Tag: pheatmap

Genomic hypomethylation in cell-free DNA predicts responses to checkpoint blockade in lung and breast cancer

Lung cancer ICB cohort Advanced non-small cell lung carcinoma patients who were treated with anti-PD-1/PD-L1 monotherapy at Samsung Medical Center, Seoul, Republic of Korea were enrolled for this study. The present study has been reviewed and approved by the Institutional Review Board (IRB) of the Samsung Medical Center (IRB no….

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Single-cell RNA-seq workflow

In this tutorial we walk through a typical single-cell RNA-seq analysis using Bioconductor packages. We will try to cover data from different protocols, but some of the EDA/QC steps will be focused on the 10X Genomics Chromium protocol. We start from the output of the Cell Ranger preprocessing software. This…

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Bioconductor – RTN

DOI: 10.18129/B9.bioc.RTN     This package is for version 3.12 of Bioconductor; for the stable, up-to-date release version, see RTN. RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons Bioconductor version: 3.12 A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target…

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removeBatchEffect with non-linear model fit

removeBatchEffect with non-linear model fit 0 @2289c15f Last seen 6 hours ago Germany Hello, I am attempting to use limma’s removeBatchEffect for visualization purposes (heatmat & PCA) while fitting non-linear models (splines) to my expression data in DESeq2. Given that my design is balanced, would this approach work within the…

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Interaction terms in DESeq2

Hi, I am hoping this isn’t a stupid question as I am really lost here. I have extensively read the manual and other forum posts but am struggling to find a solution. I am using DESeq2 to analyse my data set but running into problems with an interaction term in…

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Comparing 3 Data Sets using DeSeq and Heatmaps

Hi all, I am new to bioinformatics analysis, so I’d appreciate if someone could check my code for the goal I am trying to achieve. I have 3 samples – Wild Type (WT) FoxP3-TCF-HEB (I have 3 replicates of this) TCFKO I have defined these in the sample information csv…

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pheatmap lengends are being cutoff

I’m trying to removed annotations for the heat map legend. Originally this what the heatmap looks like using the following code:pheatmap(M.adj, annotation_col = conds, #dropData set border_color = NA, filename = “QC/QCheatmap.pdf”, color = colorRampPalette(rev(brewer.pal(n=11, name=”RdBu”)))(100), cluster_cols = TRUE, show_rownames = FALSE, main=paste(“Gene Expression (VST) of Top 1000 Variable Genes”)…

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Elevated stress response marks deeply quiescent reserve cells of gastric chief cells

Generation of inducible H2b-GFP knock-in mice Generation of inducible H2b-GFP knock-in mice was performed by using CRISPR/Cas943. In brief, a mixture containing a sgRosa26-1 crRNA43 (8.7 ng/μl, Fasmac, Japan), a tracrRNA (14.3 ng/μl, Fasmac, Japan), a single strand oligo donor nucleotide (ssODN) composed of 5′ arm, adenovirus splicing acceptor, SV40 pA, TRE3G…

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Predicting missing values splines DESeq2

Hello, I am fitting splines in DESeq2 like so: dds <- DESeqDataSetFromMatrix(countData = counts, colData = coldata, design = ~ ns(age_scaled, df = 3)) Plotting later using the code Mike Love posted elsewhere: dat <- plotCounts(dds, gene, intgroup = c(“age”, “sex”, “genotype”), returnData = TRUE) %>% mutate(logmu = design_mat %*%…

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Bioconductor – DESeq2 (development version)

DOI: 10.18129/B9.bioc.DESeq2   This is the development version of DESeq2; for the stable release version, see DESeq2. Differential gene expression analysis based on the negative binomial distribution Bioconductor version: Development (3.19) Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model…

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Myristoylated, alanine-rich C-kinase substrate (MARCKS) regulates toll-like receptor 4 signaling in macrophages

Cell line and LPS stimulation conditions Immortalized mouse macrophages (IMM), including the IMMs from TLR4 -/- mouse (generated by Bruce Beutler’s laboratory47) and available from Jackson Laboratory) were a generous gift from Dr. Eicke Latz48,49. The cells were cultured in complete DMEM (Gibco, Grand Island, NY, USA) complemented with 10%…

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Diagnostic and commensal Staphylococcus pseudintermedius genomes reveal niche adaptation through parallel selection of defense mechanisms

