFeature(‘1’, 1923450, 1923460, 0.34, 1)

myWriter.writeFeature(‘1’, 1923450, 1923460, [‘p5.1’, ‘gneg’], 1)

This command writes a feature to the annotation track.

The value parameter can be a non-list type for tracks with schema’s containing only a single value. For more complex schemas, data must be provided as a list that is ordered according to the schema description.

The style id parameter references a style record in the style table of the track. See Writing to an Annotation Track for more detail.

Write a Style Record to an Annotation Track

Syntax

track writer.writeStyle(style id, style key, style value)

Example

myWriter.writeStyle(1, ‘shape’: ‘box’)

This command assigns style data to a style id.

Flush Track Data to Disk

Syntax

track writer.flush()

Example

myWrite.flush()

This command blocks execution from returning until all data has been written to disk.

Using Progress or Status Dialogs

Initializing a Progress Dialog

Syntax

progress dialog object = ghi.progressDialog(dialog caption, total number of progress increments, allow cancel)

Example

myProgress = ghi.progressDialog(“Please Wait”, 100, 1)

This method will create a progress dialog which can be used to display the progress of a certain task and to signal the cancellation of a process. There are two required arguments for this method, and one optional argument. The first argument specifies the text to be displayed on the progress dialog. The second argument defines the number of progress increments for the progress dialog.

The third, optional, argument has two possible settings:

0: do not allow the process to be canceled
1 (default): allow the process to be canceled

Initializing a Status Dialog

Syntax

status dialog object = ghi.statusDialog(status caption, allow cancel)

Example

myStatus = ghi.statusDialog(“Calculating...”, 1)

This method will create a status dialog which can be used to indicate a process is underway and to signal the cancellation of a process. There are no required arguments for this method, and two optional arguments. The first optional argument specifies the text to be displayed on the status dialog. The second argument indicates if a Cancel button is included on the dialog to allow the process to be canceled.

0 (default): do not allow the process to be canceled
1: allow the process to be canceled

Optional second parameter: Specify a new label string for the progress dialog
Optional third parameter: Allow the process to be canceled
- 0: do not allow cancel
- 1 (default): do allow cancel
Optional fourth parameter: Indicates whether the cumulative step history from the creation of the progress dialog object should be used for the incrementing of the progress
- 0: do not keep the step history
- 1: do keep the step history

Set the Status Mode

Syntax

status dialog option.setStatusMode(new status label, show cancel indicator, keep step history indicator)

Example

myStatus.setStatusMode(“Still Working...”, 1, 1)

Renews the status dialog to keep the dialog active with a new message and options, or converts a progress dialog to a status dialog. There are no required parameters and three optional parameters. The three optional parameters are detailed below.

Optional first parameter: Specify a new label string for the status dialog
Optional second parameter: Allow the process to be canceled
- 0 (default): do not allow cancel
- 1: do allow cancel
Optional third parameter: Indicates whether the cumulative step history from the creation of the status dialog object should be kept
- 0: do not keep the step history
- 1: do keep the step history

Set up a Double Progress Bar Dialog

Syntax

progress dialog object.setDoubleProgressMode(auto increment indicator, total number of steps, new dialog label, show cancel indicator, keep step history indicator)

Example

myProgress.setDoubleProgressMode(1, 1000, “New Progress Step 2”, 1, 1)

Updates the progress dialog to add an additional progress bar for a sub-operation. The parameters for this command are detailed below.

First parameter: Indicate whether the progress bar should auto increment.
- 0: do not auto increment
- 1: auto increment the primary progress bar when the secondary progress reaches 100%.
Second parameter: Set the number of steps for the secondary progress bar.
Third parameter: Specify a new label string for the secondary progress dialog.
Fourth parameter: Allow the process to be canceled.
- 0: do not allow cancel
- 1 (default): do allow cancel
Fifth parameter: Indicates whether the cumulative step history from the creation of the progress dialog object should be used for the incrementing of the progress
- 0: do not keep the step history
- 1: do keep the step history

0: do not close the old dialog
1: do close the old dialog

0: Single-step process
1: Multi-step process

0: hide cancel button on dialog
1: show cancel button on dialog

Show the Value of the Progress Counter

Syntax

new variable = progress dialog object.value()

Example

myCount = myProgress.value()

Returns the last count used to update the progress dialog.

myBuilderObject.addRealColumn(“Response”, [0.78945, 2.334, -4.56732, …, -7.4560], 0)

This command adds a column of real values to the new dataset. The length of the Python list of real values must be equal to the number of rows specified when the dataset builder was initialized.

The real values can either be single- or double-precision floating point values. This is indicated by the optional third parameter.

0: real values are single-precision floating point values
1 (default): real values are double-precision floating point values

myBuilderObject.finish(4)

After all of the columns have been added to the dataset builder object, the dataset is ready to be added to the current project.

This command takes one optional argument specifying the id of the parent node. This command will add the dataset as a child of the project root unless an optional parent node is selected.

Check the Validity of the Dataset Builder

Syntax

dataset builder object.isValid()

Example

myBuilderObject.isValid()

This command checks to see if the dataset builder object is still valid. Either a 1 or a 0 is returned. If a 1 is returned then the dataset builder object is still valid and more columns can be added. If a 0 is returned then the dataset builder object is no longer valid and no more columns can be added without first creating another dataset builder object.

Building a Marker Map

As data is manipulated in scripting, there may be times when it is necessary to create a new marker map. The following set of commands allows a marker map to be constructed from Python lists.

Initializing a Marker Map Builder

Syntax

marker map builder object = ghi.markerMapBuilder(marker map name, list of markers, list of chromosomes, list of chromosome positions)

Example

myMarkerMapBuilder = ghi.markerMapBuilder(“My Marker Map”, [“SNP_1”,“SNP_2”,…,“SNP_100”], [“1”,“1”,…,“X”], [1,3,…,40065])

This command returns an object for use in building a new marker map. There are four required parameters for this command and they are detailed below.

First parameter: marker map name – must be a string
Second parameter: list of marker names, SNPs, ProbeIDs, etc. – this must be a list of strings
Third parameter: list of chromosomes – this must be a list of strings
Fourth parameter: list of chromosomal positions – this must be a list of integers

Commands for Spreadsheet Objects

Once a scripting spreadsheet has been created either by importing data or by building a dataset, the following commands can be used to manipulate the spreadsheet.

Specifying a Data Model

Syntax

new dataModel object = spreadsheet.dataModel([Optional Parameters])

Example

myDM = mySS.dataModel(ghi.constFilterBinary | ghi.const.FilterInt)

This command creates a data model based on the filtering flags provided. A PyDataModel is a view of the spreadsheet that can look at just active data and data of specific types. This is useful for analysis where a method has constraints on what data it can handle.

The flags are a logical OR (” | ”) combination of the following constants:

ghi.const.FilterActiveOnly Active data, not dependent
ghi.const.FilterDependent Dependent data
ghi.const.FilterActive (default) Active or dependent data, equivalent to
ghi.const.FilterActiveOnly | ghi.const.FilterDependent
ghi.const.FilterMapped Mapped columns only
ghi.const.FilterBinary Binary columns
ghi.const.FilterGenotypic Genetic columns
ghi.const.FilterCategorical Categorical columns
ghi.const.FilterInt Integer columns
ghi.const.FilterReal Real columns
ghi.const.FilterQuantitative Integer or Real columns, equivalent to ghi.const.FilterInt | ghi.const.FilterReal
ghi.const.UnsortedRows Unsorted rows, equivalent to clicking unsort on a spreadsheet

NOTE: The ghi.const.FilterMapped tag does not follow the logical OR but rather a logical AND. For example, (ghi.constFilterMapped | ghi.const.FilterReal) would specify only Real columns that are mapped.

Commands specific to dataModel objects are detailed in Commands for DataModel Objects.

Activate Rows or Columns for a Set of Chromosomes

Syntax

spreadsheet object.activateByChromosome(list of chromosomes)

Example

mySS.activateByChromosome([“1”, “2”, “X”])

This function activates marker mapped rows/columns which have a chromosome specified in the list. Marker mapped columns which have a chromosome that is not contained in the specified list will be inactivated.

The one required parameter for this command is a list of chromosomes which should be used to activate rows/columns. The chromosomes should be included as strings by name, e.g., “1”, “2”, “3”, ... “X”, “Y”, etc.

Create a Subset Spreadsheet From Active Data

Syntax

new spreadsheet object = spreadsheet object.activeSubset()

Example

myNewSS = mySS.activeSubset()

Creates a new spreadsheet object with all of the active data of the current spreadsheet object. If all of the data is active, then an error message indicating that some data needs to be deactivated in order to create an active subset spreadsheet will be displayed.

Append Two Spreadsheets

Syntax

appended spreadsheet object = spreadsheet object.appendSpreadsheet(node id or python spreadsheet object,
dataset name, [Optional Parameters])

Example

myAppendedSpreadsheet = mySS.appendSpreadsheet(45, “mySS Appended with SS Node 45”, dropColumns = 0, addToProjectRoot = 0)

myAppendedSpreadsheet = mySS1.appendSpreadsheet(mySS2,”mySS1 Appended with mySS2”, dropColumns = 0, addToProjectRoot = 1)

This command appends another spreadsheet to the current spreadsheet object. The required parameters are the node ID of the spreadsheet to append (or the name of another spreadsheet object), the new dataset name.

Two optional parameters can be specified, and their keyword arguments and defaults are detailed below. Note: Not all keyword arguments need to be used.

dropColumns:
- 0: All columns are kept, cells missing data are filled with missing values
- 1 (default): Columns that do not match are dropped.
addToProjectRoot:
- 0 (default): New spreadsheet is created as a child of the current spreadsheet object.
- 1: New spreadsheet is created as a child of the project root.

