Tag: tcga-brca

Understanding gene level copy number data from TCGAbiolinks

Hi all. Thanks in advance for helping me out. I’m trying to analyze copy number data from TCGA (using TCGAbiolinks), and trying to define genes that are either amplified or deleted. To download gene level copy number alteration, I used the code below: query <- GDCquery(project=”TCGA-BRCA”, data.category = ‘Copy Number…

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Cellular senescence triggers intracellular acidification and lysosomal pH alkalinized via ATP6AP2 attenuation in breast cancer cells

Doxo and Abe promote cellular senescence accompanied by an altered profile of senescence-related genes in breast cancer cells Doxo and Abe were used to treat breast cancer cells (human triple-negative breast cancer cell line MDA-MB-231 and human luminal A subtype breast cancer cell line MCF-7) for 24 h, without a robust…

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Batch correction of TCGA data from TCGAbiolinks and cBioPortal

Batch correction of TCGA data from TCGAbiolinks and cBioPortal 0 Hi, I have a specific question regarding whether STAR counts of TCGA-BRCA data downloaded from GDCquery is batch corrected. If not, I would like to correct this batch before performing differential expression analysis with Voom. An alternative is to analyse…

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Pan-cancer structurome reveals overrepresentation of beta sandwiches and underrepresentation of alpha helical domains

AlphaFold models significantly expanded structural space of over and underexpressed protein-coding genes in 21 cancer types Our non-redundant sets of over and underexpressed protein-coding genes for all cancer types include 5341 and 7320 genes, respectively. Figure 1A, B illustrates the availability of known 3D structures (PDB) and AlphaFold models (AF). For…

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TCGA-BRCA dataset for detecting tumor cells – Image Analysis

Machine learning models are heavily dependent on the quality of the data you feed them. As I am a newcomer to Image Analysis, I haven’t had the time to digest what has already been done in the direction of data collection and annotation. I am interested in studying trastuzumab (anti-HER2…

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Unable to create pd file for ChAMP analysis

Unable to create pd file for ChAMP analysis 0 I have downloaded TCGA-BRCA DNA methylation (of data type: Methylation Beta Value) of 895 samples (txt files, not idat files) from GDC portal. That csv file contains CpG probe Ids as rownames and TCGA Ids as column names. I wanted to…

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Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence

Ethics statement All experiments were conducted in accordance with the Declaration of Helsinki and the study was approved by the University of Chicago Institutional Review Board, IRB 22-0707. For model training, patients were included from the TCGA breast cancer cohort (BRCA)26. For validation, anonymized archival tissue samples were retrieved from…

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TCGA-BRCA tissue and RNAseq problem

TCGA-BRCA tissue and RNAseq problem 0 I’m trying to analyze RNAseq data from the TCGA-BRCA project. I’ve downloaded the STAR count tsv files as well as the clinical and sample manifests/metadata. The problem is that there is no column entry in the “clinical.tsv” which indicates tissue sample type so as…

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Initial Genomic Analysis of BRCA Prospective

CPTAC3-BRCA-TP: WXS Copy number analysis (GISTIC2) CPTAC3-BRCA-TP: Mutation Significance Analysis (MutSig2CV) CPTAC3-BRCA-TP: Mutation Signature Analysis (SignatureAnalyzer) CPTAC3-BRCA-TP: Mutation Signature Analysis Reduced Hyper Mutant Effect (SignatureAnalyzer) CPTAC3-BRCA-noHyper: Mutation Significance Analysis (MutSig2CV) CPTAC3-BRCA-noHyper: Mutation Signature Analysis (SignatureAnalyzer) CPTAC3-BRCA-noHyper: Mutation Signature Analysis Reduced Hyper Mutant Effect (SignatureAnalyzer) CPTAC3-BRCA-BasalTNBC: WXS Copy number analysis (GISTIC2)…

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Mapping IDs and file names from TCGA datasets

Mapping IDs and file names from TCGA datasets 1 Hello, I want to analyze multiple files from the TCGA-BRCA project downloaded from the GDC portal. However, I have some difficulty using different data from the same samples. For example, a case ID TCGA-E2-A1IU has proteome profiling and DNA methylation data….