Bond, R. & Loeffler, A. What’s happened to Staphylococcus intermedius? Taxonomic revision and emergence of multi-drug resistance. J. Small Anim. Pr. 53, 147–154 (2012). Article  CAS  Google Scholar  Carroll, K. C., Burnham, C. D. & Westblade, L. F. From canines to humans: clinical importance of Staphylococcus pseudintermedius. PLoS Pathog. 17,…

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Bioconductor – SEtools

DOI: 10.18129/B9.bioc.SEtools     This package is for version 3.12 of Bioconductor; for the stable, up-to-date release version, see SEtools. SEtools: tools for working with SummarizedExperiment Bioconductor version: 3.12 This includes a set of tools for working with the SummarizedExperiment class, including merging, melting, aggregation and plotting functions. In particular,…

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Error when trying to create a heatmap with DESeq2

I’m following a tutorial for using the DESeq2 package, specifically, for creating heat maps.Here’s the code I use: library(‘dplyr’) library(‘pheatmap’) library(ggplot2) counts <- read.csv(“Control vs serum – RN 1.tsv”, header=TRUE, sep = ‘\t’) counts <- counts[, -c(ncol(counts) – 1, ncol(counts))] counts$Gene <- NULL counts.matrix <- as.matrix(counts) columns <- read.csv(“Metadata.tsv”, header=TRUE,…

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Identification of hub genes associated with gastric cancer

Introduction GC is a common malignant tumor of digestive system. According to the International Agency for Research on Cancer (IARC) of the World Health Organization, there were 19.29 million new cancer cases and 9.96 million deaths worldwide in 2020, of which the number of new cases of GC was 1089,103…

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Bioconductor – scider (development version)

DOI: 10.18129/B9.bioc.scider   This is the development version of scider; to use it, please install the devel version of Bioconductor. Spatial cell-type inter-correlation by density in R Bioconductor version: Development (3.18) scider is an user-friendly R package providing functions to model the global density of cells in a slide of…

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Error creating SPIA data for KEGG Orthology (KO) Database KEGG xml files

Hi all, I’m trying to create a SPIA data file for all 483 xml files for the KEGG Orthology (KO) Database. I’m working with a non-model organism that is not supported by KEGG as it’s own organism, so I have to use the KEGG Orthology (KO) Database instead of a…

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Plotting time series data after running natural splines regression in DESeq2.

Hello, I am running differential expression analysis on age-related changes in transcription using natural splines with DESeq2 like so: dds <- DESeqDataSetFromMatrix(countData = counts, colData = coldata, design = ~ ns(age_scaled, df = 3)) keep <- rowSums(counts(dds) >= 10) >= 3 dds <- dds[keep,] dds <- DESeq(dds, test=”LRT”, reduced =…

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Bioconductor – dagLogo

DOI: 10.18129/B9.bioc.dagLogo     This package is for version 3.15 of Bioconductor; for the stable, up-to-date release version, see dagLogo. dagLogo: a Bioconductor package for visualizing conserved amino acid sequence pattern in groups based on probability theory Bioconductor version: 3.15 Visualize significant conserved amino acid sequence pattern in groups based…

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Bioconductor – DepInfeR (development version)

DOI: 10.18129/B9.bioc.DepInfeR   This is the development version of DepInfeR; for the stable release version, see DepInfeR. Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling Bioconductor version: Development (3.18) DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines…

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Annotating Proteins in heatmap with 1 or more pathways using R.

Annotating Proteins in heatmap with 1 or more pathways using R. 0 Hello, I relatively new to programming with R and I was wondering if someone could help me in providing suggestions on annotating proteins in a heatmap. Here I have tried the following code, but the heatmap generated is…

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conda – How to install Bioconda packages

The Bioconda channel builds packages with the Conda Forge channel prioritized. This requires users to likewise prioritize the Conda Forge channel when installing Bioconda packages (see Bioconda documentation). That is, the correct formulation for specifying channels at install time is: conda install -c conda-forge -c bioconda foo Global configuration and…

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Signatures of necroptosis-related genes as diagnostic markers of endometriosis and their correlation with immune infiltration | BMC Women’s Health

Technical roadmap Figure 1. Fig. 1 Analysis of endometriosis-related differentially expressed genes Using the limma package, we first normalized the expression profile data of the endometriosis datasets, GSE7305 and GSE11691. The data distribution before and after standardized treatment is revealed in a box plot (Figs. 2A–D). We found that the data after…

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DeSeq2 data comparison and extracting outputs

Hi, I have an RNA-seq experiment where there are 2 conditions and 2 genotypes. I am trying to figure out how to output the 2 conditions with 2 genotypes from the dds object. I have read online resources, however, it is still not clear what is extracted. I followed and…

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Bioconductor – RNASeqR

DOI: 10.18129/B9.bioc.RNASeqR     RNASeqR: an R package for automated two-group RNA-Seq analysis workflow Bioconductor version: Release (3.11) This R package is designed for case-control RNA-Seq analysis (two-group). There are six steps: “RNASeqRParam S4 Object Creation”, “Environment Setup”, “Quality Assessment”, “Reads Alignment & Quantification”, “Gene-level Differential Analyses” and “Functional Analyses”….