Apply a Marker Map to a Spreadsheet

Syntax

marker mapped spreadsheet object = spreadsheet object.applyMarkerMap(file name of DSM marker map file,[Optional Parameters])

Example

myMarkerMappedSS = mySS.applyMarkerMap(“myMarkerMap.dsm”, columnOriented = 1, dropDuplicates = 0)

To apply a marker map to a spreadsheet, you must have first imported a marker map to the Marker Maps folder, or converted a text marker map or MAP file to a DSM marker map dataset. Apply the marker map to a spreadsheet using the above command. The marker map file name should be used as the one parameter and does not need the path if the marker map is saved to the Marker Maps folder in the “SVS Data Directory”. This method will verify compatibility and return the new mapped spreadsheet. The mapped spreadsheet will also be added to the project as a child of the spreadsheet that is being mapped.

Two optional parameters can be specified, their keyword arguments and defaults are detailed below. Note: Not all keyword arguments need to be used.

columnOriented:
- 0: Marker names are row labels
- 1 (default): Marker names are column name headers
dropDuplicates:
- 0 (default): Apply the marker map to all instances of the same marker name.
- 1: Only apply the marker map to the first instance of the marker name. Delete all other columns or rows for the same marker from the new spreadsheet.

Select a Spreadsheet Cell

Syntax

new variable = spreadsheet object.cell(row number, column number)

Example

myVariable = mySS.cell(1,4)

This function returns the data from the spreadsheet cell found at the intersection of the specified row and column. Row 0 is the row containing column headers, and column 0 is the column containing the row labels (either generic or informative labels). An invalid row or column index throws an exception.

Select a Spreadsheet Column - 1 Based Index

Syntax

new Python list = spreadsheet object.col(column number, row state)

Example

myList = mySS.col(3, ghi.const.StateActive)

This function returns the spreadsheet column values for the selected column, but does not return the column header. An invalid column index throws an exception. Column number 0 corresponds to the row labels, column number 1 corresponds to the first column of data.

The possible row state values are as follows:

-1 (default): return all rows
ghi.const.StateInactive: return only inactive rows
ghi.const.StateActive: return only active rows

Select a Spreadsheet Column - 0 Based Index

Syntax

new Python list = spreadsheet object.zcol(column number, row state)

Example

myList = mySS.zcol(3, ghi.const.StateActive)

The possible row state values are as follows:

-1 (default): return all rows
ghi.const.StateInactive: return only inactive rows
ghi.const.StateActive: return only active rows

Obtain Column Headers

Syntax

new Python list = spreadsheet object.colHeaders(column state)

Example

myList = mySS.colHeaders(ghi.const.StateActive)

This command returns a list of the column headers for the specified column state.

The possible column state values are as follows:

-1 (default): return all column headers
ghi.const.StateInactive: return only inactive column headers
ghi.const.StateActive: return only independent/active column headers
ghi.const.StateDependent: return only dependent column headers

Obtain Column Indexes

Syntax

new Python list = spreadsheet object.col(column type, column state)

Example

myList = mySS.colIndexes(ghi.const.TypeGenotypic, ghi.const.StateActive)

Return a list of 1-based column indexes for a given column type and state.

The possible column type values are as follows:

-1 (default): all column type
ghi.const.TypeBinary: only binary columns
ghi.const.TypeInteger: only integer columns
ghi.const.TypeReal: only real columns
ghi.const.TypeCategorical: only categorical columns
ghi.const.TypeGenotypic: only genotypic columns

The possible column state values are as follows:

-1 (default): all column states
ghi.const.StateInactive: only inactive columns
ghi.const.StateActive: only independent/active columns
ghi.const.StateDependent: only dependent columns

Create a Column Subset Spreadsheet

Syntax

new spreadsheet object = spreadsheet object.colSubset()

Example

colSubsetSS = mySS.colSubset()

Creates a new spreadsheet from the active columns of the original spreadsheet.

Create a Top Level Spreadsheet

Syntax

new spreadsheet object = spreadsheet object.createTopLevelSpreadsheet(dataset name, active or all data indicator)

Example

myTopLevelSS = mySS.createTopLevelSpreadsheet(“My Top Level SS”, 0)

Takes a spreadsheet that is a child of a top level spreadsheet and creates a spreadsheet (and a new DSF file) that is a child of the project root. This spreadsheet is not dependent on other spreadsheets, and any dependencies are removed. The top level spreadsheet can be an exact copy of the original child spreadsheet, or just a subset based on active data.

The first parameter, the dataset name, is required.

The second parameter indicates if the top level spreadsheet should be created using only the active data or all data from the original spreadsheet. There are two possible settings:

0: All data is used in the creation of the top level spreadsheet.
1 (default): Only active data is used in the creation of the top level spreadsheet.

Drop a Marker Map from a Spreadsheet

Syntax

new spreadsheet object = spreadsheet object.dropMarkerMap()

Example

Syntax

list of column numbers = spreadsheet object.findCols(list of column names)

Example

myColNums = mySS.findCols([“nameOfCol56”, “nameOfCol58”, …, “nameOfCol200”])

This command searches for the columns in the spreadsheet whose column name headers are specified in the list of column names.

The column index of each of the columns found is returned in a list. If no column name headers match any of the strings provided, an empty list will be returned.

Find a Row in a Spreadsheet

Syntax

row number = spreadsheet object.findRow(row name)

Example

NOTE:

Accessing data for many cells using this method can be time consuming. If many cells need to be accessed, it is faster to get the field as a whole, and access data for all columns in that field before moving on to the next field.

Get Marker Map Field Names

Syntax

list of field names = marker mapped spreadsheet object.getMarkerMapFieldNames()

Example

myFieldNameList = mySS.getMarkerMapFieldNames()

This command returns a list containing the names of the fields contained in the applied marker map in a Python List. The names are listed in the order in which they appear in the marker map.

Get Marker Map Field Types

Syntax

list of field types = marker mapped spreadsheet object.getMarkerMapFieldTypes()

Example

myFieldNameList = mySS.getMarkerMapFieldTypes()

This command returns a list containing the type of each column contained in the applied marker map in a Python List. The types are listed in the order in which they appear in the marker map.

The returned types may take on one of the following values:

1: Integer
2: Real
3: Categorical

Get the Marker Map Offset in a Marker Mapped Spreadsheet

Syntax

offset = marker mapped spreadsheet object.getMarkerMapOffset()

Example

The four optional parameters are specified by keyword arguments. The keywords and defaults are listed below. Note: Not all keyword arguments need to be used.

newDatasetName: Specifies the name of the new dataset.
- “Joined Spreadsheet” default new dataset name
- or any other dataset name provided as a string
merge: Specifies if non-matching rows should be kept or dropped.
- 0 (default): Drop non-matching rows.
- 1: Keep non-matching rows, fill appropriate columns with missing values.
dupResolution: Specifies the behavior when there are duplicate columns.
- ghi.const.DupKeepBoth (default): Keep duplicate columns as separate columns in the new spreadsheet.
- ghi.const.DupFillLeft: Use the columns of the left spreadsheet to fill in missing values of the right spreadsheet.
- ghi.const.DupFillRight: Use the columns of the right spreadsheet to fill in missing values of the left spreadsheet.
childOfCurrent: Specifies if the new spreadsheet should be a child of the project root or a child of the current spreadsheet object.
- 0: Create the spreadsheet as a child of the project root.
- 1 (default): Create the spreadsheet as a child of the current spreadsheet object.

Get the Number of Columns in the Spreadsheet

Syntax

new variable = spreadsheet object.numCols()

Example

numberOfColumns = mySS.numCols()

This command returns the number of columns (not including the row label column) in the spreadsheet object.

Get the Number of Columns of a Certain State

Syntax

new variable = spreadsheet object.numColsState(numerical column state id)

Example

numberActive = mySS.numColsState(ghi.const.StateActive)

This command returns the number of columns of the specified state. The possible options for the column state id are:

ghi.const.StateInactive: Returns number of inactive columns
ghi.const.StateActive: Returns number of active columns
ghi.const.StateDependent: Returns number of columns set as dependent variables

Get the Number of Rows in the Spreadsheet

Syntax

new variable = spreadsheet object.numRows()

Example

numberOfRows = mySS.numRows()

This command returns the number of rows in the spreadsheet, not including the column header row.

Get the Number of Rows of a Certain State

Syntax

new variable = spreadsheet object.numRowsState(numerical row state id)

Example

numberActive = mySS.numRowsState(ghi.const.StateInactive)

This command returns the number of rows of the specified state. The possible options for the row state id are:

ghi.const.StateInactive: Returns number of inactive rows
ghi.const.StateActive: Returns number of active rows

Randomly Shuffle Rows

Syntax

spreadsheet object.permuteRows()

Example

mySS.permuteRows

This command randomly permutes the rows in the spreadsheet by modifying the sort order at random. Subsequent calls to this command will give new permutations, based on the current random number seed.

Plot a Column or Columns in Numeric Value Plot(s)

Syntax

spreadsheet object.plotColumns(column number)

spreadsheet object.plotColumns(list of column numbers, [Optional Parameter])

Example

mySS.plotColumns(1)

mySS.plotColumns([1,2,5], oneItemPerGraph=1)

This command takes a column index or list of indices for a numeric column and plots numeric value plots. The X-axis corresponds to row labels and the Y-axis corresponds to the values in the numeric column.

The one required parameter is either a column index or a Python list of column indices indicating which values to plot.

There is one optional parameter for when more than one column is plotted. This parameter is specified by a keyword argument as detailed below.

oneItemPerGraph:
- 0: All items are plotted in one graph.
- 1 (default): All items are plotted in separate graphs.

