Differential gene expression using DESeq2 for the
After individual samples are processed with the
rna-star route, manually define proper group names in the
samples.groups.csv sample sheet.
rna-star-groups-dge route from the same directory as
rna-star-groups-dge route will create a
DGE-DESeq2-* directory with the results. The name will contain the strand (determined automatically) and the number of samples in the sample sheet. The sample sheet can be modified to exclude problematic samples or change groupings for alternate analysis.
counts.raw.csv: Table of raw counts.
counts.*.csv: Table of normalized counts, FPKMs, and TPMs that can be used to check the expression levels of specific genes across samples.
counts.*.xlsx: Table of normalized counts, FPKMs, and TPMs in Excel format to avoid potential auto-conversion of gene names.
counts.vst.csv: Table of counts after variance stabilizing transformation (VST) for clustering samples or other machine learning applications. These are log2-transformed and normalized with respect to library size. The point of VST is to remove the dependence of the variance on the mean.
plot.pca.png: PCA plot that shows the samples based on their first two principal components. Useful for visualizing the overall effect of experimental covariates and batch effects.
dge.*: Differential gene expression statistics.
heatmaps: Heatmaps based on differentially expressed genes using multiple cutoffs.
volcano-plots: Volcano plots of differential expression results.
gene-set-enrichment: Pathways analysis.
input.groups.csv: Input sample sheet.
input.counts.txt: Input gene-sample matrix of raw counts.
deseq2.dds.RData: DESeq2 object (dds) that can be loaded and modified in R if more complex analysis is needed.
deseq2.vsd.RData: VST-transformed DESeq2 object (vsd) that can be loaded and modified in R if more complex analysis is needed.