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RNA sequencing and gene expression analysis in a Mouse model

Introduction Chronic obstructive pulmonary disease (COPD) is a condition that is characterized by persistent respiratory symptoms and airflow limitations that are not fully reversible. The severe complications of the disease may adversely affect its morbidity and mortality.1 According to World Health Organization (WHO) statistics, over 3 million people per year…

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Pheatmap x must be numeric

Pheatmap x must be numeric 0 I receive the following error from pheatmap: pheatmap(my_matrix,annotation_col = my_metadata) Error in cut.default(a, breaks = 100) : ‘x’ must be numeric However, my matrix is already numeric apply(my_matrix,2,is.numeric) Returns: GCBGE0767G POHRZ1835F ZCBZP7326X HHMJY1357N IMTWE9463H DJPSO3322R REXYQ6498S OFCFL1180G KTGMI0703N AQDZN3914I IBKRI1023I HGIIC0255O PEUMK2767Q TRUE TRUE…

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Integrated meta-omics approaches reveal Saccharopolyspora as the core functional genus in huangjiu fermentations

Saccharopolyspora is abundant in the wheat qu and HJFM microbiota To uncover the variation curve of microorganisms during fermentation, microbial DNA copy numbers and community structure were analyzed using quantitative real-time PCR (qPCR) and metagenomic analyses. DNA copy numbers of total bacteria and fungi in wheat qu were 11.2 ± 0.2 log10…

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Somatic SLC30A1 mutations altering zinc transporter ZnT1 cause aldosterone-producing adenomas and primary aldosteronism

Young, W. F. Primary aldosteronism: renaissance of a syndrome. Clin. Endocrinol. 66, 607–618 (2007). Article  CAS  Google Scholar  Rossi, G. P. et al. A prospective study of the prevalence of primary aldosteronism in 1,125 hypertensive patients. J. Am. Coll. Cardiol. 48, 2293–2300 (2006). Article  CAS  PubMed  Google Scholar  Fardella, C….

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Bioconductor – concordexR

DOI: 10.18129/B9.bioc.concordexR   Calculate the concordex coefficient Bioconductor version: Release (3.17) Many analysis workflows include approximation of a nearest neighbors graph followed by clustering of the graph structure. The concordex coefficient estimates the concordance between the graph and clustering results. The package ‘concordexR’ is an R implementation of the original…

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Bioconductor – TREG

DOI: 10.18129/B9.bioc.TREG   Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data Bioconductor version: Release (3.17) RNA abundance and cell size parameters could improve RNA-seq deconvolution algorithms to more accurately estimate cell type proportions given the different cell type transcription activity levels. A Total RNA Expression Gene…

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Bioconductor – granulator

DOI: 10.18129/B9.bioc.granulator   Rapid benchmarking of methods for *in silico* deconvolution of bulk RNA-seq data Bioconductor version: Release (3.17) granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface…

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Bioconductor – POWSC

DOI: 10.18129/B9.bioc.POWSC   Simulation, power evaluation, and sample size recommendation for single cell RNA-seq Bioconductor version: Release (3.17) Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available…

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Bioconductor – CiteFuse

DOI: 10.18129/B9.bioc.CiteFuse   CiteFuse: multi-modal analysis of CITE-seq data Bioconductor version: Release (3.17) CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis,…

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Requesting further clarification on interpreting relative gene expression strength

Requesting further clarification on interpreting relative gene expression strength 0 What is your opinion on the following paragraph in section 6.3 of RNA-seq workflow. (master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html) by the creators of DESeq2 package. Paragraph: “The heatmap becomes more interesting if we do not look at absolute expression strength but rather at the…

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KRTA6A and FA2H Are Hub Genes Associated With Cgas-STING-related Immunogenic Cell Death in Lung Adenocarcinoma

Abstract Background/Aim: The immunogenic cell death (ICD) pathway plays a crucial prognostic role in lung adenocarcinoma (LUAD) therapy. The cyclic GMP–AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway is an upstream mechanism that drives ICD activation, but the interaction of hub genes remains unclear. The present study aimed to investigate…

