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Ensuring Reproducible Transcriptomic Analysis with DESeq2 and tximeta
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.