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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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Translating transcriptomic findings from cancer model systems to humans through joint dimension reduction

AJIVE integration captures biologically joint-acting and cohort-specific variation AJIVE is an extension of JIVE that employs the use of thresholded Singular Value Decomposition (SVD) to create low-dimension approximations of input data matrices (Fig. 1). Then, through utilizing Principal Angle Analysis, identifies low-dimension subspace bases that either share variation structure (Joint) or…

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Enolase-1 & prognosis & immune infiltration in breast cancer

Introduction Breast cancer is the most prevalent malignancy and the leading cause of cancer death in women worldwide.1 After its diagnosis, the most immediate challenge is to tailor treatment strategies and predict the prognosis; traditional clinicopathologic features, including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2…

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TCGAbiolinks HT-Seq count

TCGAbiolinks HT-Seq count 0 Hi everyone! For legacy = F data, the workflow_type = HTSeq counts is still working? This is my code and it fails: query <- GDCquery(project = “TCGA-BRCA”, legacy = FALSE, data.category = “Transcriptome Profiling”, data.type = “Gene Expression Quantification”, workflow.type=”HTSeq – Counts”, sample.type = “Primary Tumor”)…

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Subtype and cell type specific expression of lncRNAs provide insight into breast cancer

lncRNA expression according to breast cancer clinicopathological subtypes To identify lncRNAs expressed by specific breast cancer subtypes or associated with clinicopathological features, we analyzed RNA-sequencing data from two large independent breast cancer cohorts: SCAN-B (n = 3455)17 and TCGA-BRCA (n = 1095). We focused on lncRNAs annotated in the Ensembl18 v93 non-coding reference transcriptome…

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Molecular analysis of TCGA breast cancer histologic types

Breast cancer is classified into multiple distinct histologic types, and many of the rarer types have limited characterization. Here, we extend The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset with additional histologic type annotations, in a total of 1063 breast cancers. We analyze this extended dataset to define transcriptomic and…

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HTSeq Counts no longer available

HTSeq Counts no longer available 1 @vm-21340 Last seen 8 hours ago Brazil I’m working with breast cancer expression data from the TCGA-BRCA project. All my scripts were written to retrieve HTSeq counts from GDC, but they seem to have been removed from the GDC Data Portal. When using GDCquery,…

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Genomic and epigenomic alterations of the basal-like prognostic biomarkers.

a) Heatmap with dendrogram representing the unsupervised hierarchical clustering analysis based on CNVs data of TCGA-BRCA patients. The rows in the heatmap represent the 11 basal-like prognostic biomarkers. The columns correspond to basal-like and luminal A TCGA-BRCA patients: basal-like are indicated in dark blue and luminal A in green. The…

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Index of /runs/gdc/report_2018_02_16

Name Last modified Size Description Parent Directory   –   TCGA-ACC.2018_02_16.diced_metadata.tsv 2018-02-16 01:06 250K   TCGA-ACC.2018_02_16.high_res.heatmap.png 2018-02-16 01:08 73K   TCGA-ACC.2018_02_16.low_res.heatmap.png 2018-02-16 01:08 37K   TCGA-ACC.2018_02_16.sample_counts.tsv 2018-02-16 01:06 142   TCGA-BLCA.2018_02_16.diced_metadata.tsv 2018-02-16 01:06 1.2M   TCGA-BLCA.2018_02_16.high_res.heatmap.png 2018-02-16 01:08 90K   TCGA-BLCA.2018_02_16.low_res.heatmap.png 2018-02-16 01:08 53K   TCGA-BLCA.2018_02_16.sample_counts.tsv 2018-02-16 01:06 201  …

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Overall survival and Event free survival

Overall survival and Event free survival 0 Hello everyone. I have a simple question. How do I calculate the overall survival (OS) rate and Event-free survival (EFS) using TCGA-BRCA clinical data? which column must be used for each one? Also, can I use the first quartile as my risk score…

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Differential expression analysis of TCGA data based on tumor staging

Hi everyone I wanted to analyze TCGA-BRCA data for identifying DEGs in different TNM stages (I to IV) between Normal and Tumor. How to change the following code to get the DEGs based on the staging? CancerProject <- “TCGA-BRCA” DataDirectory <- paste0(“../GDC/”,gsub(“-“,”_”,CancerProject)) FileNameData <- paste0(DataDirectory, “_”,”HTSeq_Counts”,”.rda”) query <- GDCquery(project =…

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Survival Analysis Cut-off

Survival Analysis Cut-off 0 Hello guys, I am doing a survival analysis using TCGA-BRCA project data. I am trying different cut-offs to separate my samples into high and low risk groups, but since it is my first time I would like to ask a question just to be fully sure…

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