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244 k microarray data  (Agilent technologies)


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    Structured Review

    Agilent technologies 244 k microarray data
    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.
    244 K Microarray Data, supplied by Agilent technologies, 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/pmc09968332-141-17-14
    Average 90 stars, based on 1 article reviews
    244 k microarray data - by Bioz Stars, 2026-10
    90/100 stars

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    1) Product Images from "Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously"

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

    Journal: Communications Biology

    doi: 10.1038/s42003-023-04588-6

    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.
    Figure Legend 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.

    Techniques Used: Microarray, RNA Sequencing Assay, Mutagenesis, Expressing

    Related Articles

    Expressing:

    Article Title: A comprehensive meta-analysis of transcriptome data to identify signature genes associated with pancreatic ductal adenocarcinoma
    Article Snippet: Affymetrix datasets were preprocessed and normalized with the Robust Multi-Array Average (RMA) approach [ ] using Expression Console (Affymetrix, Santa Clara, CA, USA). .. The expression values of Agilent microarray data were normalized using the Loess algorithm. ..

    Microarray:

    Article Title: A comprehensive meta-analysis of transcriptome data to identify signature genes associated with pancreatic ductal adenocarcinoma
    Article Snippet: Affymetrix datasets were preprocessed and normalized with the Robust Multi-Array Average (RMA) approach [ ] using Expression Console (Affymetrix, Santa Clara, CA, USA). .. The expression values of Agilent microarray data were normalized using the Loess algorithm. ..

    Article Title: A transcriptomics approach to expand therapeutic options and optimize clinical trials in oncology
    Article Snippet: .. The dataset used in our in silico analysis consists of Agilent microarray data generated from tumor and analogous organ-matched normal lung tissues from each patient. ..

    Article Title: Detection of Expressional Changes Induced by Intrauterine Growth Restriction in the Developing Rat Mammary Gland via Exploratory Pathways Analysis
    Article Snippet: .. Using IPA we were able to compare our Agilent microarray data with the current Ingenuity Pathways Knowledge Base (IPKB) (as of 05/13/2013). ..

    Article Title: Profound Effect of Profiling Platform and Normalization Strategy on Detection of Differentially Expressed MicroRNAs – A Comparative Study
    Article Snippet: .. All Agilent microarray data were MIAME compliant and were registered into ArrayExpress database , a publicly available repository consistent with the MIAME guidelines. ..

    Article Title: The Homeobox Gene MEIS1 Is Methylated in BRAF p.V600E Mutated Colon Tumors
    Article Snippet: .. Agilent microarray data of paired tumor and normal tissue were processed in R2.10.0 (Bioconductor), as previously described [ ]. ..

    Article Title: Suppressive stroma-immune prognostic signature impedes immunotherapy in ovarian cancer and can be reversed by PDGFRB inhibitors
    Article Snippet: .. The microarray data of the Agilent ovarian cancer cohort were extracted from Gene Expression Omnibus (GSE53963, GSE73614, GSE17260, GSE32062, GSE32063). ..

    Article Title: Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously
    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 . .. For GBM (150 pairs of matched samples) we obtained Affymetrix HT Human Genome U133A Array data from refine.bio (GSE83130) normalized by the SCAN method , .

    Article Title: Integrating Multi-omics Data with EHR for Precision Medicine Using Advanced Artificial Intelligence
    Article Snippet: .. The group integrated RNAseq data and Agilent microarray data as genomic data to uncover the predictive power of MRI scans to four immune subsets for glioma. ..

    In Silico:

    Article Title: A transcriptomics approach to expand therapeutic options and optimize clinical trials in oncology
    Article Snippet: .. The dataset used in our in silico analysis consists of Agilent microarray data generated from tumor and analogous organ-matched normal lung tissues from each patient. ..

    Generated:

    Article Title: A transcriptomics approach to expand therapeutic options and optimize clinical trials in oncology
    Article Snippet: .. The dataset used in our in silico analysis consists of Agilent microarray data generated from tumor and analogous organ-matched normal lung tissues from each patient. ..

    Indirect Immunoperoxidase Assay:

    Article Title: Detection of Expressional Changes Induced by Intrauterine Growth Restriction in the Developing Rat Mammary Gland via Exploratory Pathways Analysis
    Article Snippet: .. Using IPA we were able to compare our Agilent microarray data with the current Ingenuity Pathways Knowledge Base (IPKB) (as of 05/13/2013). ..

    Gene Expression:

    Article Title: Suppressive stroma-immune prognostic signature impedes immunotherapy in ovarian cancer and can be reversed by PDGFRB inhibitors
    Article Snippet: .. The microarray data of the Agilent ovarian cancer cohort were extracted from Gene Expression Omnibus (GSE53963, GSE73614, GSE17260, GSE32062, GSE32063). ..

    Transformation Assay:

    Article Title: Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously
    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 . .. For GBM (150 pairs of matched samples) we obtained Affymetrix HT Human Genome U133A Array data from refine.bio (GSE83130) normalized by the SCAN method , .

    RNA Sequencing:

    Article Title: Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously
    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 . .. For GBM (150 pairs of matched samples) we obtained Affymetrix HT Human Genome U133A Array data from refine.bio (GSE83130) normalized by the SCAN method , .

    Magnetic Resonance Imaging:

    Article Title: Integrating Multi-omics Data with EHR for Precision Medicine Using Advanced Artificial Intelligence
    Article Snippet: .. The group integrated RNAseq data and Agilent microarray data as genomic data to uncover the predictive power of MRI scans to four immune subsets for glioma. ..



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    Agilent technologies 244 k microarray data
    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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    Image Search Results


    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