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Mycobacterial DNA-binding protein 1 is critical for BCG survival in stressful environments and simultaneously regulates gene expression

Koul, A. et al. Delayed bactericidal response of Mycobacterium tuberculosis to bedaquiline involves remodelling of bacterial metabolism. Nat. Commun. doi.org/10.1038/ncomms4369 (2014). Article  PubMed  Google Scholar  Global Tuberculosis Report 2021. www.who.int/publications/digital/global-tuberculosis-report-2021. Colditz, G. A. et al. Efficacy of BCG vaccine in the prevention of tuberculosis: Meta-analysis of the published literature. JAMA…

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Get the “average” of a column in a heatmap, combine 3 heatmaps into 1?

Get the “average” of a column in a heatmap, combine 3 heatmaps into 1? 1 Hey guys, So I have 3 gene signatures that I wanna look into on my own RNASeq data. I’ve created hierarchal clustering heatmaps for these gene signatures, and I want to get the “average” expression…

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Combine 3 heatmaps into 1? Get the “average” of a column

Combine 3 heatmaps into 1? Get the “average” of a column 0 Hey guys, So I have 3 gene signatures that I wanna look into on my own RNASeq data. I’ve created hierarchal clustering heatmaps for these gene signatures, and I want to get the “average” expression of a sample/column,…

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Problems using ERCC spike-ins for normalization in DESeq2

Hello everyone, I’m analyzing some RNA-seq datasets for differential expression. We did not run the experiments ourselves, but the database where I got them from indicates that they were done adding additional spike-ins (ERCC92) for normalization. Originally, I did not use these spike-ins, and just went along with the default…

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The script to install the required packages for gene expression analysis using DESeq2 for downstream analysis and visualization

Tool:The script to install the required packages for gene expression analysis using DESeq2 for downstream analysis and visualization 0 if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”) # Install Bioconductor packages BiocManager::install(‘DESeq2’) BiocManager::install(“apeglm”) BiocManager::install(“EnhancedVolcano”) BiocManager::install(“Glimma”) BiocManager::install(“ggrepel”) BiocManager::install(‘limma’) BiocManager::install(‘edgeR’) BiocManager::install(“reactome.db”) BiocManager::install(“AnnotationDbi”) # Install CRAN packages install.packages(‘tidyverse’) install.packages(‘ggplot2’) install.packages(“pheatmap”) R RNAseq installation DESeq2 •…

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the script to install all the required packages for gene expression analysis using DESeq2

Tool:the script to install all the required packages for gene expression analysis using DESeq2 0 if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”) # Install Bioconductor packages BiocManager::install(‘DESeq2’) BiocManager::install(“apeglm”) BiocManager::install(“EnhancedVolcano”) BiocManager::install(“Glimma”) BiocManager::install(“ggrepel”) BiocManager::install(‘limma’) BiocManager::install(‘edgeR’) BiocManager::install(“reactome.db”) BiocManager::install(“AnnotationDbi”) # Install CRAN packages install.packages(‘tidyverse’) install.packages(‘ggplot2’) install.packages(“pheatmap”) R RNAseq installation DESeq2 • 29 views Read more…

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Make heatmap for RNA-seq with non replicate

Make heatmap for RNA-seq with non replicate 0 Hi all, degs = rownames(subset(DEG, PValue < 0.05 & abs(logFC > 9))) rownames(counts) = DEG[rownames(counts), ‘symbol’] counts_degs = counts[degs,] pheatmap(counts_degs, clustering_method = ‘ward.D’, scale=”row”) Could I use TPM matrix instead of raw count matrix to make heat map using the code above?…

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R Heatmap

R Heatmap A short tutorial for decent heat maps in R 图片 How to Create a Beautiful Interactive Heatmap in R – Datanovia 图片 Heatmap in R: Static and Interactive Visualization – Datanovia 图片 A short tutorial for decent heat maps in R 图片 How To Make a Heatmap in…

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Gut microbiota analyses of inflammatory bowel diseases from a representative Saudi population | BMC Gastroenterology

Study populations Between 2015 and 2019, stool samples and data were collected from 219 IBD subjects (CD or UC) attending the Internal Medicine Clinics, King Fahd Hospital of the University, Al-Khobar and King Fahad Hospital, Alhafof, Saudi Arabia. Diagnosis of IBD was based on endoscopy (for CD) or colonoscopy (for…

