pyPAGE: A framework for Addressing biases in gene-set enrichment analysis—A case study on Alzheimer’s disease
Fig 4
Cell type and regional specific differential activity patterns of transcriptional factors in AD.
(A) Cells from the analyzed ROSMAP dataset represented on a force-directed graph embedding. The clusters are colored according to cell-types: excitatory neurons (Ex), inhibitory neurons (In), astrocytes (Ast), oligodendrocytes (Oli), oligodendrocyte progenitor cells (Opc), microglia (Mic), endothelial cells (End), pericytes (Per). (B) The same cell-type clusters colored according to differential activity of SOX10 between cells from non-AD and AD samples estimated using pyPAGE. The magnitude of the regulation pattern was calculated as scaled conditional mutual information multiplied by the factor representing the direction of deregulation. (C) Summary of the cell-type specific deregulation patterns of the TFs identified in the analysis of the bulk data. Heatmap cells with significant associations (p-value<0.05) are framed. The regulation is calculated as the normalized conditional mutual information of the relationship multiplied by the sign of the log fold change. (D) Heatmap representations of concordant expression changes in expression of TF target genes in inhibitory neurons and oligodendrocytes. Here 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. (E) This heatmap summarizes deregulation patterns in various cortical layers of TFs that we previously identified in the analysis of bulk data. Heatmap cells with significant associations (p-value<0.05) are framed. Regulation pattern is estimated as normalized conditional mutual information of the association multiplied by the sign of log fold change.