Introduction In this paper, by performing a pan-cancer analysis of single myeloid cells from 210 patients across 15 human cancer
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# set a color for each cancer type library(RColorBrewer) cancer_color <- data.frame(“type”=sort(unique(readRDS(“CCLE_heterogeneity_Rfiles/CCLE_metadata.RDS”) $cancer_type_trunc)), “color”= c(brewer.pal(12, “Set3”)[c(1:6,8,7,10:11)], “maroon”,”gray93″, “yellow2”, “goldenrod1”, “slateblue2”),stringsAsFactors
Introduction In this paper, single-cell 10X RNA sequencing was performed on a mixture of a variety of known cell lines
Introduction Do not require additional dependencies, follow this comparison table to merge to switch between ensemble ID and gene symbol
Introduction In this paper, single-cell 10X RNA sequencing was performed on a mixture of a variety of known cell lines
Introduction Show that different factors (age, smoking, treatment) have an impact on clonal hematopoiesis Code explanation Load the required tool
Introduction Show the mutation signal of a sample Code explanation Load the package, read in the library(MutationalPatterns) nhdp <- readRDS(‘mousesignatures_norm.rds’
Introduction Use GenVisR package to draw heat map of driver mutation Code explanation Load the package, read in the data
Introduction View local motifs through sequence logo Code explanation Load the package, read in the data library(MutationalPatterns) library(BSgenome.Mmusculus.UCSC.mm10) library(ggseqlogo) library(gridExtra)
Introduction Through mouse models of carcinogenesis in different environments, it is found that tumors caused by different chemical exposures have