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Heatmap for ATAC-seq

Heatmap for ATAC-seq 0 Hi all, I apply similar code that I used for RNA-seq (DeSeq2) to ATAC-seq to make a heatmap, so I am not use if it still makes sense. The function here is Heatmap(), not pheatmap(). Instead of count matrix for genes, here I use consensus_peaks.mLb.clN.featureCounts.txt, a…

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Molecular features driving cellular complexity of human brain evolution

King, M. C. & Wilson, A. C. Evolution at two levels in humans and chimpanzees. Science 188, 107–116 (1975). Article  ADS  CAS  PubMed  Google Scholar  Konopka, G. et al. Human-specific transcriptional networks in the brain. Neuron 75, 601–617 (2012). Article  CAS  PubMed  PubMed Central  Google Scholar  Liu, X. et al….

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Bioconductor – rrvgo

DOI: 10.18129/B9.bioc.rrvgo     Reduce + Visualize GO Bioconductor version: Release (3.13) Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity. Author: Sergi Sayols [aut, cre] Maintainer: Sergi Sayols <sergisayolspuig at gmail.com> Citation (from within R, enter citation(“rrvgo”)): Installation To install this package, start…

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Quarto render error with chinese docs and knitr opts_chunk set in YAML heading – #2 by goo – RStudio IDE

A minimal demo: — title: “demo” lang: zh knitr: opts_chunk: collapse: false format: gfm: default — 第一行 第二行 – 第三行 – 第四行 – 第五行 The output # demo 第一行二行 第三行 第四行 第五行 However, the expected output is # demo 第一行 第二行 – 第三行 – 第四行 – 第五行 There are more…

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Advantages of install.packages() versus BiocManager::install()

Suppose we wish to run a workflow requiring a multitude of packages, and that others may use. We could write: libsNeeded<-c(‘vsn’, ‘RColorBrewer’, ‘pheatmap’, ‘ggplot2’, ‘DESeq2’, ‘dplyr’, ‘ashr’, ‘apeglm’, ‘openxlsx’, ‘tidyr’) installedLibs <- libsNeeded[(libsNeeded %in% installed.packages()[,”Package”])] # which are installed locally missingLibs <- libsNeeded[!(libsNeeded %in% installed.packages()[,”Package”])] # which are not for…

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Bioinformatics and system biology approach to identify potential common pathogenesis for COVID-19 infection and osteoarthritis

Hunter, D. J. & Bierma-Zeinstra, S. Osteoarthritis. Lancet 393, 1745–1759 (2019). Article  CAS  PubMed  Google Scholar  Puig-Junoy, J. & Ruiz Zamora, A. Socio-economic costs of osteoarthritis: A systematic review of cost-of-illness studies. Semin. Arthritis Rheum. 44, 531–541 (2015). Article  PubMed  Google Scholar  Hunter, D. J., March, L. & Chew, M….

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Bioconductor – CHETAH

DOI: 10.18129/B9.bioc.CHETAH     This package is for version 3.13 of Bioconductor; for the stable, up-to-date release version, see CHETAH. Fast and accurate scRNA-seq cell type identification Bioconductor version: 3.13 CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate, selective and fast scRNA-seq classifier. Classification is guided…

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Italicise annotations with pheatmap

Hello, I have some column annotations in a heatmap (generated with pheatmap), which correspond to species that i want to be in italics. I have read through the pheatmap documentation and I can’t find anything. I have also tried to find and adapt solutions online (for example), but most tutorials…

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Handling NA’s in Deseq2

Hi everyone First of all thank you for making rna-seq data much more accessible to an average clinical doctor through the DEseq2 packages and vignettes. I am though running into some trouble: I have a dataset of Nanostring mRNA-data from clinical study, which later was followed up. I therefore have…

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IL5RA as an immunogenic cell death-related predictor in progression and therapeutic response of multiple myeloma

Differential expression analysis We downloaded GSE125361 (n = 48) microarray data from the Gene Expression Omnibus (GEO) database, which included 45 myeloma samples and 3 controls, for expression analysis of IL5RA in cancer16. Additionally, we analyzed the expression of IL5RA in smoldering myeloma (SMM) patients who progressed to active MM (n = 10) and…

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How do I add row names to pheatmap() when I am using a pre-normalized matrix?

How do I add row names to pheatmap() when I am using a pre-normalized matrix? 0 I have extracted normalized values from DESeq2 because I want to display only certain genes. I would now like to create a heatmap with my final matrix. This is the code I am using,…

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Trouble annotating heatmap with pheatmap. Error in annotation_col[colnames(mat), , drop = F] : subscript out of bounds

Hi everyone, I would like to ask if anyone could help me with Pheatmap. I am trying to annotate my heatmap, but nothing is working. I already checked if the dimensions and column names fit in the metadata and counts file (you can find all the tests in the code)….

