Supplemental Material for Cary, Podshivalova, and Kenyon, 2020
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
File S1. Partitioned S matrix (genes that belong to each of the 209 DEXICA modules). The two hemi-modules are designated by opposite signs: 0 indicates that a gene does not belong to a given module; 1 indicates that a gene belongs to the “a” hemi-module of a given module; -1 indicates that a gene belongs to the “b” hemi-module of a given module.
File S2. Module annotation summaries. For each module, the perturbation (from the 716 perturbations in the microarray compendium) in which that module explains the greatest amount of variance (i.e. the perturbation that activates that module the most strongly) is indicated. “None” indicates that a module does not explain more than 5% of the variance in any perturbation (SVE is between -0.05 and 0.05). The top two GO terms from each GO category that are enriched in each hemi-module are also indicated. In cases where enriched GO terms and the perturbation show clear semantic agreement, this is indicated in the first column “Module likely represents”.
File S3. Finding experiments that activate similar modules. Module activity in all possible pairs of perturbations (contrasts) in the microarray compendium was calculated. The pairs are ranked by the absolute value of the Pearson correlation coefficient calculated based on activity of each module (SVE).
File S4. Module-weighted GO terms. Weighted associations between each gene and each GO term are shown. Numbers represent z-scores of gene weights in each GO category. GO categories used for this analysis contain as least 15 genes.
Supplemental information file contains supplemental discussion, supplemental methods, 8 supplemental figures and supplemental references