Create XY Scatter Plot(s) by Setting Dependent State in the Spreadsheet

Syntax

spreadsheet object.plotDependents(column number,[Optional Parameter])

Example

mySS.plotDependents(1, oneItemPerGraph = 1)

This command takes a column index for a numeric column and plots XY scatter plots. The independent variable (X-axis) corresponds to the column specified and the dependent variable(s) are all columns whose states are set as dependent in the current spreadsheet object.

The one required parameter is a column index specifying the independent variable or X-axis.

There is one optional parameter for when more than one column is plotted. This parameter is specified by a keyword argument as detailed below.

oneItemPerGraph:
- 0: All items are plotted in one graph.
- 1 (default): All items are plotted in separate graphs.

Create XY Scatter Plot(s) by Specifying both Independent and Dependent Columns

Syntax

spreadsheet object.plotXY(independent column number,list of dependent columns,[Optional Parameter])

Example

mySS.plotXY(1,[2,3,4,5] oneItemPerGraph = 1)

This command takes a column index for the independent column and plots XY scatter plots for each specified dependent column. The independent variable (X-axis) corresponds to the column specified and the dependent variable(s) are all columns specified in the dependent column list.

The required parameters are the a independent column index and the list of dependent column indexes.

There is one optional parameter for when more than one column is plotted. This parameter is specified by a keyword argument as detailed below.

oneItemPerGraph:
- 0: All items are plotted in one graph.
- 1 (default): All items are plotted in separate graphs.

Plot a Column or Columns in Histograms

Syntax

spreadsheet object.plotHistograms(column number)

spreadsheet object.plotHistograms(list of column numbers, [Optional Parameter])

Example

mySS.plotHistograms(1)

mySS.plotHistograms([1,2,5], oneItemPerGraph=1)

This command takes a column index or list of indices for a numeric column and plots histograms. The X-axis corresponds to the values in the numeric column(s) and the Y-axis corresponds to frequency counts for each histogram bin.

The one required parameter is a column index or a Python list of column indices indicating which values to plot in a histogram.

There is one optional parameter for when more than one column is plotted. This parameter is specified by a keyword argument as detailed below.

oneItemPerGraph:
- 0: All items are plotted in one graph.
- 1 (default): All items are plotted in separate graphs.

The possible column state values are as follows:

-1 (default): return data from all columns
ghi.const.StateInactive: return data from only inactive columns
ghi.const.StateActive: return data from only independent/active columns
ghi.const.StateDependent: return data from only dependent columns

Select a Spreadsheet Row - 0 Based Index

Syntax

list of row elements = spreadsheet object.zrow(row number, column state)

Example

myRowData = mySS.zrow(3, ghi.const.StateActive)

This command returns a list of elements in a row given by the specified row number, but does not return the row label. Row number 0 is the first row of data. Note that row access is generally slower than column access. An error is displayed if an invalid row number is specified.

The possible column state values are as follows:

-1 (default): return data from all columns
ghi.const.StateInactive: return data from only inactive columns
ghi.const.StateActive: return data from only independent/active columns
ghi.const.StateDependent: return data from only dependent columns

Obtain Row Indexes

Syntax

new Python list = spreadsheet object.rowIndexes(row state)

Example

myList = mySS.rowIndexes(ghi.const.StateActive)

Return a list of 1-based row indexes for a given row state.

The possible row state values are as follows:

-1 (default): return all row indexes
ghi.const.StateInactive: return only inactive row indexes
ghi.const.StateActive: return only active row indexes

Obtain Row Labels

Syntax

new Python list = spreadsheet object.rowLabels(row state)

Example

myList = mySS.rowLabels(ghi.const.StateActive)

This command returns a list of the row labels for the specified row state.

The possible row state values are as follows:

-1 (default): return all row labels
ghi.const.StateInactive: return only inactive row labels
ghi.const.StateActive: return only active row labels

The third parameter is optional and indicates whether rows corresponding to column values less than or greater than the threshold should be activated. The direction of the activation is indicated below:

0: Greater than
1 (default): Less than

Set the Column State

Syntax

spreadsheet object.setColState(column number or a list of column numbers, column state indicator)

Example

mySS.setColState(3,ghi.const.StateInactive)

mySS.setColState([3,5,9,12],ghi.const.StateInactive)

This command sets the specified column or list of columns to the specified state. The first parameter is the column number or a Python list of column numbers. The second parameter is the column state. Other column states remain unchanged. There are three possible states:

ghi.const.StateInactive: Inactive
ghi.const.StateActive: Active/Independent
ghi.const.StateDependent: Dependent

Set the Row State

Syntax

spreadsheet object.setRowState(row number, row state indicator)

spreadsheet object.setRowState(first row number, last row number, row state indicator)

spreadsheet object.setRowState(list of row numbers, row state indicator)

Example

mySS.setRowState(3, ghi.const.StateInactive)

mySS.setRowState(3, 15, ghi.const.StateInactive)

mySS.setRowState([4,5,7,15], ghi.const.StateInactive)

This command sets the specified row, range of rows, or rows specified in the list to the specified state. In the first and third cases, there are two required parameters; in the second case, there are three required parameters.

The first case sets the state of one particular row. For this case, the first parameter is the row number. The second parameter is the row state. Other row states remain unchanged. There are two possible states detailed below.

The second case sets the state of a range of rows. For this case, the first parameter is the first row number of the range, the second parameter is the last row number of the range of rows. The third parameter is the row state. Other row states remain unchanged. There are two possible states detailed below.

The third case sets the state of those rows specified in a list. For this case, the first parameter is the list of row numbers. The second parameter is the row state. Other row states remain unchanged. There are two possible states detailed below:

ghi.const.StateInactive: Inactive
ghi.const.StateActive: Active/Independent

Set the State of Rows Randomly

Syntax

spreadsheet object.setRowStateRandom(number of rows to select randomly, row state indicator)

spreadsheet object.setRowStateRandom(percent of rows to select randomly, row state indicator)

Example

mySS.setRowStateRandom(100, ghi.const.StateInactive)

mySS.setRowStateRandom(0.5,ghi.const.StateActive)

This command sets the randomly selected number or percent of rows to the specified state. In the each case, there are two required parameters.

The first case will set a number of randomly selected rows to the specified state. Other rows are set to the opposite state. There are two possible states detailed below.

The second case will at random set a fraction of the total number of rows to the specified state. The percent parameter must be a number between 0 and 1. This is useful for selecting a certain percentage of the data irregardless of the number of rows. Other rows are set to the opposite state. There are two possible states detailed below:

ghi.const.StateInactive: Inactive
ghi.const.StateActive: Active/Independent

The following numbers could also be returned:

0: sorted by row labels
-1: not sorted
-2: sorted by custom order

Get the Sort Direction Used in Sorting

Syntax

new variable = spreadsheet object.sortDirection()

Example

mySortDirection = mySS.SortDirection()

Returns the sort direction last used. This command should be used in conjunction with the sortColIdx() command.

There are two possible outcomes:

0: Last sort was in ascending order
1: Last sort was in descending order

Transpose the Spreadsheet

Syntax

new spreadsheet object = spreadsheet object.transpose(new dataset name, type of columns to transpose, [Optional Parameters])

Example

myTransposedSS = mySS.transpose(“SS Transposed”, ghi.const.DataTypeGenotypic, labelHeader = “SNPs”, activeDataOnly = 0, childOfCurrent = 0)

This command transposes all columns of the same column type in the spreadsheet. There are two required parameters for this command and three optional parameters. The two required parameters are, in order, the new dataset name, and the type of the columns to transpose. The possible types of columns are as follows:

ghi.const.DataTypeBinary: Binary Columns
ghi.const.DataTypeInteger: Integer Columns
ghi.const.DataTypeFloat: Columns of Single-Precision (4 bytes) floating point numbers
ghi.const.DataTypeDouble: Columns of Double-Precision (8 bytes) floating point numbers
ghi.const.DataTypeCategorical: Categorical/Nominal Columns
ghi.const.DataTypeGenotypic: Genotypic Columns

The three optional parameters are specified by keyword arguments. The keywords and defaults are listed below. Note: Not all keyword arguments need to be used.

labelHeader: Specify a header for the column names. This parameter takes string arguments.
activeDataOnly: Indicates if all data or only active data is to be transposed.
- 0 (default): All data is to be transposed
- 1: Only active data is to be transposed
childOfCurrent: Indicates if the transposed spreadsheet is to be created as a child of the current spreadsheet or of the project root.
- 0: Created as a child of the project root
- 1 (default): Created as a child of the current spreadsheet
memoryLimit: Indicates the maximum amount of memory to be used while transposing.
- (default) current program limit for transpose memory usage
- any integer value 64 ≤ value ≤ program maximum cache size

Unsort the Spreadsheet

Syntax

spreadsheet object.unsort()

Example

mySS.unsort()

Returns the spreadsheet to the original row order.

Commands for DataModel Objects

Select a Data Model Row - 1 Based Index

Syntax

list of row elements = dataModel object.row(row number)

Example

myRowData = myDM.row(3)

Select a Data Model Row - 0 Based Index

Syntax

list of row elements = dataModel object.zrow(row number)

myVariable = myDM.cell(1,4)

new variable = dataModel object.numCols()

Example

numberOfColumns = myDM.numCols()

This command returns the number of columns (not including the row label column) in the spreadsheet object.

Get the Number of Rows in the Data Model

Syntax

new variable = dataModel object.numRows()

Example

numberOfRows = myDM.numRows()

This command returns the number of rows in the spreadsheet, not including the column header row.

Determine if a Data Model Has a Marker Map Applied

Syntax

new variable = dataModel object.hasMarkerMap()

myVariable = myMarkerMappedDM.markerMapFieldCell(3, 6)

This command returns the data from the applied marker map for the given field at the given column number in a Python object of a type corresponding to the cell’s data type.