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Permafrost microbial communities and functional genes are structured by latitudinal and soil geochemical gradients

Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, et al. Structure and function of the global topsoil microbiome. Nature. 2018;560:233–7. Article  CAS  PubMed  Google Scholar  Waldrop MP, Holloway JM, Smith DB, Goldhaber MB, Drenovsky RE, Scow KM, et al. The interacting roles of climate, soils, and…

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Sulfur metabolism in subtropical marine mangrove sediments fundamentally differs from other habitats as revealed by SMDB

Summary of genes and pathways in SMDB Using keywords (e.g., sulfur, sulfate) to retrieve 284,541 literature reports from 1976 to 2021 in Web of Science and then obtained records of sulfur genes through a web crawler with Python. After manual verification, 875 related literature reports (representative literature was recruited) and…

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`MOFAobject@expectations` is empty list

`MOFAobject@expectations` is empty list 0 Hello I run the tutorial (raw.githack.com/bioFAM/MOFA2_tutorials/master/R_tutorials/CLL.html) library(MOFA2) library(MOFAdata) library(data.table) library(ggplot2) library(tidyverse) utils::data(“CLL_data”) MOFAobject <- create_mofa(CLL_data) MOFAobject data_opts <- get_default_data_options(MOFAobject) model_opts <- get_default_model_options(MOFAobject) model_opts$num_factors <- 15 train_opts <- get_default_training_options(MOFAobject) train_opts$convergence_mode <- “slow” train_opts$seed <- 42 MOFAobject <- prepare_mofa(MOFAobject, data_options = data_opts, model_options = model_opts, training_options =…

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lfcshrink error DESeq2

Hello! I’m having problems with lfcShrink in my DESeq2 workflow. I’m trying to do a differential expression analysis (with only one comparison term: “MULTIseq_ID_call2”) on my single-cell data. However when I do lfcShrink I get an error that I cannot interpret. Can you help me? dds <- DESeq(dds, test =…

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Can I consider these values as differentially expressed?

Hello! I’m needing some help from the more experienced ones! n_n’ I’m doing a transcriptome expression comparison using DESeq2 and I would like to be sure I’m using the right parameters. This doubt came after seeing that a gene increased the expression between different sampling points but was not included…

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A DBHS family member regulates male determination in the filariasis vector Armigeres subalbatus

Mosquitoes The Armigeres subalbatus GZ strain (Guangzhou Guangdong Province, China) was established in the laboratory in 2018 and reared in 30-cm3 nylon cages in the insectary at 28 ± 1 °C with 70–80% humidity and a 12:12 h (light: dark) light cycles. Larvae were fed with finely-ground fish food mixed 1:1 with yeast powder,…

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Bioconductor – Cepo (development version)

DOI: 10.18129/B9.bioc.Cepo     This is the development version of Cepo; for the stable release version, see Cepo. Cepo for the identification of differentially stable genes Bioconductor version: Development (3.17) Defining the identity of a cell is fundamental to understand the heterogeneity of cells to various environmental signals and perturbations….

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Bioconductor – eegc

DOI: 10.18129/B9.bioc.eegc     This package is for version 3.12 of Bioconductor; for the stable, up-to-date release version, see eegc. Engineering Evaluation by Gene Categorization (eegc) Bioconductor version: 3.12 This package has been developed to evaluate cellular engineering processes for direct differentiation of stem cells or conversion (transdifferentiation) of somatic…

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Dryad | Data — RNAseq transcriptome of draining lymph node (LN) and tumor of MC38 murine tumors treated with cryoablation and chitosan/IL-12

Focal ablation technologies are routinely used in the clinical management of inoperable solid tumors but often result in incomplete ablations leading to high recurrence rates. Adjuvant therapies capable of safely eliminating residual tumor cells are therefore of great clinical interest. Interleukin 12 (IL-12) is a potent antitumor cytokine that can…

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data file link: | Chegg.com

data file link: drive.google.com/file/d/1Odr12yDiUwI02-BfaXrHehKBM1uMW_1N/view?usp=share_link Step 1 (5pts) Load the file GSE124548.raw.txt into R and create a new dataframe with just the columns with the raw counts for healthy (HC) and CF patients before treatment (Base) and call it readcount. Use the first column (EntrezID) in the original file as the…

