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gene expression data tcga brca  (Broad Institute Inc)

 
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    Broad Institute Inc gene expression data tcga brca
    Gene Expression Data Tcga Brca, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/244+k+microarray+data/tcga+gene+expression+data/pm36309544-337-5-19
    Average 90 stars, based on 1 article reviews
    gene expression data tcga brca - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    Gene Expression:

    Article Title: Clinical relevance of CERK and SPHK1 in breast cancer and their association with metastasis and drug resistance.
    Article Snippet: .. Gene expression data pertaining to breast invasive carcinoma (TCGA abbreviation BRCA ) was retrieved from the Firehose portal of Broad Institute (gdac.broadinstitute.org); this dataset comprises lowest-normalized level-3 microarray data (Agilent 244 K [G4502A]) from 529 tumor and 61 adjacent normal tissues. ..

    Article Title: Sialylated glycoproteins suppress immune cell killing by binding to Siglec-7 and Siglec-9 in prostate cancer.
    Article Snippet: .. TCGA gene expression data from primary tumors and matched adjacent normal tissues of 52 patients diagnosed with prostate adenocarcinoma (PRAD) were obtained from the Broad Institute. ..

    Article Title: Sialylated glycoproteins suppress immune cell killing by binding to Siglec-7 and Siglec-9 in prostate cancer
    Article Snippet: .. TCGA gene expression data from primary tumors and matched adjacent normal tissues of 52 patients diagnosed with PRAD were obtained from the Broad Institute. ..

    Article Title: Molecular biomarkers for cancer immunotherapy
    Article Snippet: .. RNA-Seq-Based Gene Expression Data TCGA gene expression data (90% tumor biopsies, 10% solid tissue controls) were obtained through GDAC Firehose (Broad Institute TCGA Genome Data Analysis Center, 2014) and included all available “Level_3” gene-level data (a mix of Illumina HiSeq and Illumina GA data). ..

    Microarray:

    Article Title: Clinical relevance of CERK and SPHK1 in breast cancer and their association with metastasis and drug resistance.
    Article Snippet: .. Gene expression data pertaining to breast invasive carcinoma (TCGA abbreviation BRCA ) was retrieved from the Firehose portal of Broad Institute (gdac.broadinstitute.org); this dataset comprises lowest-normalized level-3 microarray data (Agilent 244 K [G4502A]) from 529 tumor and 61 adjacent normal tissues. ..

    other:

    Article Title: Clinical relevance of CERK and SPHK1 in breast cancer and their association with metastasis and drug resistance.
    Article Snippet: A PPI network comprising CERK and SPHK1, metastasis markers and drug resistance markers was inferred by integrating physical PPIs and gene co-expression regulations among candidates as well as their corresponding interacting partners; physical interactions were obtained from the STRING database (string-db.org) while gene co-expression regulations were identified by processing gene expression data (BRCA-TCGA) obtained from the Broad Institute (gdac.broadinstitute.org).

    Article Title: TRF2 interaction with nuclear envelope is required for cell polarization and metastasis in triple negative breast cancer
    Article Snippet: Normalized TCGA-BRCA gene expression of tumor samples were obtained from Broad Institute TCGA Genome Data Analysis Center ( http://gdac.broadinstitute.org/ ): Firehose stddata__2016_01_28.

    Article Title: TRF2 interaction with nuclear envelope is required for cell polarization and metastasis in triple negative breast cancer.
    Article Snippet: TCGA dataset and bioinformatics analysis Normalized TCGA-BRCA gene expression of tumor samples were obtained from Broad Institute TCGA Genome Data Analysis Center (http:// gdac.broadinstitute.org/): Firehose stddata__2016_01_28.

    Article Title: Clinical relevance of CERK and SPHK1 in breast cancer and their association with metastasis and drug resistance
    Article Snippet: A PPI network comprising CERK and SPHK1 , metastasis markers and drug resistance markers was inferred by integrating physical PPIs and gene co-expression regulations among candidates as well as their corresponding interacting partners; physical interactions were obtained from the STRING database (string-db.org) while gene co-expression regulations were identified by processing gene expression data (BRCA-TCGA) obtained from the Broad Institute (gdac.broadinstitute.org).