NOTE:

Accessing data for many cells using this method can be time consuming. If many cells need to be accessed, it is faster to get the field as a whole, and access data for all columns in that field before moving on to the next field.

Get Marker Map Field Names

Syntax

list of field names = marker mapped dataModel object.markerMapFieldNames()

Example

myFieldNameList = myMarkerMappedDM.markerMapFieldNames()

This command returns a list containing the names of the fields contained in the applied marker map in a Python List. The names are listed in the order in which they appear in the marker map.

Get Marker Map Field Types

Syntax

list of field types = marker mapped dataModel object.markerMapFieldTypes()

Example

myFieldNameList = myMarkerMappedDM.markerMapFieldTypes()

This command returns a list containing the type of each column contained in the applied marker map in a Python List. The types are listed in the order in which they appear in the marker map.

The returned types may take on one of the following values:

1: Integer
2: Real
3: Categorical

Get the Marker Map Offset in a Marker Mapped Spreadsheet

Syntax

offset = marker mapped dataModel object.markerMapOffset()

Example

myOffset = myMarkerMappedDM.markerMapOffset()

Get the Marker Map Orientation of a Spreadsheet

Syntax

orientation = dataModel object.getMarkerMapOrientation()

Example

myOrientation = myDM.getMarkerMapOrientation()

This command returns the orientation of the marker map. This function returns the column type as one of the following values.

0: There is no marker map applied
1: The map is oriented along the columns of the spreadsheet
2: The map is oriented along the rows of the spreadsheet

Analysis with Spreadsheet Objects

Once a scripting spreadsheet has been created either by importing data or by building a dataset, the following commands can be used to analyze the spreadsheet’s data.

CNAM Segmentation

Syntax

list of objects = spreadsheet object.cnamSegmentation([Optional Parameters])

Example

myResultList = mySS.cnamSegmentation(algorithm = ghi.const.SegmentMultivariate, movingWindow = 0, maxSegments = 20, memSize = 512)

This command performs CNAM optimal segmenting analysis on the current spreadsheet object and returns a list of objects containing the results of the segmentation procedure.

The objects and indexes of the list returned from this command are as follows:

0: Segmentation Covariates Every Column spreadsheet
1: Segmentation Covariates First Column spreadsheet
2: Segment List spreadsheet
3: Segmentation Run Log viewer

All parameters are specified by keyword arguments and are optional. They are as follows:

algorithm: Indicates whether a univariate or multivariate algorithm should be used. The possible values are:
- ghi.const.SegmentUnivariate (default): univariate algorithm
- ghi.const.SegmentMultivariate: multivariate algorithm
- specifyMemoryLimit: If algorithm = ghi.const.SegmentMultivariate, indicates that CNAM should use a user-specified memory limit.
- memoryLimit: If specifyMemoryLimit = True, memory limit in MB.
useMovingWindow: Indicates whether a moving window should be used as a speed optimization. The possible values are:
- 0 (default): no moving window will be used
- 1: a moving window should be used
- windowSize: If applicable, indicates the size of the moving window to be used. The possible values are:
  - 20000 (default)
  - any positive integer value
maxSegmentsPer10k: Indicates the maximum number of expected segments in each window. The possible values are:
- 10 (default)
- any positive integer value
minMarkers: Indicates the minimum number of markers needed to be considered a segment. The possible values are:
- 1 (default)
- any positive integer value
maxPairwisePVal: Indicates the significance level for comparing adjacent segments. The possible values are:
- 0.005 (default)
- any floating point value between 0.0 and 1.0
- NOTE: The significance level can be much higher if running multivariate segmentation.
- NOTE: If the significance level is set to 1, permutation testing is not done.
numThreads: Indicates the number of threads to use when computing optimal segments. The possible values are:
- 2 (default)
- any integer 1 ≤ numThreads ≤ 64
outputEveryColumn: Indicates whether to create output for each marker. The possible values are:
- 0: Do not create output for each marker
- 1 (default): create output for each marker
- NOTE: At least one Segmentation Covariates (Every Column or First Column) must be outputted.
outputFirstColumn: Indicates whether to create output for the first column of each segment. The possible values are:
- 0: Do not create output for the first column of each segment
- 1 (default): create output for the first column of each segment
- NOTE: At least one Segmentation Covariates (Every Column or First Column) must be outputted.
memSize: Indicates the amount of memory(in MB) to use during segmentation. The possible values are:
- 256 (default)
- any integer 128 ≤ memSize ≤ 32768
wiggleFile: Indicates that UCSC wiggle files (one for each row/sample) should be written to the given location.
removeOutliers: Indicates whether or not to remove univariate outliers. The possible values are:
- 0: do not remove outliers
- 1 (default): remove outliers
fullLogging: Indicates whether or not to produce detailed log output.
- 0 (default): Output basic logging.
- 1: Output detailed logging.
useHardwareAcceleration: Indicates whether to use OpenCL accelerated segmentation.
- 0 (default): disable OpenCL acceleration
- 1: enable OpenCL acceleration
- openCLDevice: If useHardwareAcceleration = True, the one-based index of the device to use.
  - 1(default): Use the first available device.
  - an integer specifying the index of the device to use, such that 1 ¡= device ¡= number of devices.

CNV Association Tests

Syntax

list of objects = spreadsheet object.cnvAssociationTests([Optional Parameters])

Example

myResultList = mySS.cnvAssociationTests(tTest = 1, corrTrend = 1, bonferroni = 0)

This command performs CNV association tests using the current spreadsheet and creates a list of spreadsheets containing the test results. The list of spreadsheets will be of consistent size, and if a particular spreadsheet is not created during the tests, then the returned list will contain 0 at the corresponding index.

The following is an outline of the returned list of spreadsheets by list index:

0: Association test output
1: PCA-corrected input
2: Principal component spreadsheet
3: Principal component eigenvalues
4: PCA outlier spreadsheet

All parameters are specified by keyword arguments and are optional. They are as follows:

tTest: Indicates whether or not to perform a T-Test. The possible values are:
- 0 (default): do not perform a T-Test
- 1: perform a T-Test
corrTrend: Indicates whether or not to perform the Correlation/Trend Test. The possible values are:
- 0 (default): do not perform Correlation/Trend Test
- 1: perform Correlation/Trend Test
regression: Indicates whether or not to perform regression. The possible values are:
- 0 (default): do not perform regression
- 1: perform regression
bonferroni: Indicates whether or not to use Bonferroni adjustment. The possible values are:
- 0: do not use Bonferroni adjustment
- 1 (default): use Bonferroni adjustment
fdr: Indicates whether or not use False Discovery Rate. The possible values are:
- 0: do not use False Discovery Rate
- 1 (default): use False Discovery Rate
singleValuePermutations: Indicates whether or not to perform single value permutation tests. The possible values are:
- 0 (default): do not perform single value permutation tests
- 1: perform single value permutation tests
fullScanPermutations: Indicates whether or not to perform full-scan permutation tests. The possible values are:
- 0 (default): do not perform full scan permutation tests
- 1: perform full scan permutation tests
numPermutations: (if applicable) indicates the number of permutations to be used for the selected permutation tests. The possible values are:
- 0 (default)
- any positive integer value ≥ 3
outputPPQQ: Indicates whether to output data for P-P/Q-Q plots. The possible values are:
- 0 (default): do not output P-P/Q-Q data
- 1: output P-P/Q-Q data
dataCentering: Indicates whether perform data centering by marker, sample, or both. The possible values are:
- ghi.const.PcaUncentered (default): do not perform data centering
- ghi.const.PcaCenteredByMarker: perform data centering by marker
- ghi.const.PcaCenteredBySample: perform data centering by sample
- ghi.const.PcaCenteredByMarkerAndSample: perform data centering by marker and by sample
usePca: Indicates whether or not to use PCA. The possible values are:
- 0 (default): do not use PCA
- 1: use PCA
usePcaForDependent: If applicable, indicates whether to use a PCA corrected dependent. The possible values are:
- 0 (default): do not use a PCA corrected dependent
- 1: use a PCA corrected dependent

The following PCA parameters are available if usePca = 1 was specified:

pcaPrecomputedSheet: Indicates the use of pre-computed components. The possible values are:
- the node ID (spreadsheet number) of the pre-computed principal components spreadsheet
pcaMaxTopComponents: Indicates the maximum number of PCA components. The possible values are:
- 10 (default)
- any positive integer value
pcaOutputCorrected: (if applicable) indicates whether or not to output a spreadsheet containing PCA corrected values. The possible values are:
- 0 (default): do not output corrected input data
- 1: output corrected input data
pcaOutputPcSheet: (if applicable) indicates whether or not to output a principal components spreadsheet. The possible values are:
- 0: do not output a principal components spreadsheet
- 1 (default): output a principal components spreadsheet
pcaOutputEigenSheet: (if applicable) indicates whether or not to output a spreadsheet of eigenvalues. The possible values are:
- 0: do not output an eigenvalue spreadsheet
- 1 (default): output an eigenvalue spreadsheet
pcaRecompute: (if applicable) whether or not to remove outliers and recompute principal components. The possible values are:
- 0 (default): do not recompute principal components
- 1: recompute principal components
pcaRecompStdDev: (if applicable) indicates the number or standard deviations with which to identify outliers. The possible values are:
- 6.0 (default)
- any floating point value
pcaRecompCount: (if applicable) indicates the number of times to recompute principal components. The possible values are:
- 0 (default)
- any positive integer value
pcaRecompComponents: (if applicable) indicates the number of components used in identifying outliers. The possible values are:
- 5 (default)
- any positive integer value

Find LD Between Two Markers

Syntax

LD value = spreadsheet object.computeLD(first column number, second column number, method, output statistic type, [Optional Parameters])

Example

myLDValue = mySS.computeLD(2, 5, ghi.const.ImputeEM, ghi.const.StatRSquared, imputeMissings = 1, maxIters = 40, convTolerance = .001)

Syntax

LD value = dataModel object.computeLD(first column number, second column number, method, output statistic type, [Optional Parameters])

Example

myLDValue = myDM.computeLD(2, 5, ghi.const.ImputeEM, ghi.const.StatRSquared, imputeMissings = 1, maxIters = 40, convTolerance = .001)

This function returns the LD value between the two specified marker columns. This function can be used on both spreadsheet and dataModel objects.

