Tag: ConsensusClusterPlus

what is the RefSeq? | 3 Answers from Research papers

whole genome sequencing of ASFV 5 answers What are the challenges and opportunities of data storytelling? 5 answers what methods exist for genotyping non model species? 5 answers RNA expression data ConsensusClusterPlus other similar tools? 5 answers What are the gaps in tuber crops genomic studies? 5 answers Have quantitative…

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Bioconductor – TCGAbiolinks

DOI: 10.18129/B9.bioc.TCGAbiolinks     TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data Bioconductor version: Release (3.5) The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses…

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Insights into expression patterns and immunotherapy response prediction

[1] D. Schadendorf, D. E. Fisher, C. Garbe, J. E. Gershenwald, J. Grob, A. Halpern, et al., Melanoma, Nat. Rev. Dis. Primers, 1 (2015), 15003. doi.org/10.1038/nrdp.2015.3 doi: 10.1038/nrdp.2015.3 [2] A. M. M. Eggermont, A. Spatz, C….

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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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Bioconductor – CVE

DOI: 10.18129/B9.bioc.CVE     This package is deprecated. It will probably be removed from Bioconductor. Please refer to the package end-of-life guidelines for more information. This package is for version 3.11 of Bioconductor. This package has been removed from Bioconductor. For the last stable, up-to-date release version, see CVE. Cancer…

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Histone regulator KAT2A acts as a potential biomarker related to tumor microenvironment and prognosis of diffuse large B cell lymphoma | BMC Cancer

Zhang Y, Tan H, Daniels JD, Zandkarimi F, Liu H, Brown LM, et al. Imidazole Ketone Erastin induces ferroptosis and slows Tumor Growth in a mouse lymphoma model. Cell Chem Biol. 2019;26(5):623–33e9. Article  CAS  PubMed  PubMed Central  Google Scholar  Hartert KT, Wenzl K, Krull JE, Manske M, Sarangi V, Asmann…

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Consensus Cluster takes normalized gene counts or raw gene counts?

Hi! I am using package ConsensusClusterPlus in R to discover the optimal number of gene expression clusters. Following the steps [note: the code is pseudocode, just to help the understanding]: 1 Get the RNA SEQ data (rows: genes, cols: samples/patients) 2 Keep only the top 30% Most Variable Genes by…

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Unsupervised clustering on gene expression data

Clustering is a data mining method to identify unknown possible groups of items solely based on intrinsic features and no external variables. Basically, clustering includes four steps: 1) Data preparation and Feature selection, 2) Dissimilarity matrix calculation, 3) applying clustering algorithms, 4) Assessing cluster assignment I use an RNA-seq dataset…

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Comprehensive analysis of m6A regulators associated with immune infiltration in Hepatitis B virus-related hepatocellular carcinoma | BMC Gastroenterology

M6A regulators are likely to have a significant effect on HBV-related HCC Twenty m6A regulators, including 12 readers, 7 writers, and 1 eraser, were identified in the TCGA and GEO cohorts. The correlation network provided interactive information among the m6A regulators. The ratio of somatic mutations and CNV for the…

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Bioconductor – CATALYST

DOI: 10.18129/B9.bioc.CATALYST     This package is for version 3.12 of Bioconductor; for the stable, up-to-date release version, see CATALYST. Cytometry dATa anALYSis Tools Bioconductor version: 3.12 Mass cytometry (CyTOF) uses heavy metal isotopes rather than fluorescent tags as reporters to label antibodies, thereby substantially decreasing spectral overlap and allowing…

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Bioconductor – ChromSCape

DOI: 10.18129/B9.bioc.ChromSCape     This package is for version 3.15 of Bioconductor; for the stable, up-to-date release version, see ChromSCape. Analysis of single-cell epigenomics datasets with a Shiny App Bioconductor version: 3.15 ChromSCape – Chromatin landscape profiling for Single Cells – is a ready-to-launch user-friendly Shiny Application for the analysis…

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Integration of bulk RNA sequencing data and single-cell RNA sequencing analysis on the heterogeneity in patients with colorectal cancer

