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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
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 »