Independent component analysis recovers consistent regulatory signals from disparate datasets
Fig 1
Characterization of iModulons derived from five independent gene expression datasets.
(A) Schematic illustration of the workflow applied to each data set. (B) Descriptions of the three classes of characterized iModulons. The first column contains histograms illustrating the distribution of gene weightings in each of three independent components (ICs) from the RNAseq-1 dataset. Genes outside of a threshold (in red) belong to an “iModulon”. The second column illustrates the biological interpretation of the iModulon types. iModulons are characterized by comparing their genes with known regulons, ontological annotations, and genotypes. The third column displays the ICA-computed activity levels for the selected ICs across all 278 conditions in the RNAseq-1 dataset. (C) Bar chart describing the four types of iModulons computed from each of the five gene expression datasets. (D) Comparison of three Functional iModulons, each derived from decomposition of a different dataset. Asterisks indicate genes annotated with the GO term “lipopolysaccharide core region biosynthetic process”.