Tag: pheatmap

1. Sharma VK. Adaptive significance of circadian clocks. Chronobiol Int. 2003;20(6):901–19. PubMed  Google Scholar  2. Paranjpe DA, Sharma VK. Evolution of temporal order in living organisms. J Circadian Rhythms. 2005;3(1):7. PubMed  PubMed Central  Google Scholar  3. Yerushalmi S, Green RM. Evidence for the adaptive significance of circadian rhythms. Ecol Lett….

I’m using deseq2 for DEA but when I create a heatmap with only DEGs, it looks very strange: I’m not sure whether there are only overexpressed genes or whether the dataset is not normalized properly. I probably made a mistake somewhere in my coding but I don’t know where to…

Introduction Kidney cancer is one of the most commonly diagnosed tumors around the globe.1 According to the statistics from the World Health Organization, annually, there are more than 140,000 RCC-related deaths.2 ccRCC is the most typical subtype of kidney cancer and contributes to the majority of kidney cancer-related deaths.3,4 Until…

Introduction Tuberculosis (TB) is considered to be one of the top ten causes of death in the world, about a quarter of the world’s population is infected with M. tuberculosis.1 The World Health Organization (WHO) divides tuberculosis into pulmonary tuberculosis (PTB) and extra-pulmonary tuberculosis (EPTB). Although breakthroughs have been made…

I am making a heatmap using RNA-Seq data in R. The heatmap shows gene expression values (RPKM) in different brain regions. I have the following code: library(tidyverse) library(pheatmap) library(matrixStats) read_csv(“prenatal_heatmap_data.csv”) -> all_data all_data %>% column_to_rownames(“Brain Region”) -> heatmap_data heatmap_data %>% pheatmap() Which generates the following heatmap: I want to do…

Hello, I am using DESeq2 for analysis of RNAseq data. I would like to ask you about the design in the DESEq2 formula. I have tissue from animals treated with a chemical and my animal model is a colorectal cancer model. My variables are gender (male or female), treatment (treated…

Hi, all! First post, so apologies for any flaws with post structure. I am attempting to make a basic heatmap that shows the log fold change of differentially expressed genes, as identified by DESeq2. See below the code I am using for DESeq2: ##Load DESeq2 source(“https://bioconductor.org/biocLite.R”) biocLite(“DESeq2”) biocLite(“stringi”) biocLite(“MASS”) install.packages(“survival”)…

DOI: 10.18129/B9.bioc.GEOexplorer     This is the development version of GEOexplorer; to use it, please install the devel version of Bioconductor. GEOexplorer: an R/Bioconductor package for gene expression analysis and visualisation Bioconductor version: Development (3.14) GEOexplorer is a Shiny app that enables exploratory data analysis and differential gene expression of…

DOI: 10.18129/B9.bioc.conclus     ScRNA-seq Workflow CONCLUS – From CONsensus CLUSters To A Meaningful CONCLUSion Bioconductor version: Release (3.13) CONCLUS is a tool for robust clustering and positive marker features selection of single-cell RNA-seq (sc-RNA-seq) datasets. It takes advantage of a consensus clustering approach that greatly simplify sc-RNA-seq data analysis…

Bit of an R newbie here. I’m trying to generate a figure to see how RNA-seq samples are grouping via hierarchical clustering. Using this code rld<-vst(dds, blind=TRUE) rld_mat<- assay(rld) rld_cor<-cor(rld_mat) head(rld_cor) pheatmap(rld_cor,annotation = meta) heat.colors<-brewer.pal(9, “Blues”) annotdf<-data.frame(row.names = rownames(rld_cor)) pheatmap(rld_cor, annotation=meta, color=heat.colors, annotation_colors = ColorCode, border_color = NA, fontsize =…

Contents Serial circulating tumor DNA (ctDNA) monitoring is emerging as a non-invasive strategy to predict and monitor immune checkpoint blockade (ICB) therapeutic efficacy across cancer types. Yet, limited data exist to show the relationship between ctDNA dynamics and tumor genome and immune microenvironment in patients receiving ICB. Here, we present…

Hi there, Essentially, my experimental design is control vs treatment. Cells were sorted based on fluorescence, so there are 4 different “colors” of treated cells, i.e. red, green, green+red, and blue+green+red. I am interested in how the colors differ from one another. And, I have duplicates for all colors and…

DOI: 10.18129/B9.bioc.DESeq2     This package is for version 3.10 of Bioconductor; for the stable, up-to-date release version, see DESeq2. Differential gene expression analysis based on the negative binomial distribution Bioconductor version: 3.10 Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on…

DOI: 10.18129/B9.bioc.Single.mTEC.Transcriptomes     This package is for version 3.8 of Bioconductor; for the stable, up-to-date release version, see Single.mTEC.Transcriptomes. Single Cell Transcriptome Data and Analysis of Mouse mTEC cells Bioconductor version: 3.8 This data package contains the code used to analyse the single-cell RNA-seq and the bulk ATAC-seq data…

R Programming – how to make a simple heat map 5 Hi can anyone guide me how to make a simple heat map in R? Heatmap R • 264 views There is github.com/XiaoLuo-boy/ggheatmap which is fully ggplot in case you feel more comfortable with it rather than the suggested pheatmap/ComplexHeatmap…

R Programming 4 Hi can anyone guide me how to make a simple heat map in R? in Heatmap R • 201 views There is github.com/XiaoLuo-boy/ggheatmap which is fully ggplot in case you feel more comfortable with it rather than the suggested pheatmap/ComplexHeatmap packages and want to have a consistent…