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Cannot load DESeq2 in R

I have been using DESeq2 without problem for many months, until today. When I try to load the package in R I get the following problem: > library(DESeq2) Loading required package: SummarizedExperiment Error: package or namespace load failed for ‘SummarizedExperiment’ in dyn.load(file, DLLpath = DLLpath, …): unable to load shared…

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Error in loading DESeq2

Error in loading DESeq2 0 Hi all I have been using DESeq2 no problem for a while including earlier today Now, whenever I load it I get the below error message. I tried re-downloading DESeq2 and restarting my computer and R, but no dice. Any thoughts? Error: package or namespace…

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Bioconductor – scviR (development version)

DOI: 10.18129/B9.bioc.scviR   This is the development version of scviR; to use it, please install the devel version of Bioconductor. experimental inferface from R to scvi-tools Bioconductor version: Development (3.17) This package defines interfaces from R to scvi-tools. A vignette works through the totalVI tutorial for analyzing CITE-seq data. Another…

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How to compare RNA expression correlation?

How to compare RNA expression correlation? 1 I have a set of genes that is important for the cell cycle and DNA damage. I wish to compare the RNA expression of these genes between each two cell lines and generate a heatmap to visualize the correlation (the x and y…

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Error when running GDC_prepare

Error when running GDC_prepare 0 @4bb5d1af Last seen 10 hours ago Sweden I’m trying to retrieve and prepare some data from GDC and I’m running the following code which I copy-pasted from this YouTube video: www.youtube.com/watch?v=UWXv9dUpxNE library(TCGAbiolinks) library(tidyverse) library(maftools) library(pheatmap) library(SummarizedExperiment) # get a list of projects gdcprojects <- getGDCprojects()…

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‘gpar’ element ‘fill’ must not be length 0

‘gpar’ element ‘fill’ must not be length 0 1 Hello everyone, I’m trying to make a heatmap but I get the following error, can you help me? topVarGenes <- head(order(rowVars(assay(rld)), decreasing = TRUE), 20) mat <- assay(rld)[ topVarGenes, ] mat <- mat – rowMeans(mat) anno <- as.data.frame(colData(rld)[, c(“Condition”)]) pheatmap(mat, annotation_col…

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Unravelling microalgal-bacterial interactions in aquatic ecosystems through 16S rRNA gene-based co-occurrence networks

Croft, M. T., Lawrence, A. D., Raux-Deery, E., Warren, M. J. & Smith, A. G. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature doi.org/10.1038/nature04056 (2005). Article  PubMed  Google Scholar  Kazamia, E. et al. Mutualistic interactions between vitamin B12-dependent algae and heterotrophic bacteria exhibit regulation. Environ. Microbiol. doi.org/10.1111/j.1462-2920.2012.02733.x…

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ggplot2 – Comparison between two different species with different gene, tissue names and number of tissues in R

I want to create 2,3 heatmaps or barcharts side by side for every gene ortholog between two or three different species in R. The problem is that my data are unbalanced, meaning I have gene names which are different between the two species (cat1_gene – mat1_gene) and tissues which are…

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Bioinformatics construction and experimental validation of a cuproptosis-related lncRNA prognostic model in lung adenocarcinoma for immunotherapy response prediction

Data collection and processing The RNA-sequencing data, clinical information and simple nucleotide variation of LUAD patients were retrieved from TCGA database (portal.gdc.cancer.gov/, accessed April 8, 2022). Nineteen cuproptosis-related genes (CRG) were mainly collected from previous study, including LIPT1, GLS, NFE2L2, NLRP3, LIAS, ATP7B, ATP7A, SLC31A1, FDX1, LIPT2, DLD, DLAT, PDHA1,…

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Limma contrasts for DEGs

I have a 2 factor design with one factor being 2 levels and the other being 4 levels. I am only interested in the upregulated genes specific to the interaction of level 2 of the first factor and the 4th level of the second factor. I am unsure how to…

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adding row label instead of row names

Thank you, Seidel. Yes, I want to avoid the row names, and I did that earlier: show_rownames = FALSE, which worked well. I also removed the dendrogram along the y-axis. The aim was to put the label “participants” into the position where the rownames were before. I played around with…

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Amoxicillin and thiamphenicol treatments may influence the co-selection of resistance genes in the chicken gut microbiota

General description of sequences After the quality filtering step, removal of chimeric fragments, and read merging, a total of 3,378,323 reads with 3007 different features was obtained, with an average of 27,244 sequences per individual sample. After quality filtering, none of the samples was excluded from the analysis of microbial…

