Clinical investigations have highlighted cell infiltrations in TME as pivotal contributors to the complex anti-tumor immunity in malignancies. TME-cell deconvolution is the major technological hurdle, and the deconvolution algorithms vary in their merits and pitfalls (10 (link), 11 (link)). IOBR integrates eight open-source deconvolution methodologies, namely, CIBERSORT (12 (link)), ESTIMATE (13 (link)), quanTIseq (14 (link)), TIMER (15 (link)), IPS (16 (link)), MCPCounter (17 (link)), xCell (18 (link)), and EPIC (19 (link)).
CIBERSORT is the most well-recognized method for detecting 22 immune cells in TME, allowing large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets with promising accuracy (12 (link)). Notably, through the adoption of the linear vector regression principle of CIBERSORT, IOBR allows users to construct a self-defined signature. The availability of its input file was extended to cell-subsets derived from single-cell sequencing results. ESTEMATE dissects non-malignant contextures, including stromal and immune signatures, to determine tumor purity (13 (link)). The quanTIseq method enumerates 10 immune cell subsets from bulk RNAseq data (14 (link)). TIMER quantifies the abundance of six tumor-infiltrating immune compartments and provides six major analytic modules for analyzing the immune infiltration with other cancer molecular profiles (15 (link)). IPS estimates 28 TIL subpopulations, including effector and memory T cells and immunosuppressive cells (16 (link)). MCP-counter conducts robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data (17 (link)). xCell provides a comprehensive view of 64 immune cells from RNA-seq data and other cell subsets in bulk tumor tissue (18 (link)). EPIC decodes the proportion of immune and cancer cells from the expression of genes and compares it with the gene expression profiles from specific cells to predict the cell subpopulation landscape (19 (link)). In a nutshell, IOBR R package and web-based interface enable the convenient integration and visualization of the above-mentioned deconvolution results and a flexible selection of particular methodologies of interest.
CIBERSORT is the most well-recognized method for detecting 22 immune cells in TME, allowing large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets with promising accuracy (12 (link)). Notably, through the adoption of the linear vector regression principle of CIBERSORT, IOBR allows users to construct a self-defined signature. The availability of its input file was extended to cell-subsets derived from single-cell sequencing results. ESTEMATE dissects non-malignant contextures, including stromal and immune signatures, to determine tumor purity (13 (link)). The quanTIseq method enumerates 10 immune cell subsets from bulk RNAseq data (14 (link)). TIMER quantifies the abundance of six tumor-infiltrating immune compartments and provides six major analytic modules for analyzing the immune infiltration with other cancer molecular profiles (15 (link)). IPS estimates 28 TIL subpopulations, including effector and memory T cells and immunosuppressive cells (16 (link)). MCP-counter conducts robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data (17 (link)). xCell provides a comprehensive view of 64 immune cells from RNA-seq data and other cell subsets in bulk tumor tissue (18 (link)). EPIC decodes the proportion of immune and cancer cells from the expression of genes and compares it with the gene expression profiles from specific cells to predict the cell subpopulation landscape (19 (link)). In a nutshell, IOBR R package and web-based interface enable the convenient integration and visualization of the above-mentioned deconvolution results and a flexible selection of particular methodologies of interest.
Free full text: Click here