The first four parameters are required. The first two are the column numbers of the markers. The third is the method parameter, which may have two possible settings:

ghi.const.ImputeCHM CHM algorithm
ghi.const.ImputeEM EM algorithm

The fourth required parameter is the type of output statistic requested, which may have two possible settings:

ghi.const.StatRSquared R-squared
ghi.const.StatDPrime D-prime

The three optional parameters are specified by keyword arguments. The keyword arguments and defaults are listed below. Note: These keyword arguments are only applicable when using the EM algorithm. Note: Not all keyword arguments need to be used.

imputeMissings: Indicates whether or not to impute missing values.
- 0 (default): do not impute missing values
- 1: impute missing values
maxIters: Indicates the maximum number of EM iterations performed regardless of reaching convergence
- (default) 50
- any integer ≥ 1
convTolerance: Indicates the desired tolerance that the EM algorithm will use to determine convergence
- (default) 0.0001
- any floating point value ≥ 0

Compute Genotype Allele Counts

Syntax

genotype allele counts = spreadsheet object.genotypeAlleleCounts(column number)

Syntax

genotype allele counts = dataModel object.genotypeAlleleCounts(column number)

This function returns a list of pairs of allele names and the ocunt of those alleles in a genotypic column. The missing allee would always be the last in the list regardless of its count. This function works on both spreadsheet and dataModel objects.

For example, if a genotypic columnn had the values:

[’A_A’, ’A_B’, ’A_A’, ’B_B’, ’?_?’]

Calling this function on that column would return: [[’A’, 5], [’B’,3], [’?’,2]]

For bi-allelic columsn, the major allele will always be the first and the minor allee the second in the list.

Filter Data by Genotype

Syntax

marker filter status spreadsheet = spreadsheet object.filterGenotypes([Optional Parameters])

spreadsheet object.filterGenotypes([Optional Parameters])

Example

myStatusSpreadsheet = mySS.filterGenotypes(filterBasis = ghi.const.ControlsOnly, callRate = [ghi.const.LessOrEqual, .75], maf = [ghi.const.LessThan, .07])

This command performs genotype filtering, with an option to deactivate columns that do not pass filtering criteria, as well as an option to create a spreadsheet containing column statistics and filtering status.

This command returns a spreadsheet containing statistics and a filter status for each marker, unless the optional parameter outputMarkersSheet is set to zero. In that case, nothing is returned from this command.

All parameters are specified by keyword arguments and are optional. Four are general parameters. These are as follows:

outputMarkersSheet: Indicates whether or not to output a spreadsheet containing statistics and filter status for each marker.
- 0: do not create a spreadsheet
- 1 (default): output a spreadsheet
inactivateCols: Indicates whether or not to deactivate columns that do not meet filtering criteria.
- 0: do not inactivate columns
- 1 (default): inactivate columns
filterBasis: Indicates what samples to filter markers by.
- ghi.const.CasesAndControls (default): filter using all data
- ghi.const.ControlsOnly: filter using controls only
- ghi.const.CasesOnly: filter using cases only
outputNegLog: If applicable, indicates whether or not to output negative log p-values in the filtering status spreadsheet.
- 0 (default): do not output negative log P-Values
- 1: output negative log P-Values

The following parameters indicate which test statistics to calculate. The use of any of these parameters specifies that the corresponding test statistic should be calculated. The values for each of the parameters should be a list where the first index contains an integer indicating what type of filter to use, and the second index should contain a floating point value representing the threshold to use when filtering.

Filtering types are represented as follows:

ghi.const.LessThan: drop values less than the selected threshold
ghi.const.LessOrEqual: drop values less than or equal to the selected threshold
ghi.const.GreaterThan: drop values greater than the selected threshold
ghi.const.GreaterOrEqual: drop values greater than or equal to the selected threshold

The parameters that require values in this form are as follows:

callRate: indicates whether or not to filter on call rates
maf: indicates whether or not to filter on minor allele frequency
hwep: indicates whether or not to filter on HWE P-Value
fisherHwep: indicates whether or not to filter on Fishers Exact HWE P-Val
signedHweR: indicates whether or not to filter on Signed HWE R

Genotype Association Tests

Syntax

list of objects = spreadsheet object.genotypeAssociationTests([Optional Parameters])

Example

myResultList = mySS.genotypeAssociationTests(corrTrend = 1, outputPPQQ = 1, usePca = 1, pcaMaxTopComponents = 15, genoCounts = 1)

This command performs genotype association tests on the current spreadsheet, creating a list of output spreadsheets dependent on the options used in the tests. The list of spreadsheets that is returned will contain 0 for indexes where the corresponding spreadsheet is not created. Note that the tests available when running this analysis are dependent on the type of the dependent spreadsheet column, the genetic model selected, and other parameters specified.

The returned list of spreadsheets objects will always be the same size. If a spreadsheet that exists in a specific index is not created, the list will contain 0 for that index. The following is an outline of the returned list of spreadsheets by list index:

0: Association test output spreadsheet
1: PCA-corrected input spreadsheet
2: Principal component spreadsheet
3: Principal component eigenvalues spreadsheet
4: PCA outlier spreadsheet

All parameters are specified by keyword arguments and are optional. They are as follows:

geneticModel: Indicates the genetic model or tests used. The possible values are:
- ghi.const.ModelAllelic: Basic Allele Tests
- ghi.const.ModelGenotypic: Genotypic Tests
- ghi.const.ModelAdditive (default): Additive model
- ghi.const.ModelDominant: Dominant model
- ghi.const.ModelRecessive: Recessive model

The following tests are available to be performed when applicable:

corrTrend: Indicates whether or not to perform the Correlation/Trend Test
chiSq: Indicates whether or not to perform a Chi-Squared Test
armitage: Indicates whether or not to perform a Cochran-Armitage Test
exactArmitage: Indicates whether or not to perform an exact form of the Cochran-Armitage Test
fishers: Indicates whether or not to perform Fishers Exact Test
oddsRatio: Indicates whether or not to use Odds Ratio
analysisDev: Indicates whether or not to use Analysis of Deviance
regression: Indicates whether or not to perform regression
fTest: Indicates whether or not to perform an F-Test

For each of the above tests, use

0 (default): do not perform the specified test
1: perform the specified test

The following multiple testing/false positive adjustments are available:

bonferroni: Indicates whether or not to use Bonferroni adjustment
fdr: Indicates whether or not use False Discovery Rate
singleValuePermutations: Indicates whether or not to perform single value permutation tests
fullScanPermutations: Indicates whether or not to perform full-scan permutation tests

For each of the above adjustments, use

0 (default): do not use the specified adjustment
1: use the specified adjustment

Other association test parameters are as follows:

numPermutations: If applicable, indicates the number of permutations to be used for the selected permutation tests. The possible values are:
- 0 (default)
- any positive integer ≥ 3
showInflationFactor: Indicates whether or not to show inflation factor, Chi-Squares and corrected values used in genomic control. The possible values are:
- 0 (default): do not show the inflation factor
- 1: show inflation factor and related values
specifyInflationFactor: If applicable, specify an inflation factor to use for genomic control. The possible values are:
- 0 (default): do not specify an inflation factor
- 1: specify an inflation factor
inflationFactor: If applicable, the inflation factor (lambda) to use for genomic control. The possible values are:
- 0.0 (default)
- any floating point value ≥ 1
useMissings: Indicates whether or not to use missing values. The possible values are:
- 0 (default) drop missing values
- 1 use missing values
outputPPQQ: Indicates whether to output data for P-P/Q-Q plots. The possible values are:
- 0 (default) do not output P-P/Q-Q data
- 1 output P-P/Q-Q data
usePca: Indicates whether or not to use PCA. The possible values are:
- 0 (default) do not use PCA
- 1 use PCA

The following PCA parameters are available if usePca = 1 was specified:

pcaPrecomputedSheet: Indicates the use of pre-computed components. The possible values are:
- the node ID (spreadsheet number) of the pre-computed principal components spreadsheet
pcaMaxTopComponents: Indicates the maximum number of PCA components. The possible values are:
- 10 (default)
- any positive integer value
pcaOutputCorrected: Indicates whether or not to output a spreadsheet containing PCA corrected values. The possible values are:
- 0 (default): do not output corrected input data
- 1: output corrected input data
pcaOutputPcSheet: Indicates whether or not to output a principal components spreadsheet. The possible values are:
- 0: do not output a spreadsheet
- 1 (default): output a spreadsheet
pcaOutputEigenSheet: Indicates whether or not to output an eigenvalues spreadsheet. The possible values are:
- 0: do not output spreadsheet
- 1 (default): output spreadsheet
pcaNormalization: Indicates which method of PCA marker normalization to use for each marker. The possible values are:
- ghi.const.PcaNormDefault (default): normalize by theoretical std deviation under HWE
- ghi.const.PcaNormActual: normalize by actual standard deviation
- ghi.const.PcaNormNone: do not normalize marker data
pcaRecompute: Indicates whether or not to remove outliers and recompute principal components. The possible values are:
- 0 (default): do not recompute principal components
- 1: recompute principal components

If pcaRecompute = 1 was specified, then the following PCA parameters are applicable:

pcaRecompStdDev: The number of std deviations to identify outliers. The possible values are:
- 6.0 (default)
- any floating point value
pcaRecompCount: Indicates the number of times to recompute PCs. The possible values are:
- 0 (default)
- any positive integer value
pcaRecompComponents: Indicates the number of PCs used in finding outliers. The possible values are:
- 5 (default)
- any positive integer value

Genotype statistics may be output using the following parameters:

maf: Indicates whether to output minor allele frequency.
callRate: Indicates whether or not to output call rates.
fisherHwep: Indicates whether to output Fishers HWE P-Values.
hwep: Indicates whether to calculate HWE P-Values.
signedHweR: Indicates whether or not to output Signed HWE R.
genoCounts: Indicates whether or not to output genotype counts.
alleleCounts: Indicates whether or not to output allele counts.

