Immune-deconvolution, gene set enrichment analysis, differential expression analysis, gene ontology analysis
The MCP-counter tool (v1.2.0)In order to evaluate biological pathways at the single-sample level and their correlation with the prognostic score as a continuous variable, we applied GSVA (v1.36.3)The differential gene expression analysis between good and poor prognosis groups was performed using DEseq2 on gene-level abundance estimates from Salmon (functions “DESeqDataSetFromTximport” and “DESeq”). These analyses were performed on the NeoALTTO and CALGB 40601 data separately. Genes with low expression levels (sum of the reads across all samples < 10) were removed before the analysis. In order to identify genes and pathways/biological processes associated to prognosis according to our model, prognostic groups in the whole population (Gene set enrichment analysis comparing the prognostic groups was performed applying the “fgsea” R package (v1.14.0)Gene ontology analysis on the “biological process” domain was performed on all the identified differentially expressed genes with FDR < 0.05 and log