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Insights on the bacterial composition of Parmigiano Reggiano Pure Whey Starter by a culture-dependent and 16S rRNA metabarcoding portrait

Smid, E. J. et al. Practical implications of the microbial group construction of undefined mesophilic starter cultures. Microb. Cell Factories 13, S2. doi.org/10.1186/1475-2859-13-S1-S2 (2014). Article  Google Scholar  Stadhouders, J. & Leenders, G. J. M. Spontaneously developed mixed-strain cheese starters. Their behaviour in direction of phages and their use within the…

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Draw Table in Plot in R (4 Examples) | Barplot, Histogram & Heatmap – Stats Idea

  This article shows several alternatives on how to plot a table object in R programming. The article will consist of the following information: Here’s how to do it!   Creating Example Data Have a look at the example data below: x <- c(letters[1:4], letters[2:4], “d”) # Create example vector…

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Log2FC values slightly higher in some genes after DESeq2 shrinkage

Hi, I have a question about DESeq2 LFCshrinkage: Is it possible that some genes have a slightly higher LFC after shrinkage? It happened during my RNAseq DE analysis, I have very deeply sequenced samples with large base means. I tried visualizing using MAplot check, and it looks fine. I’m mainly…

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DESeq2 aggregated single cell data

Hi, Im aiming to use aggregated single cell data to perform a pseudobulk analysis to assess differential expression between those with sarcopenia and those without, termed “status_binary” with the levels “yes” and “no”. # DESeq2 —————————————————————— dds <- DESeqDataSetFromMatrix(y$counts, colData = y$samples, design = ~Sex+age_scaled+status_binary) # Transform counts for data…

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How to change color of rownames display in pheatmap

How to change color of rownames display in pheatmap 2 The row name labels of my heatmap are genes. The default color for the column names and row names are black, however, I would like to change some gene names to different colors (for example, red for up-regulated genes and…

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Bioconductor – ComplexHeatmap

DOI: 10.18129/B9.bioc.ComplexHeatmap     This package is for version 3.14 of Bioconductor; for the stable, up-to-date release version, see ComplexHeatmap. Make Complex Heatmaps Bioconductor version: 3.14 Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly…

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GDCquery_Maf error

GDCquery_Maf error 0 @76e1237b Last seen 1 day ago Singapore Hi all, I really need some help. I am trying to run GDCquery_Maf which worked fine until yesterday. Now I get the following error: Error in GDCquery(paste0(“TCGA-“, tumor), data.category = “Simple Nucleotide Variation”, : Please set a valid workflow.type argument…

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Bioconductor – mirTarRnaSeq

DOI: 10.18129/B9.bioc.mirTarRnaSeq     mirTarRnaSeq Bioconductor version: Release (3.14) mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis…

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Pathway analysis of RNAseq data using goseq package

Hello, I have finished the RNA seq analysis and I am trying to perform some pathway analysis. I have used the gage package and I was looking online about another package called goseq that takes into account length bias. However, when I run the code I get an error. How…

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Validation of hub genes in acute pancreatitis

Introduction Acute pancreatitis (AP) is a common disease found in clinics, and requires urgent Hospital admission. The incidence of AP is increasing in recent years worldwide.1 The patients with AP increased from 1,727,789.3 to 2,814,972.3 between 1990 and 2019 in 204 countries and territories.2 Meanwhile, nearly 20% of AP patients…

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Bioconductor – DaMiRseq (development version)

DOI: 10.18129/B9.bioc.DaMiRseq     This is the development version of DaMiRseq; for the stable release version, see DaMiRseq. Data Mining for RNA-seq data: normalization, feature selection and classification Bioconductor version: Development (3.15) The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them…

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Time-course RNASeq of Camponotus floridanus forager and nurse ant brains indicate links between plasticity in the biological clock and behavioral division of labor | BMC Genomics

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….

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Heatmap deseq2

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…

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Immune-related Prognostic Genes of ccRCC

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…

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Bioinformation Analysis Reveals IFIT1 as Potential Biomarkers in Centr

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…

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r – RNA-Seq Data Heatmap: Is it necessary to do a log2 transformation of RPKM values before doing the Z-score standardisation?

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…

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Design formula in DESeq2

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…

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Plot LFC with pheatmap of differentially expressed gene list from DESeq2.

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”)…

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Bioconductor – GEOexplorer (development version)

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…

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Bioconductor – conclus

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…

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Changing colour labels of samples with pheatmap

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 =…

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