For each of the above genotype statistics, use

0 (default): do not output the specified value
1: output the specified value

Genotypic Principal Components Analysis

Syntax

list of objects = spreadsheet object.genotypePCA([Optional Parameters])

Example

mySSList = mySS.genotypePCA(pcaMaxTopComponents = 35, pcaNormalization = ghi.const.PcaNormNone, pcaOutputCorrected = 1)

This command performs PCA analysis and returns a list of spreadsheet objects, the contents of which will vary depending on the options used.

0: PCA-corrected input spreadsheet
1: Principal component spreadsheet
2: Principal component eigenvalues spreadsheet
3: PCA outlier spreadsheet

All parameters are specified by keyword arguments and are optional. They are as follows:

pcaPrecomputedSheet: Indicates the use of pre-computed components. The possible values are:
- the node ID (spreadsheet number) of the pre-computed principal components spreadsheet
pcaGeneticModel: Indicates which model to perform PCA analysis under. The possible values are:
- ghi.const.PcaModelAdditive (default): additive model
- ghi.const.PcaModelDominant: dominant model
- ghi.const.PcaModelRecessive: recessive model
pcaNormalization: Indicates which method of PCA marker normalization to use for each marker. The possible values are:
- ghi.const.PcaNormDefault (default): normalize by theoretical std deviation under HWE
- ghi.const.PcaNormActual: normalize by actual standard deviation
- ghi.const.PcaNormNone: do not normalize marker data
pcaMaxTopComponents: Indicates the maximum number of PCA components. The possible values are:
- 10 (default)
- any positive integer value
pcaOutputCorrected: Indicates whether or not to output a spreadsheet containing PCA corrected values. The possible values are:
- 0 (default): do not output corrected input data
- 1: output corrected input data
pcaOutputPcSheet: Indicates whether or not to output a principal components spreadsheet. The possible values are:
- 0: do not output a spreadsheet
- 1 (default): output a spreadsheet
pcaOutputEigenSheet: Indicates whether or not to output an eigenvalues spreadsheet. The possible values are:
- 0: do not output spreadsheet
- 1 (default): output spreadsheet
pcaRecompute: Indicates whether or not to remove outliers and recompute principal components. The possible values are:
- 0 (default): do not recompute principal components
- 1: recompute principal components

If pcaRecompute = 1 was specified, then the following parameters are applicable:

pcaRecompStdDev: The number of std deviations to identify outliers. The possible values are:
- 6.0 (default)
- any floating point value
pcaRecompCount: Indicates the number of times to recompute PCs. The possible values are:
- 0 (default)
- any positive integer value
pcaRecompComponents: Indicates the number of PCs used in finding outliers. The possible values are:
- 5 (default)
- any positive integer value

Genotypic Statistics by Marker

Syntax

statistics spreadsheet object = spreadsheet object.genotypeStatsByMarker([Optional Parameters])

Example

mySS.genotypeStatsByMarker(maf = 1, callRate = 1, hwep = 1, signedHweR = 1)

This command creates an output spreadsheet containing the selected statistics.

All parameters are specified by keyword arguments and are optional. They are as follows:

maf: Indicates whether to output minor allele frequency.
callRate: Indicates whether or not to output call rates.
fisherHwep: Indicates whether to output Fishers HWE P-Values.
hwep: Indicates whether to calculate HWE P-Values.
signedHweR: Indicates whether or not to output Signed HWE R.
genoCounts: Indicates whether or not to output genotype counts.
alleleCounts: Indicates whether or not to output allele counts.
outputNegLog: Indicates whether or not to output negative log p-values

For each of the above genotype statistics, use

0 (default): do not output the specified value
1: output the specified value

Genotypic Statistics by Sample

Syntax

statistics spreadsheet object = spreadsheet object.genotypeStatsBySample([Optional Parameters])

Example

mySS.genotypeStatsBySample(callRate = 1, hwep = 1, outputNegLog = 1)

This command creates an output spreadsheet containing the selected statistics.

All parameters are specified by keyword arguments and are optional. They are as follows:

callRate: Indicates whether or not to output call rates.
hwep: Indicates whether to calculate HWE Thw P-Values.
outputNegLog: Indicates whether or not to output negative log p-values. This parameter can only be used if hwep = 1.

For each of the above genotype statistics, use

0 (default): do not output the specified value
1: output the specified value

Haplotype Association Tests

Syntax

result spreadsheet object = spreadsheet object.haplotypeAssociationTests([Optional Parameters])

Example

myResultsSS = mySS.haplotypeAssociationTests(blockDefinitionSource = ghi.const.HapBlockAllMarkers, chiSq = 1, oddsRatio = 1, fdr = 1)

This command runs haplotype association tests on the current spreadsheet and returns a reference to the results spreadsheet.

All parameters are specified by keyword arguments and are optional. Note that some parameters are only valid when other parameters are specified. They are as follows:

blockDefinitionSource: Indicates the source of the haplotype block definitions. The possible values are:
- ghi.const.HapBlockPrecomputed (default): perform tests on precomputed blocks
- ghi.const.HapBlockAllMarkers: perform tests on all markers as a single block
- ghi.const.HapBlockMovingWindow: perform tests using a moving window
blockDefinitionSheet: If applicable, specifies a spreadsheet containing haplotype block information. The possible values are:
- the node id of the haplotype block spreadsheet
- the column number of the haplotype block column
movingWindowType: If applicable, indicates whether to use a fixed or dynamic moving window. The possible values are:
- 1 (default) use a fixed window
- 2 use a dynamic window
fixedWindowSize: If applicable, indicates the fixed window size. The possible values are:
- 2 (default)
- any integer value ≥ 1
dynamicWindowBasePairs: If applicable, indicates the dynamic window size in kilo-base pairs. The possible values are:
- 10 (default)
- any integer value ≥ 1
limitDynamicMaxCols: If applicable, indicates the maximum dynamic moving window size. The possible values are:
- 20 (default)
- any integer value ≥ 1
haploCalculationType: (Optional) indicates whether to calculate tests on a per haplotype or per block basis. The possible values are:
- 1 (default) calculate tests per haplotype
- 2 calculate tests per block
chiSq: (Optional) indicates whether to calculate chi-squared. The possible values are:
- 0 (default) do not calculate chi-squared
- 1 calculate chi-squared
oddsRatio: (Optional) indicates whether or not to calculate odds ratio. The possible values are:
- 0 (default) do not calculate odds-ratio
- 1 calculate odds-ratio
regression: (Optional) indicates whether or not to perform regression tests. The possible values are:
- 0 (default) do not perform regression tests
- 1 perform regression tests
haplotypeMethod: (Optional) indicates whether to use the EM or CHM algorithm. The possible values are:
- 1 (default) calculate haplotype frequencies using the EM algorithm
- 2 calculate haplotype frequencies using the CHM algorithm
maxEmIterations: (Optional) maximum number of EM iterations regardless of reaching convergence. The possible values are:
- 50 (default)
- any integer value ≥ 1
emConvergeTolerance: (Optional) desired tolerance the EM algorithm will use to determine when it reaches convergence. The possible values are:
- 0.00001 (default)
- any floating point value
frequencyThreshold: (Optional) minimum imputed frequency to determine whether a haplotype is used. The possible values are:
- 0.01 (default)
- any floating point value 0 < frequency < 1
imputeMissing: (Optional) indicates whether or not to impute missing values. The possible values are:
- 0 (default) do not impute missing values
- 1 impute missing values
bonferroni: (Optional) indicates whether or not to use Bonferroni adjustment. The possible values are:
- 0 do not use Bonferroni adjustment
- 1 (default) use Bonferroni adjustment
fdr: (optional) Indicates whether or not use False Discovery Rate. The possible values are:
- 0 do not use False Discovery Rate
- 1 (default) use False Discovery Rate
singleValuePermutations: (Optional) indicates whether or not to perform single value permutation tests. The possible values are:
- 0 (default) do not perform single value permutation tests
- 1 perform single value permutation tests
fullScanPermutations: (Optional) indicates whether or not to perform full-scan permutation tests. The possible values are:
- 0 (default) do not perform full scan permutation tests
- 1 perform full scan permutation tests
numPermutations: If applicable, indicates the number of permutations to be used for the selected permutation tests. The possible values are:
- 0 (default)
- any positive integer value ≥ 3
outputPPQQ: (Optional) indicates whether to output data for P-P/Q-Q plots. The possible values are:
- 0 (default) do not output P-P/Q-Q data
- 1 output P-P/Q-Q data
outputHaplotypeFreq: (Optional) indicates whether or not to output haplotype frequencies. The possible values are:
- 0 (default) do not output frequencies
- 1 output frequencies
outputNegLog: (Optional) indicates whether or not to output -log(P) values. The possible values are:
- 0 (default) do not output -log(p) values
- 1 output -log(p) values

Haplotype Block Detection

Syntax

result spreadsheet object = spreadsheet object.haplotypeBlockDetection(method, [Optional Parameters])

Example

myHapBlocksSS = mySS.haplotypeBlockDetection(ghi.const.BlockDetectGabriel, lowerConfidenceBound = .75, imputeMissing = 1)

This command create a haplotype blocks spreadsheet and returns the new sheet.

