Fig. 5

Patient subgroup-specific signatures can be used to predict potential drug targets. a Schematic workflow of the drug prediction analysis. Drug signatures were collected using the platforms iLINCS and CLUE. Signatures were selected by highest counteracting ΔNES score and combined with signatures of drugs under investigation from the literature. b Visualization of genes targeted by drugs approved or undergoing trial for the treatment of COVID-19 patients included in the whole blood co-expression network. Numbers of such genes from each module are designated on the right of the panel. Genes are represented as hexagons and colored by the expression fold change between COVID-19 patient severity group (G1–G5) and the control group (G6) (upregulated: red, downregulated: blue, not regulated: grey). c Drug predictions based on ΔNES score of drug signatures in regard to diseased patient group-specific gene expression patterns (G1–5 vs G6). Signatures were clustered by k-means clustering. A high ΔNES score accounts for drug signatures which counteract the gene expression of the patient group they are compared to. Drug signatures with a negative ΔNES score induce a gene expression pattern similar to the input. The number of signatures within a cluster determines its size. d Display of selected drug signatures from k-means cluster 5 from c showing the highest ΔNES score in the most severe COVID-19 patient group G1 and the least effect in patient group G4. e Visualization of recurring target genes in the G1 vs G6 comparison of cluster 5 signatures and their frequency mapped onto the CoCena² network