Characteristics of host proteins interacting with known B. mal/ei virulence factors | First, we applied functional enrichment analyses based on Gene Ontology (GO) annotation data [23] to assess the characteristics of the human proteins targeted by the nine virulence factors. |
Gene set functional enrichment analyses | Gene set functional enrichment analyses |
Gene set functional enrichment analyses | We performed GO and KEGG enrichment analyses in R using the Bioconductor packages Bio-Mart and KEGGgraph, respectively [43, 44]. |
Gene set functional enrichment analyses | For the KEGG enrichment analysis , as the universe of human proteins, we used the human proteins available in KEGGgraph that participated in at least one KEGG pathway [44]. |
Using multiple host-pathogen interaction networks to predict the role of pathogen proteins | However, detecting specific mechanisms of action for each pathogen protein based on enrichment analysis of large-scale Y2H protein interaction data is not trivial. |
Supporting Information | Genes utilised in the gene set enrichment analysis to identify gene sets associated with signalling entropy’s prognostic power in breast and lung cancer. |
Supporting Information | Gene set enrichment analysis results displaying the top 10 most significant enriched gene sets associated with signalling entropy’s prognostic power in breast and lung cancer. |
Supporting Information | Tables display results for the gene set enrichment analysis performed on gene lists identified in lung and breast cancer separately, both With and Without the intersection of the |
The prognostic impact of signalling entropy is associated with genes involved in cancer stem cells and treatment resistance | We performed a gene set enrichment analysis , using a Fisher’s Exact test, comparing each of these gene lists separately against the Molecular Signatures Database [50] (S6 Table shows the top 10 enriched gene sets for both gene lists). |
The prognostic impact of signalling entropy is associated with genes involved in cancer stem cells and treatment resistance | We note that gene set enrichment analysis performed on the genes comprising the SE scores gave broadly similar results (S7 Table). |
Cryptic 3’SS selection is limited to tumors with mutations in HEAT repeat hotspots | To characterize the roles of the genes affected by cryptic 3’SS usage, we performed a gene set enrichment analysis for the 912 genes that contained the 619 proximal and 417 distal cryptic 3’SSs used significantly more often in the SF3BI mutant samples (SS File). |
Cryptic 3’SS selection is limited to tumors with mutations in HEAT repeat hotspots | These results may reflect the fact that we are more likely to identify cryptic 3’SSs in genes that are highly expressed which may bias such a gene set enrichment analysis . |
Cryptic 3’SSs are used infrequently relative to canonical 3’SSs | To investigate the potential role of NMD, we identified differentially expressed genes between the SF3BI mutant and wild-type samples in a joint analysis of all three cancers and performed a gene set enrichment analysis . |
Differential gene expression | Gene set enrichment analysis was performed using GSEA [21]. |
Gene set enrichment for genes with cryptic 3’SS usage | We performed a gene set enrichment analysis using GSEA [21] for the genes that contained cryptic 3’SSs by combining the genes that contained the 619 proximal (S3 File) and the 417 dis |
Biomarker discovery through functional and network analysis | Functional enrichment analysis of the most informative genes reveals a rich repertoire of biological processes where their differential enrichment is discriminative of each specific class (Fig. |
Selection of most informative genes and functional enrichment analysis | Selection of most informative genes and functional enrichment analysis |
Selection of most informative genes and functional enrichment analysis | For functional enrichment analysis , we use all selected genes that optimize the classifier performance. |