Tag: clusterProfiler

Bioconductor – EasyCellType

DOI: 10.18129/B9.bioc.EasyCellType     Annotate cell types for scRNA-seq data Bioconductor version: Release (3.16) We developed EasyCellType which can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA)…

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Phenotypic plasticity and genetic control in colorectal cancer evolution

Sample preparation and sequencing The method of sample collection and processing is described in a companion article (ref. 23). Sequencing and basic bioinformatic processing of DNA-, RNA- and ATAC-seq data are included there as well. Gene expression normalization and filtering The number of non-ribosomal protein-coding genes on the 23 canonical chromosome pairs…

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Comprehensive Analysis of NPSR1-AS1 as a Novel Diagnostic and Prognostic Biomarker Involved in Immune Infiltrates in Lung Adenocarcinoma

The incidence of lung adenocarcinoma (LUAD), the most common subtype of lung cancer, continues to make lung cancer the largest cause of cancer-related deaths worldwide. Long noncoding RNAs (lncRNAs) have been shown to have a significant role in both the onset and progression of lung cancer. In this study, we…

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The comprehensive detection of miRNA and circRNA in the regulation of intramuscular and subcutaneous adipose tissue of Laiwu pig

Zhou, G. et al. Global comparison of gene expression profiles between intramuscular and subcutaneous adipocytes of neonatal landrace pig using microarray. Meat. Sci. 86(2), 440–450 (2010). CAS  PubMed  Article  Google Scholar  Alfaia, C. M. et al. Current feeding strategies to improve pork intramuscular fat content and its nutritional quality. Adv….

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Single-cell multi-omics of human clonal hematopoiesis reveals that DNMT3A R882 mutations perturb early progenitor states through selective hypomethylation

Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018). CAS  PubMed  PubMed Central  Google Scholar  Yizhak, K. et al. RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Science 364, eaaw0726 (2019). CAS  PubMed  PubMed Central  Google Scholar  Yokoyama, A….

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Live-seq enables temporal transcriptomic recording of single cells

Biological materials RAW264.7, 293T and HeLa cells were obtained from ATCC. RAW264.7 cells with Tnf-mCherry reporter and relA-GFP fusion protein (RAW-G9 clone) were kindly provided by I.D.C. Fraser (National Institutes of Health). The IBA cell line derived from the stromal vascular fraction of interscapular brown adipose tissue of young male…

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Immune Infiltration and N(6)-Methyladenosine ncRNA Isoform Detection in Acute Lung Injury

Acute lung injury (ALI) is a severe form of sepsis that is associated with a high rate of morbidity and death in critically ill individuals. The emergence of ALI is the result of several factors at work. Case mortality rates might range from 40% to 70%. Researchers have discovered that…

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Endometriosis-related functional modules and hub genes

Introduction Endometriosis (EMS) is a chronic gynecological disease defined as implantation and periodic growth of the endometrial glands and stroma outside the uterine cavity, causing chronic pelvic pain, severe dysmenorrhea, and infertility in 10% reproductive-age women, among which the infertility rate is approximately 30–50%.1,2 Surgical excision is commonly used for…

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Identification of potentially functional circular RNAs hsa_circ_0070934 and hsa_circ_0004315 as prognostic factors of hepatocellular carcinoma by integrated bioinformatics analysis

Rawla, P., Sunkara, T., Muralidharan, P. & Raj, J. P. Update in global trends and aetiology of hepatocellular carcinoma. Contemp. Oncol. (Poznan, Poland) 22, 141–150 (2018). CAS  Google Scholar  Kong, D. et al. Current statuses of molecular targeted and immune checkpoint therapies in hepatocellular carcinoma. Am. J. Cancer Res. 10,…

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A single-cell atlas of human and mouse white adipose tissue

Rosen, E. D. & Spiegelman, B. M. What we talk about when we talk about fat. Cell 156, 20–44 (2014). CAS  PubMed  PubMed Central  Google Scholar  Kahn, S. E., Hull, R. L. & Utzschneider, K. M. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444, 840–846 (2006)….

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Identification of Hub Genes Associated with COPD Through Integrated Bi

Introduction Chronic obstructive pulmonary disease (COPD) will become the third leading cause of death worldwide.1,2 The incidence of COPD worldwide is 13.1%3 and is 13.7% in the Chinese population over 40 years of age.4 Emphysema is one of the most common phenotypes.1 Over the past few decades, we have conducted…

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Cluster Profiler output not the same as Enrichr output

Cluster Profiler output not the same as Enrichr output 0 @angkoo-23537 Last seen 18 hours ago United Kingdom Hi there, I have am getting different outputs after running enrichGO on cluster profiler when I put the same genes into enrichR (by Maayan Lab) website. Example here using Biological Process 2021…

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Fatty infiltration after rotator cuff tear

Introduction Rotator cuff tear (RCT) is a common shoulder disorder causing shoulder pain and disability. The prevalence of full-thickness RCT is 20.7% in the general population, and increased with age.1 Rotator cuff play essential roles in shoulder function and the treatment of proximal humeral fractures.2,3 It is important to repair…

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Profiling and functional characterization of maternal mRNA translation during mouse maternal-to-zygotic transition

INTRODUCTION Mammalian life starts with the fusion of two terminally differentiated gametes, sperm and oocyte, resulting in a totipotent zygote. After going through preimplantation development, the zygote reaches blastocyst before implantation. The two most important events taking place during preimplantation development are zygotic genome activation (ZGA) and the first cell…