This command takes one required parameter, which is the method used in the block detection. The possible value is:

ghi.const.BlockDetectGabriel minimize historical recombination (Gabriel et al.)

This command also takes the following optional keyword arguments, as follows:

upperConfidenceBound: The minimum upper bound of the D’ statistic. The possible values are:
- 0.98 (default)
- any floating point value 0.0001 < value < 0.999
lowerConfidenceBound: The minimum lower bound of the D’ statistic. The possible values are:
- 0.70 (default)
- any floating point value 0.0001 < value < 0.999
confidenceLevel: Statistical confidence level. The possible values are:
- ghi.const.Confidence90 corresponds to a confidence of 0.90
- ghi.const.Confidence95 (default) corresponds to a confidence of 0.95
- ghi.const.Confidence99 corresponds to a confidence of 0.99
minUpperConfidence: The reject criteria based on the upper bound of the D’ statistic. The possible values are:
- 0.90 (default)
- any floating point value 0.0001 < value < 0.999
minMAF: Minor allele frequency to filter SNPs from blocks. The possible values are:
- 0.05 (default)
- any floating point value 0.00001 < value < 0.999
maxMarkers: Maximum number of SNPs in a block. The possible values are:
- 30 (default)
- any integer value ≥ 1
maxBlockLength: Maximum length of a block in kilobase pairs. The possible values are:
- 160 (default)
- any integer value ≥ 1
haplotypeMethod: Indicates the method used to estimate haplotype frequencies. The possible values are:
- 1 (default) EM algorithm
- 2 CHM algorithm
maxEmIterations: Maximum number of EM iterations regardless of reaching convergence. The possible values are:
- 50 (default)
- any integer value ≥ 1
emConvergeTolerance: Desired tolerance the EM algorithm will use to determine when it reaches convergence. The possible values are:
- 0.00001 (default)
- any floating point value
frequencyThreshold: Minimum imputed frequency to determine whether a haplotype is used. The possible values are:
- 0.01 (default)
- any floating point value 0 < frequency < 1
imputeMissing: Indicates whether or not to impute missing values. The possible values are:
- 0 (default) do not impute missing values
- 1 impute missing values

Numeric Association Tests

Syntax

list of objects = spreadsheet object.numericAssociationTests([Optional Parameters])

Example

myResultList = mySS.numericAssociationTests(tTest = 1, corrTrend = 1, bonferroni = 0)

This command performs numeric association tests using the current spreadsheet and creates a list of spreadsheets containing the test results. The list of spreadsheets will be of consistent size, and if a particular spreadsheet is not created during the tests, then the returned list will contain 0 at the corresponding index.

The following is an outline of the returned list of spreadsheets by list index:

0: Association test output
1: PCA-corrected input
2: Principal component spreadsheet
3: Principal component eigenvalues
4: PCA outlier spreadsheet

All parameters are specified by keyword arguments and are optional. They are as follows:

tTest: Indicates whether or not to perform a T-Test. The possible values are:
- 0 (default): do not perform a T-Test
- 1: perform a T-Test
corrTrend: Indicates whether or not to perform the Correlation/Trend Test. The possible values are:
- 0 (default): do not perform Correlation/Trend Test
- 1: perform Correlation/Trend Test
regression: Indicates whether or not to perform regression. The possible values are:
- 0 (default): do not perform regression
- 1: perform regression
bonferroni: Indicates whether or not to use Bonferroni adjustment. The possible values are:
- 0: do not use Bonferroni adjustment
- 1 (default): use Bonferroni adjustment
fdr: Indicates whether or not use False Discovery Rate. The possible values are:
- 0: do not use False Discovery Rate
- 1 (default): use False Discovery Rate
singleValuePermutations: Indicates whether or not to perform single value permutation tests. The possible values are:
- 0 (default): do not perform single value permutation tests
- 1: perform single value permutation tests
fullScanPermutations: Indicates whether or not to perform full-scan permutation tests. The possible values are:
- 0 (default): do not perform full scan permutation tests
- 1: perform full scan permutation tests
numPermutations: (if applicable) indicates the number of permutations to be used for the selected permutation tests. The possible values are:
- 0 (default)
- any positive integer value ≥ 3
outputPPQQ: Indicates whether to output data for P-P/Q-Q plots. The possible values are:
- 0 (default): do not output P-P/Q-Q data
- 1: output P-P/Q-Q data
dataCentering: Indicates whether to perform data centering by marker, sample, or both. The possible values are:
- ghi.const.PcaUncentered (default): do not perform data centering
- ghi.const.PcaCenteredByMarker: perform data centering by marker
- ghi.const.PcaCenteredBySample: perform data centering by sample
- ghi.const.PcaCenteredByMarkerAndSample: perform data centering by marker and by sample
usePca: Indicates whether or not to use PCA. The possible values are:
- 0 (default): do not use PCA
- 1: use PCA
usePcaForDependent: If applicable, indicates whether to use a PCA corrected dependent. The possible values are:
- 0 (default): do not use a PCA corrected dependent
- 1: use a PCA corrected dependent

The following PCA parameters are available if usePca = 1 was specified:

pcaPrecomputedSheet: Indicates the use of pre-computed components. The possible values are:
- the node ID (spreadsheet number) of the pre-computed principal components spreadsheet
pcaMaxTopComponents: Indicates the maximum number of PCA components. The possible values are:
- 10 (default)
- any positive integer value
pcaOutputCorrected: Indicates whether or not to output a spreadsheet containing PCA corrected values. The possible values are:
- 0 (default): do not output corrected input data
- 1: output corrected input data
pcaOutputPcSheet: Indicates whether or not to output a principal components spreadsheet. The possible values are:
- 0: do not output a spreadsheet
- 1 (default): output a spreadsheet
pcaOutputEigenSheet: Indicates whether or not to output an eigenvalues spreadsheet. The possible values are:
- 0: do not output spreadsheet
- 1 (default): output spreadsheet
pcaRecompute: Indicates whether or not to remove outliers and recompute principal components. The possible values are:
- 0 (default): do not recompute principal components
- 1: recompute principal components

If pcaRecompute = 1 was specified, then the following PCA parameters are applicable:

pcaRecompStdDev: The number of std deviations to identify outliers. The possible values are:
- 6.0 (default)
- any floating point value
pcaRecompCount: Indicates the number of times to recompute PCs. The possible values are:
- 0 (default)
- any positive integer value
pcaRecompComponents: Indicates the number of PCs used in finding outliers. The possible values are:
- 5 (default)
- any positive integer value

Numeric Principal Components Analysis

Syntax

list of objects = spreadsheet object.numericPCA([Optional Parameters])

Example

mySSList = mySS.numericPCA(pcaMaxTopComponents = 27, pcaOutputCorrected = 1)

This command performs PCA analysis and returns a list of spreadsheet objects, the contents of which will vary depending on the options used.

0: PCA-corrected input spreadsheet
1: Principal component spreadsheet
2: Principal component eigenvalues spreadsheet
3: PCA outlier spreadsheet

All parameters are specified by keyword arguments and are optional. They are as follows:

dataCentering: Indicates whether to perform data centering by marker, sample, or both. The possible values are:
- ghi.const.PcaUncentered (default): do not perform data centering
- ghi.const.PcaCenteredByMarker: perform data centering by marker
- ghi.const.PcaCenteredBySample: perform data centering by sample
- ghi.const.PcaCenteredByMarkerAndSample: perform data centering by marker and by sample
pcaPrecomputedSheet: Indicates the use of pre-computed components. The possible values are:
- the node ID (spreadsheet number) of the pre-computed principal components spreadsheet
pcaMaxTopComponents: Indicates the maximum number of PCA components. The possible values are:
- 10 (default)
- any positive integer value
pcaOutputCorrected: Indicates whether or not to output a spreadsheet containing PCA corrected values. The possible values are:
- 0 (default): do not output corrected input data
- 1: output corrected input data
pcaOutputPcSheet: Indicates whether or not to output a principal components spreadsheet. The possible values are:
- 0: do not output a spreadsheet
- 1 (default): output a spreadsheet
pcaOutputEigenSheet: Indicates whether or not to output an eigenvalues spreadsheet. The possible values are:
- 0: do not output spreadsheet
- 1 (default): output spreadsheet
pcaRecompute: Indicates whether or not to remove outliers and recompute principal components. The possible values are:
- 0 (default): do not recompute principal components
- 1: recompute principal components

If pcaRecompute = 1 was specified, then the following parameters are applicable:

pcaRecompStdDev: The number of std deviations to identify outliers. The possible values are:
- 6.0 (default)
- any floating point value
pcaRecompCount: Indicates the number of times to recompute PCs. The possible values are:
- 0 (default)
- any positive integer value
pcaRecompComponents: Indicates the number of PCs used in finding outliers. The possible values are:
- 5 (default)
- any positive integer value

Numeric Regression

Syntax

list of objects = spreadsheet object.numericRegression([Optional Parameters])

Example

mySSList = mySS.numericRegression()

This command performs linear regression on the current spreadsheet object using the currently selected dependent column, and returns a list of objects.