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    a TCGA matched samples (520 from BRCA, 150 from GBM) run on both <t>microarray</t> and RNA-seq were split into a training set (2/3) and test set (1/3). b RNA-seq samples were titrated into each training set, 10% at a time (0–100%), resulting in eleven training sets for each normalization method. Each RNA-seq sample replaces its matched microarray sample. Cross-platform normalization methods were applied to each training set independently. c We used three supervised algorithms to train classifiers (molecular subtype and mutation status of TP53 and PIK3CA in both BRCA and GBM) on each training set and tested on the microarray and RNA-seq test sets. The test sets were projected onto and back out of the training set space using unsupervised Principal Components Analysis to obtain reconstructed test sets. The subtype classifiers trained in step 3A were used to predict on the reconstructed test sets. Pathways regulating gene expression were identified using the unsupervised method PLIER.
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    244 k (g4502a) microarray data  (Agilent technologies)
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    a TCGA matched samples (520 from BRCA, 150 from GBM) run on both <t>microarray</t> and RNA-seq were split into a training set (2/3) and test set (1/3). b RNA-seq samples were titrated into each training set, 10% at a time (0–100%), resulting in eleven training sets for each normalization method. Each RNA-seq sample replaces its matched microarray sample. Cross-platform normalization methods were applied to each training set independently. c We used three supervised algorithms to train classifiers (molecular subtype and mutation status of TP53 and PIK3CA in both BRCA and GBM) on each training set and tested on the microarray and RNA-seq test sets. The test sets were projected onto and back out of the training set space using unsupervised Principal Components Analysis to obtain reconstructed test sets. The subtype classifiers trained in step 3A were used to predict on the reconstructed test sets. Pathways regulating gene expression were identified using the unsupervised method PLIER.
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    a TCGA matched samples (520 from BRCA, 150 from GBM) run on both microarray and RNA-seq were split into a training set (2/3) and test set (1/3). b RNA-seq samples were titrated into each training set, 10% at a time (0–100%), resulting in eleven training sets for each normalization method. Each RNA-seq sample replaces its matched microarray sample. Cross-platform normalization methods were applied to each training set independently. c We used three supervised algorithms to train classifiers (molecular subtype and mutation status of TP53 and PIK3CA in both BRCA and GBM) on each training set and tested on the microarray and RNA-seq test sets. The test sets were projected onto and back out of the training set space using unsupervised Principal Components Analysis to obtain reconstructed test sets. The subtype classifiers trained in step 3A were used to predict on the reconstructed test sets. Pathways regulating gene expression were identified using the unsupervised method PLIER.

    Journal: Communications Biology

    Article Title: Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously

    doi: 10.1038/s42003-023-04588-6

    Figure Lengend Snippet: a TCGA matched samples (520 from BRCA, 150 from GBM) run on both microarray and RNA-seq were split into a training set (2/3) and test set (1/3). b RNA-seq samples were titrated into each training set, 10% at a time (0–100%), resulting in eleven training sets for each normalization method. Each RNA-seq sample replaces its matched microarray sample. Cross-platform normalization methods were applied to each training set independently. c We used three supervised algorithms to train classifiers (molecular subtype and mutation status of TP53 and PIK3CA in both BRCA and GBM) on each training set and tested on the microarray and RNA-seq test sets. The test sets were projected onto and back out of the training set space using unsupervised Principal Components Analysis to obtain reconstructed test sets. The subtype classifiers trained in step 3A were used to predict on the reconstructed test sets. Pathways regulating gene expression were identified using the unsupervised method PLIER.

    Article Snippet: For BRCA (520 pairs of matched samples), we used log 2 -transformed, lowess normalized Agilent 244 K microarray data and RSEM (RNA-seq by Expectation Maximization) gene-level count RNA-seq data .

    Techniques: Microarray, RNA Sequencing Assay, Mutagenesis, Expressing