TCGAimmunosurv: An R package to identify genes associated with patient survival and immune cell state transitions using TCGA and single-cell RNA-seq data.

Cancer is a disease rooted in genomic alterations and has long stood as one of the most formidable foes for humanity since its inception. A patient's survival often hinges on the specific genomic mutations present and the corresponding immune system response. As each genomic alteration has its deleterious damage, identification of potential drivers of cancer progression may risk yielding false positives without a mutation-specific approach. To advance our understanding of tumor biology, there is a need for a systematic computational framework that combines both bulk RNA-seq and single-cell RNA-seq data analyses. In this work, we developed a pan-cancer R package called TCGAimmunosurv that integrates bulk RNA-Seq data from The Cancer Genome Atlas (TCGA) with single-cell RNA-Seq data to identify the genes to investigate mutation-specific immune dynamics. TCGAimmunosurv allows mutation-specific analysis by allowing users to select the genes of interest. It then identifies survival-associated genes from the mutation-specific samples obtained from TCGA. Then, it analyzes the user-given single-cell datasets and performs immune-subtype-specific pseudotime trajectory analysis. Finally, it seeks the gene(s) with strong expression patterns along pseudotime trajectories that are also associated with patient survivability and, thereby, bridges bulk and single-cell analyses for a comprehensive insight into the cancer driver genes.