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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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GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data | BMC Bioinformatics

1. Van den Berge K, Hembach KM, Soneson C, Tiberi S, Clement L, Love MI, Patro R, Robinson MD. RNA sequencing data: Hitchhikers guide to expression analysis. Annu Rev Biomed Data Sci. 2019;2(1):139–73. doi.org/10.1146/annurev-biodatasci-072018-021255. Article  Google Scholar  2. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A,…

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Establishment of sunitinib-resistant CDX model of ccRCC

Introduction Renal cell carcinoma (RCC) accounts for approximately 2–3% of all malignant tumors, and its prevalence is rising. Metastatic RCC accounts for 25–30% of all RCC cases, and has an exceedingly poor prognosis.1 In 2020, among approximately 430,000 newly discovered cases of RCC, 179,000 died.2 Clear cell renal cell carcinoma…

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Identification of Hub Genes in Patients with Alzheimer Disease and Obs

Introduction Alzheimer’s disease (AD) ranks first among the common dementia type of the world. According to epidemiological investigation from the International Alzheimer’s disease association, about 45 million people has been suffered from AD, and the number is expected to increase to 131 million in 2050.1 Despite the widespread prevalence of…

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Proper way(s) to perform enrichment analysis in R

I am not sure what is the proper way to carry out over-representation analysis (and also gene set enrichment analysis) for RNAseq data. Ideally, the analysis can be performed in R, otherwise, if the software/ platform can export the output file (also include all the non-statistical-significant term) will also be…

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What is the codification in genestrand 1 and 2?

What is the codification in genestrand 1 and 2? 0 Hi there, I’m doing some peak annotation using ChIPseeker library(ChIPseeker) library(TxDb.Hsapiens.UCSC.hg38.knownGene) library(clusterProfiler) library(annotables) library(org.Hs.eg.db) txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene peaks= readPeakFile(“peaks_”, header = F) peakAnno <- annotatePeak(peaks, tssRegion=c(-3000, 3000), TxDb=txdb, annoDb=”org.Hs.eg.db”) peaks_annot <- as.data.frame(peakAnno) In my annotation file “geneStrand” is codified as…

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clusterProfiler won’t read gene list

clusterProfiler won’t read gene list 0 So I have a list of DE genes that I would like to analyse for enriched GO and KEGG terms. I was going to use clusterProfiler for this, but I can’t seem to get past constructing the gene list. I have followed the vignette…

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Classifiers for predicting coronary artery disease

Introduction Coronary artery disease (CAD) is a complex pathology associated with behavioral and environmental factors.1–3 CAD shows high prevalence and is associated with a high fatality rate among cardiovascular diseases. The main manifestations of CAD are stable or unstable angina pectoris and identifiable or unrecognized myocardial infarction.4 The main risk…

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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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number of GO terms in results

clusterProfiler: number of GO terms in results 0 I am working with a non-model organism. So I constructed TERM2GENE and TERM2NAME files and used enricher to run GO enrichment analysis. The code I used was below. Finally, I got 71 GO terms in the result. But actually, there are 99…

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Can cnetplot plot points change shape in clusterprofiler?

Can cnetplot plot points change shape in clusterprofiler? 0 I have a cnetplot and I am wondering if it is possible for me to do further categorising of the plot by point shape? I have a cnetplot of genes and their interacting pathways, but the genes also have a few…

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How to transform the deg gene list from seurat to a gene list input to clusterProfiler compareCluster ?

Sorry for lateness, I wanted to do something similar. This is what I did for reference: Using a Seurat generated gene list for input into ClusterProfiler to see the GO or KEGG terms per cluster. I’ll keep the meat and potatoes of the Seurat vignette in this tutorial: library(dplyr) library(Seurat)…

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FindMarkers for ClusterProfiler

FindMarkers for ClusterProfiler 1 Hi, I recently ran FindMarkers to compare DEG between two different clusters in a single-cell RNA-seq analysis This is my code: markers= FindMarkers(obj, ident.1=c(4), ident.2 = c(5)) head(markers) dim(markers) table(markers$avg_log2FC > 0) table(markers4v5$p_val_adj < 0.05 & markers$avg_log2FC > 0) I would like to run ClusterProfiler to…

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working with .gmt files

working with .gmt files 3 Hi! I have downloaded a pathway data set in .gmt format form the GSEA website. I’m wondering how can I properly read this data set in R. Could anyone help me? Thank you!   myposts • 9.5k views • link updated 2 hours ago by…

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How to colour points in cnetplot of clustprofiler?

I have a cnetplot from running enrichment with kegg using clusterprofiler. I have scores input as the fold change but for each gene in the plot they are not varying in colour to show their difference in the fold change score. My dataset is genes of entrez IDs and then…

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What’s the difference between enrichKEGG and gseKEGG

What’s the difference between enrichKEGG and gseKEGG 3 Hi, I was wondering what is the difference between enrichKEGG and gseKEGG in R package ClusterProfile. Thanks! clusterprofiler KEGG • 2.3k views Source link

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Functional enrichment analysis of bacteria

Functional enrichment analysis of bacteria 2 Hello, I have gene lists from an RNA-seq experiment from E.coli bacteria. So far, I have only worked with model organisms, which are supported by biomaRt, so conversion of gene IDs and functional enrichment analysis within R was easy. Now that I am working…

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