The objects and their list indexes are as follows:

0 = Regression results viewer
1 = Regression results spreadsheet
2 = Detailed regression results output

All parameters are specified by keyword arguments and are optional. They are as follows:

fullModelRegressors: Indicates how full model regressors are selected. The possible values are:
- ghi.const.RegressPerColumn (default) regress once on each data column
- ghi.const.RegressMovingWindow use a moving window of regressors
- ghi.const.RegressSelected perform regression on selected covariates only

The following parameters are applicable when fullModelRegressors = 2:

movingWindowType: Indicates the type of moving window to use. The possible values are:
- ghi.const.WindowFixed (default) fixed window
- ghi.const.WindowDynamic dynamic window defined by size in genetic distance
fixedWindowSize: Indicates the size of the fixed moving window. The possible values are:
- 1 (default)
- any positive integer value
dynamicWindowBasePairs: Indicates the size of the dynamic window in bp. The possible values are:
- 10000 (default)
- any positive integer value
limitDynamicMaxCols: Indicates whether to limit the number of columns that can be spanned by the dynamic moving window. The possible values are:
- 0 (default): do not limit the moving window size
- 1: limit the moving window size
dynamicWindowMaxColumns: Indicates the maximum number of columns that can be included in the dynamic moving window. The possible values are:
- 20 (default)
- any positive integer
outputResidualSheet: Indicates if spreadsheet of residual values is output. The possible values are:
- 0 (default): do not output a residual spreadsheet
- 1: output a residual spreadsheet
useStepwise: Indicates whether or not to use stepwise regression. The possible values are:
- 0 (default): do not use stepwise regression
- 1: use stepwise regression

The following parameters are applicable when useStepwise = 1 was specified:

stepwiseCutoff: Indicates the P-Value cutoff for stepwise regression. The possible values are:
- 0.01 (default)
- a floating point value 0 ≤ stepwiseCutoff ≤ 1
stepwiseMethod: Indicates stepwise regression method. The possible values are:
- ghi.const.StepwiseBackward (default) use backward elimination
- ghi.const.StepwiseForward use forward selection
fullModelCovariates: Indicates the columns to use as full model covariates. The possible values are:
- [] (default) no columns as full model covariates
- a Python list of column numbers for covariates, ex: [2,3]
fullModelInteractions: Indicates pairs of columns to use as interactions in the full model covariates. The possible values are:
- [[]] (default) no pairs of columns for interaction terms
- a Python list of lists where each sublist contains the column numbers for the two columns that make up the interaction ex: [[2,3],[3,4]]
computeFullVsReducedModel: Indicates full only or full vs. reduced model. The possible values are:
- 0 (default): do not compute the full vs. reduced model
- 1: compute the full vs. reduced model

The following parameters are applicable if computeFullVsReducedModel = 1:

reducedModelCovariates: Indicates columns for reduced model covariates. The possible values are:
- [] (default): no reduced model covariates
- a Python list of column numbers for covariates, ex: [4,6]
reducedModelInteractions: Indicates pairs of columns for interaction terms in the reduced model covariates. The possible values are:
- [[]] (default): no pairs of columns for interaction terms
- a Python list of lists where each sublist contains the column numbers for the two columns that make up the interaction ex: [[3,4],[4,5]]
singleValuePermutations: Indicator for single value permutation testing. The possible values are:
- 0 (default): do not perform single value permutation tests
- 1: perform single value permutation tests
fullScanPermutations: Indicator for full scan permutation testing. The possible values are:
- 0 (default): do not perform full scan permutation tests
- 1: perform full scan permutation tests
numPermutations: If applicable, the number of permutations to use. The possible values are:
- 0 (default)
- any positive integer ≥ 3
outputPPQQ: Indicates whether or not to output data for P-P/Q-Q plots. The possible values are:
- 0 (default): do not output data for P-P/Q-Q plots
- 1: output data for P-P/Q-Q plots
fdr: Indicates whether or not to calculate False Discovery Rate. The possible values are:
- 0: do not use False Discovery Rate
- 1 (default): use False Discovery Rate
bonferroni: Indicates whether or not to calculate Bonferroni Adjustment . The possible values are:
- 0: do not use Bonferroni Adjustment
- 1 (default): use Bonferroni Adjustment
outputDetailedResults: Indicates that detailed output should be included if the criteria is satisfied. The possible values are:
- [] (default)
- a list with the following form:
  - index 0: value to threshold:
    - ghi.const.ThresholdMainP Main P-Value
    - ghi.const.ThresholdLogMainP Log main P-Value
    - ghi.const.ThresholdFullP Full model P-Value
    - ghi.const.ThresholdLogFullP Log full model P-Value
    - ghi.const.ThresholdRSquared R-Squared
  - index 1: type of threshold to be used:
    - ghi.const.LessThan <
    - ghi.const.LessOrEqual ≤
    - ghi.const.GreaterThan >
    - ghi.const.GreaterOrEqual ≥
  - index 2: a floating point value to be used as a threshold
  Note: Some threshold values may only be available for certain models and regressions.

Runs of Homozygosity

Syntax

list of objects = spreadsheet object.roh([Optional Parameters])

Example

mySSList = mySS.roh(minLengthSnps = 100)

This command tests for runs of homozygosity in the current spreadsheet object. It returns a list of 6 spreadsheet objects which will vary depending on the parameters used and the results of the test. If a spreadsheet is not created for any reason, the list will contain 0 at that index.

The list of returned objects and their list indexes is as follows:

0 = cluster of runs summary spreadsheet
1 = spreadsheet containing each run per sample
2 = spreadsheet containing the incidence of common runs per SNP
3 = spreadsheet containing the binary ROH run status
4 = cluster of runs spreadsheet (first column of each cluster)
5 = cluster of runs spreadsheet (every column of each cluster)

All parameters are specified by keyword arguments and are optional. They are as follows:

minLengthKBase: Specify a run based on length in kilo-base pairs. The possible values are:
- any positive real value ≥ 1.0
minSnpsKBase: used in conjunction with minLengthKBase, sets the minimum number of SNPs in a run as determined by the length in k-bp. The possible values are:
- 25 (default)
- any integer value ≥ 2
minLengthSnps: Indicates the minimum number of SNPS that constitute a run. The possible values are:
- any integer value ≥ 2
minSamples: Minimum number of samples that must contain a run to define a cluster of runs. The possible values are:
- 20 (default)
- any integer-value ≥ 1
allowHetero: Indicates whether or not heterozygotes can be included in a run. The possible values are:
- 0: do not allow runs to contain any heterozygotes
- 1 (default): allow runs to contain heterozygotes
maxHetero: Used in conjunction with allowHetero = 1, sets the maximum number of allowed heterozygotes. The possible values are:
- 1 (default)
- any integer value ≥ 1
restrictMissing: Indicates whether the number of allowed missing genotypes in a run should be restricted or not. The possible values are:
- 0: allow any number of missing genotypes
- 1 (default): allow runs to contain up to ’n’ missing genotypes
maxMissing: Used in conjunction with restrictMissing, sets the max number of allowed missing genotypes in a run. The possible values are:
- 5 (default)
- any integer value ≥ 0
restrictGap: Indicates whether the maximum distance between SNPs in a run should be restricted or not. The possible values are:
- 0: do not restrict max gap between SNPs in a run
- 1 (default): restrict max gap between SNPs in a run
maxGap: Used in conjunction with restrictGap, specifies the max gap in a run. The possible values are:
- 100 (default)
- any real-value ≥ 1.0
restrictDensity: Indicates whether the minimum density of SNPs a run should be restricted or not. The possible values are:
- 0 (default): do not restrict min density of SNPs a run
- 1: restrict min density of SNPs in a run
minDensity: Used in conjunction with restrictDensity, sets the min density of SNPs in a run. The possible values are:
- 50 (default)
- any real-value ≥ 1.0
createRunsSheet: Indicates whether to output a spreadsheet containing each homozygous run per sample. The possible values are:
- 0: do not output a spreadsheet
- 1 (default): output a spreadsheet
createIncidenceSheet: Indicates whether to output a spreadsheet containing the incidence of common runs per SNP The possible values are:
- 0: do not output a spreadsheet
- 1 (default): output a spreadsheet
createBinarySheet: Indicates whether or not to output a spreadsheet containing binary ROH run status. The possible values are:
- 0: do not output a spreadsheet
- 1 (default): output a spreadsheet
createClusterSheet: Indicates whether to output a spreadsheet containing summary information for clusters of runs if clusters were found. The possible values are:
- 0: do not output a spreadsheet
- 1 (default): output a spreadsheet
createFirstColClusterSheet: Indicates whether to output a spreadsheet containing cluster information for the first column of each cluster. The possible values are:
- 0: do not output a spreadsheet
- 1 (default): output a spreadsheet
createEveryColClusterSheet: Indicates whether to output a spreadsheet containing cluster information for every column of each cluster. The possible values are:
- 0 (default): do not output a spreadsheet
- 1: output a spreadsheet

Commands for Writing Spreadsheet Editor Scripts

dataEditModel.setData(312, 54, “A_A”, 1)

This command changes the data in a specified cell. There are four required parameters, the row number, the column number, the new value for the cell, and an indicator parameter specified below:

0: Do not check to see if the specified value is new
1 (default): Check to see if the specified value is new

0: Don’t check to see if the two labels are different
1 (default): Check to see if the two labels are different

The values of the new columns are 1 if the value of the cells in the original column equal the value corresponding to the value assigned to the boolean column and 0 otherwise. For example, if a categorical column “Level” had three values “Low”, “Medium”, and “High”, three binary columns would be created “Level=Low?”, “Level=Medium?”, and “Level=High?”. Every instance of “Low” in the “Level” column would be indicated with a 1 in the column “Level=Low?”. All other values would be indicated with a 0. The other binary columns would be created in the same manner.

The allowable values for the second parameter are as follows:

0 (default): Expand missing data into a separate column with a value of 1 for missing data rows.
1: Missing data is NOT expanded into a separate column–instead, missing-value indicators (“?”) are placed in the missing data rows for every output column.

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