Introduction Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein–protein
Introduction NicheNet, a method that predicts ligand–target links between interacting cells by combining their expression data with prior knowledge on
Introduction Whether it is single-cell RNA sequencing or bulk RNA sequencing, we need to perform differential gene expression analysis and
Introduction By performing a pan-cancer analysis of single myeloid cells , authors found that some mutations were correlated with the
Introduction In this paper, by performing a pan-cancer analysis of single myeloid cells from 210 patients across 15 human cancer
# 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 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’