Ensuring Reproducible Transcriptomic Analysis with DESeq2 and tximeta
Michael Love (The University of North Carolina at Chapel Hill)
To extend DESeq2 functions to develop interfaces with Bioconductor’s rich experiment and annotation data, including single-cell datasets and genomic annotations, all leveraging tximeta’s metadata functionality for computational reproducibility.
A basic task in the analysis of count data from RNA-seq is the detection of differentially expressed genes. The count data are presented as a table which reports, for each sample, the number of sequence fragments that have been assigned to each gene. Analogous data also arise for other assay types, including comparative ChIP-Seq, HiC, shRNA screening, and mass spectrometry. An important analysis question is the quantification and statistical inference of systematic changes between conditions, as compared to within-condition variability. The package DESeq2 provides methods to test for differential expression by use of negative binomial generalized linear models; the estimates of dispersion and logarithmic fold changes incorporate data-driven prior distributions.