Multi Omics comparison RNA seq : Condition specific gene signature

Question

The authors of an article are comparing different cell type and the organ type and they show the ontology enrichment of the RNA-seq:

Cell-type-specific and organ-specific marker genes were identified using a two-step procedure. First, we performed pairwise differential expression analysis for each of the three type of structural cells, comparing them across organs. Second, based on the resulting pairwise comparisons, we identified for each organ those genes that were upregulated in each cell type compared to at least five other organs. Pairwise comparisons between organs were performed using the limma package in R, separately for each of the three structural cell types. Significantly differential genes were selected based on statistical significance (adjusted P < 0.05), average expression (log2(CPM) > −1) and sequencing coverage (median number of reads greater than 10 in the group with stronger signal). On the basis of these pairwise comparisons, we counted the total number of times each gene was upregulated in a specific organ compared to all other organs. Genes that were upregulated in comparison to five or more other organs were selected as marker genes of the corresponding organ.

Now how is this part done ? "Genes that were upregulated in comparison to five or more other organs were selected as marker genes of the corresponding organ.*"

The figure for this results is this:

Answer 1

From their methods section:

On the basis of these lists of marker genes and receptor–ligand pairs, we inferred potential cell–cell interactions for all pairs of one structural cell type and one haematopoietic immune cell type, quantifying the enrichment for known receptor–ligand pairs among all pairs of marker genes between the structural cell type and the haematopoietic cell type (Fig. 2a). First, we counted all pairs of marker genes for each pair of structural and immune cell types. Second, we calculated the fraction of these gene pairs that were annotated as receptor–ligand pairs. Third, we tested whether this fraction was greater than the fraction of annotated receptor–ligand pairs across all pairs of genes. Fisher’s exact test was used to obtain P values and odds ratios, as implemented by the function ‘fisher.test’ in R. P values were adjusted for multiple testing using the Benjamini–Hochberg method, as implemented the function ‘p.adjust’ in R. Finally, significantly enriched pairs (adjusted P < 0.05) of structural and immune cell types were connected by edges to generate a graph of cell–cell interactions.

In addition to testing for significant enrichment (as described in the previous paragraph), we calculated aggregated receptor and ligand gene signatures (Fig. 2c) based on a manually curated list of immune-related receptors and ligands (Supplementary Table 4). These gene signatures were derived by normalizing expression values for each gene and averaging across all genes in a given set of biologically related receptors and ligands.

The actual values plotted then seem to be from EnrichR, since they mention that elsewhere in the methods.