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Taking advantage of analysis techniques developed by Anders and Huber 2010, the DESeq tool is designed to estimate variance-mean dependence in count data and test for differential expression between types using a model based on the negative binomial distribution. DESeq in SVS not only calculates the mean values from your genes or transcripts for each group, but also detects the squared coefficient of variation (SCV). This approach helps to recognize those transcripts with the highest consistency by providing p-values and fold change between each study group while filtering out erratic variations found within certain transcripts.
Various aspects of the RNA-Seq sample preparation and sequencing process can result in extremely high variance of read counts within a sample and between a sample, even when each sample is sequenced with the same target depth. While DESeq has a built in normalization method, you can also normalize your data as outlined by Bullard et al. 2010. This normalized data can then be used in PCA analysis to see if your biological factors are driving the primary principle components or to run association analysis with some of our many supported statistical tests such as T-Test and regression with optional covariates.
Advanced visualization can be used to interpret the analysis of your RNA-seq differential expression.
Getting to a standard volcano plot showing p-values versus fold-change is a cinch. And you can interactively set thresholds on the data and see what genes show statistical significance and large-magnitude count differences.
Your top genes in their normalized form are outputted from DESeq and can be hierarchically clustered and plotted in a heatmap. The dendogram on both the sample and gene axes provide clear feedback that the undirected clustering followed the biological grouping and the statistic test provided genes with stark differences in expression between groups.
And, as with all genomic data in SNP & Variation Suite, plotting any of the many statistical or summary outputs in our built-in genome browser provides you with the genomic context to interpret key genes. Our live-streaming annotation repository as well as custom annotations can help decipher the significance of any results in the context of your research.
The core architecture of SVS has been designed to efficiently handle datasets of virtually any size and type on a desktop computer. SVS natively supports over 70 different file formats and over 40 export formats to streamline data management, ensuring you spend most of your time on the more important aspects of analysis.
Real-time spreadsheet manipulation, data editing, and enrichment help eliminate the hassles of working with large-scale, complex data. Easily combine multiple sample sets and data of different types, from different arrays, or even platforms. Further, an integrated spreadsheet editor facilitates data editing and transformations on a grand scale.
Genomic Build, Marker Map, and Annotation Management
SVS provides a robust set of tools for working with and managing genomic information. Easily switch among a wide-variety of supported species and genomic builds and apply genetic marker maps to ensure all analyses and visualizations are accurate based on correct genomic coordinates. Further, genomic annotations can be used to enrich analyses with visualization alongside data for greater context in the genome browser. SVS provides real-time network access to an expanding list of genomic annotations. You can also use your own custom annotations from private sources or public databases, such as UCSC, RefSeq, and Ensemble.
Python is a clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java. SVS gives you fully programmatic access to most SVS functionality via a Python scripting interface enabling you to automate workflows, interoperate with other programs, and develop more robust data management and manipulation routines. Also included is the mature statistical and numeric packages of NumPy and SciPy giving you a broad base of standardized test statistics to add your own methods as well as the 2D plotting library, matplotlib, for generating a near limitless number of publication quality plots and other visualizations. More about Python Scripting in SVS »