Tag: enrichr

Gene Set – TNFSF10

Dataset MSigDB Cancer Gene Co-expression Modules Category transcriptomics Type co-expressed gene Description tumor necrosis factor (ligand) superfamily, member 10|The protein encoded by this gene is a cytokine that belongs to the tumor necrosis factor (TNF) ligand family. This protein preferentially induces apoptosis in transformed and tumor cells, but does not…

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Landscape of helper and regulatory antitumour CD4+ T cells in melanoma

Sallusto, F. & Lanzavecchia, A. Heterogeneity of CD4+ memory T cells: functional modules for tailored immunity. Eur. J. Immunol. 39, 2076–2082 (2009). CAS  PubMed  Article  Google Scholar  Swain, S. L., McKinstry, K. K. & Strutt, T. M. Expanding roles for CD4+ T cells in immunity to viruses. Nat. Rev. Immunol….

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EnrichR website not responding is too vague

I’ve noticed 10 issues related to”EnrichR website not responding”. Is it possible to output some more details about the connection errors when running listEnrichrSites or setEnrichrSite? For example, httr::GET below might return the problem and this would be a much better message than “EnrichR website not responding”. I’m sure there…

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Human distal lung maps and lineage hierarchies reveal a bipotent progenitor

Verleden, S. E. et al. Small airways pathology in idiopathic pulmonary fibrosis: a retrospective cohort study. Lancet Respir. Med. 8, 573–584 (2020). CAS  PubMed  PubMed Central  Google Scholar  Hogg, J. C., Macklem, P. T. & Thurlbeck, W. M. The resistance of small airways in normal and diseased human lungs. Aspen…

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

The protocol presented here describes a complete pipeline to analyze RNA-sequencing transcriptome data from raw reads to functional analysis, including quality control and preprocessing steps to advanced statistical analytical approaches. Welcome to the protocol of high-throughput transcriptome analysis for investigating host-pathogen interactions. This protocol is divided in the following steps….

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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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Cell Strain-Derived Induced Pluripotent Stem Cells as an Isogenic Approach To Investigate Age-Related Host Response to Flaviviral Infection

INTRODUCTION Dengue is the most common mosquito-borne viral disease globally (1). This acute disease, which can be life-threatening, is caused by four different dengue viruses (DENVs) (DENV-1, DENV-2, DENV-3, and DENV-4). An estimated 390 million people are infected with these DENVs annually (2), and populations throughout the tropics face frequent…

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Identification of a four-gene signature & PTC.

Introduction Thyroid carcinoma (THCA) is the most common type of endocrine malignancy and its incidence is increasing.1 Based on its histopathological characteristics, thyroid carcinoma can be classified into multiple subtypes, such as papillary thyroid carcinoma (PTC), follicular thyroid carcinoma, and anaplastic thyroid carcinoma.2 PTC is the most common subtype of…

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Identification of a regulatory pathway inhibiting adipogenesis via RSPO2

Integration of APC scRNA-seq data reveals heterogeneity of adipocyte progenitor cells In a previous study9, we defined Lin−Sca1+CD142+ APCs as adipogenesis regulatory (Areg) cells and demonstrated that these cells are both refractory toward adipogenesis and control adipocyte formation of APCs through paracrine signaling. In contrast, Merrick et. al.4 observed that Lin−CD142+ cells…

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No results when inputting genes with scores

Hi, I am using EnrichR to analyze my DEGs gotten from limma. I extracted subsets of significant DEGs with their IDs and t-statistics that have been scaled, like the following example: CHI3L1 1.0000000 ARPC1B 0.9605097 SH3D21 0.9303946 FCGBP 0.9165999 MFNG 0.8830144 C2 0.8153162 CTA-398F10.2 0.8459803 CTSD 0.7543101 GRN 0.7503898 CTSB…

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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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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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