DMG single cell regulatory networks

Context-specific regulatory networks—consisting of bivariate transcriptional regulatory interactions between a set of predefined putative transcriptional regulators and potential transcriptional targets—were reverse engineered using the ARACNe-AP algorithm (publicly available at github.com/califano-lab/ARACNe-AP). Candidate transcriptional regulators (available at github.com/califano-lab/tf_cotf_signalling_list) included 1,813 transcription factors (genes annotated in the Gene Ontology Molecular Function database (GO) as GO:0003700—‘DNA binding transcription factor activity’, or as GO: :0003677—‘DNA binding’ and GO:0030528—‘Transcription regulator activity’, or as GO:0003677 and GO:0045449—‘Regulation of transcription’), 969 transcriptional co-factors (a manually curated list, not overlapping with the transcription factor list, built upon genes annotated as GO:0003712—‘transcription cofactor activity’ or GO:0030528 or GO:0045449) or 3,370 signaling-pathway-related genes (annotated in the GO Biological Process database as GO:0007165—‘signal transduction’ and in the GO Cellular Component database as GO:0005622—‘intracellular’ or GO:0005886—‘plasma membrane’). Standard parameters were set including zero DPI (Data Processing Inequality) tolerance and MI (Mutual Information) p-value threshold of 10-8, computed by permuting the original dataset as a null model.For tumor compartment-specific analyses, DMG tumor networks were generated using the log2(CPM/10+1) normalized gene expression counts for tumor cells for each of the six patients individually. This resulted in 1,440 regulators, 5,118 targets and 72,000 interactions for BCH1126; 2,469 regulators, 7,088 targets and 123,450 interactions for BCH836; 2,045 regulators, 6,343 targets and 102,250 interactions for BCH869; 1,117 regulators, 4,558 targets and 55,850 interactions for MUV1; 714 regulators, 4,505 targets and 35,700 interactions for MUV10; and 2,281 regulators, 7,007 targets and 114,050 interactions for MUV5.