Bao X, Shi R, Zhao T, Wang Y, Anastasov N, Rosemann M et al (2021) Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels tumour heterogeneity plus M2-like tumour-associated macrophage infiltration and aggressiveness in TNBC. Cancer Immunol Immunother 70(1):189–202 Article  CAS  PubMed  Google Scholar  Becht E, McInnes L, Healy J,…

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Identification of macrophage-related genes in sepsis-induced ARDS using bioinformatics and machine learning

Screening of differentially expressed genes in GSE32707 According to the screening criteria of differentially expressed genes, there were 489 differentially expressed genes between the control and sepsis groups, of which 152 genes were downregulated in sepsis patients and 337 genes were upregulated in sepsis patients (Fig. 1A). In contrast, there were…

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Establishment of risk model, analysis of immunoinfiltration

Introduction Atrial fibrillation (AF) is a tachyarrhythmia whose incidence and prevalence are steadily increasing with better chronic disease management in the aging global population. As of 2020, 37.5 million patients worldwide were affected by AF, accounting for 0.51% of the global population.1 As a common arrhythmia, AF greatly increases the…

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A Novel Model Using Ferroptosis-Related Genes Predicts Prognosis in DLBCLs

The following is a summary of “Identification of a novel model based on ferroptosis-related genes for predicting the prognosis of diffuse large B-cell lymphomas,” published in the May 2023 issue of Hematology by Wang, et al. Diffuse large B-cell lymphomas (DLBCLs) are characterized by their phenotypic and genetic heterogeneity. For…

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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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An integrated tumor, immune and microbiome atlas of colon cancer

Samples used in this observational cohort study (tumor tissue and matched healthy colon tissue, AC-ICAM cohort) are from patients with colon cancer diagnosed at Leiden University Medical Center, the Netherlands, from 2001 to 2015 that did not object for future use of human tissues for scientific research and that were…

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Nidogen-2 (NID2) is a Key Factor in Collagen Causing Poor Response to

Yan Sha,1,* An-qi Mao,1,* Yuan-jie Liu,2 Jie-pin Li,2 Ya-ting Gong,3 Dong Xiao,1 Jun Huang,1 Yan-wei Gao,1 Mu-yao Wu,3 Hui Shen1 1Departments of Dermatology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, People’s Republic of China; 2Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu…

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Identification and validation of BCL6 and VEGFA as biomarkers and ageing patterns correlating with immune infiltrates in OA progression

Hunter, D. J. & Bierma-Zeinstra, S. Osteoarthritis. Lancet 393, 1745–1759 (2019). Article  CAS  Google Scholar  Hunter, D. J., March, L. & Chew, M. Osteoarthritis in 2020 and beyond: A Lancet Commission. Lancet 396, 1711–1712 (2020). Article  Google Scholar  Puig-Junoy, J. & RuizZamora, A. Socio-economic costs of osteoarthritis: A systematic review…

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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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ConsensusClusterPlus package

ConsensusClusterPlus package 0 Hi guys, How can I check the plots individually, when I run the ConsensusClusterPlus command they are generated above each other very fast and end up with Tracking plot, I cant find them anywhere else, I tried to generate them as PDFs or as png but nothing…

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How to extract most contributing features for each cluster

ConsensusClusterPlus: How to extract most contributing features for each cluster 0 Hi, I am using the R package ConsensusClusterPlus. Here is an example with the ALL data: library(ConsensusClusterPlus) library(ALL) data(ALL) d = exprs(ALL) res <- ConsensusClusterPlus(d, clusterAlg = “pam”, finalLinkage = “average”, distance = “spearman”, plot = NULL, reps =…

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How to extract plots from ConsensusClusterPlus package

Hello All, Is there a way to extract the plots separately from the ConsensusClusterPlus package? For example, using the example data from the package, I can print the last three plots as below, library(ALL) data(ALL) d=exprs(ALL) mads=apply(d,1,mad) d=d[rev(order(mads))[1:5000],] d = sweep(d,1, apply(d,1,median,na.rm=T)) library(ConsensusClusterPlus) par(mfrow=c(1,3)) title=tempdir() results = ConsensusClusterPlus(d,maxK=6,reps=50,pItem=0.8,pFeature=1, title=title,clusterAlg=”hc”,distance=”pearson”,seed=1262118388.71279) But,…

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