SNP & Variation Suite offers advanced analysis tools designed to perform differential expression workflows for RNA expression profiling experiments. Regardless of the upstream secondary analysis tool used to align and quantify reads into weighted counts, SVS provides all the data normalization, differential expression, and visualization techniques needed to be able to conduct RNA sequencing analysis quickly and easily, giving you everything you might expect from expression micro-arrays.
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.
Normalization and Log Transformation
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 output from DESeq and can be hierarchically clustered and plotted in a heatmap. The dendrogram 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 with GenomeBrowse 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. Learn more about GenomeBrowse as a stand-alone tool »
Case Study - Julia Pinsonneault, The Ohio State University
"If I didn't have SVS, I'm not quite sure what I'd do."
RNA-Seq using Public Data
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