pyPAGE: A framework for Addressing biases in gene-set enrichment analysis—A case study on Alzheimer’s disease
Fig 3
Transcription factors associated with gene expression changes in Alzheimer’s Disease.
(A) Regulons of TFs differentially expressed between AD and non-AD samples discovered by pyPAGE. In this representation the rows correspond to TFs and columns to gene bins of equal size ordered by differential expression, the cells are colored according to the enrichment of genes from regulons in a corresponding bin. The leftmost column of the heatmap depicts the differential expression of the regulator itself. (B) The barplot representing Pearson correlations between the expression of TFs and of their regulons, as measured by median TPM of its members. Asterix indicated significant correlation (p-value<0.05). (C) The scatter plot demonstrating association between the expression of the well-known AD regulator KDM5A with the expression of its regulon. (D) The association between the expression of another AD regulator ATF4 with the expression of its regulon. (E) Biological roles of the identified TFs inferred based on the functions of the genes controlled by these TFs. In these heatmap colored cells correspond to TFs whose regulons are significantly (p-value<0.05) enriched with genes from a corresponding biological pathway based on PantherDB. (F) Plot showcasing how robust are predictions of three different methods to subsampling of expression data. To measure consistency of the predictions we computed intersection over union (IoU) of the method’s output with and without subsampling of genes.