Cross-platform prediction of gene expression signatures.
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE50051 | GEO | 2013/08/21
SECONDARY ACCESSION(S): PRJNA215871
REPOSITORIES: GEO
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Project description:We developed a method to convert gene expression signatures across the Illumina and Affymetrix platforms. We generated an E2F1 signature. We processed RNA for E2F1, MYC, and Ras signatures on the Illumina platform. 16 Melanoma samples are also included for validation.
2013-08-21 | E-GEOD-50051 | biostudies-arrayexpress
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels.
2014-08-18 | GSE53084 | GEO
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels.
2014-08-18 | GSE53083 | GEO
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels.
2014-08-18 | GSE53082 | GEO
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels. Male Wistar rats were treated by oral gavage with the eight nongenotoxic hepatocarcinogens Phenobarbital sodium (PB), Piperonylbutoxide (PBO), Dehydroepiandrosterone (DHEA), Acetamide (AA), Methapyrilene HCl (MPy), Methylcarbamate (Mcarb), Diethylstilbestrol (DES) and Ethionine (ETH), the two genotoxic carcinogens C.I Direct Black (CIDB) and dimethylnitrosamine (DMN), the two non-hepatocarcinogens Cefuroxime (CFX) and Nifedipine (Nif), and the three compounds with undefined carcinogenic class Cyproterone acetate (CPA), Thioacetamid (TAA) and Wy-14643 (Wy). Depending on the administered compound, livers were taken after 3, 7, or 14 days for histopathological evaluation. From the five animals per treatment group three animals were selected based on the histopathological findings and subjected to molecular profiling using Affymetrix RG-230A arrays (mRNA expression), Agilent G4473A arrays (miRNA expression) and Zeptosens ZeptoMARK reverse arrays (protein expression).
2014-08-18 | E-GEOD-53083 | biostudies-arrayexpress
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels. Male Wistar rats were treated by oral gavage with the eight nongenotoxic hepatocarcinogens Phenobarbital sodium (PB), Piperonylbutoxide (PBO), Dehydroepiandrosterone (DHEA), Acetamide (AA), Methapyrilene HCl (MPy), Methylcarbamate (Mcarb), Diethylstilbestrol (DES) and Ethionine (ETH), the two genotoxic carcinogens C.I Direct Black (CIDB) and dimethylnitrosamine (DMN), the two non-hepatocarcinogens Cefuroxime (CFX) and Nifedipine (Nif), and the three compounds with undefined carcinogenic class Cyproterone acetate (CPA), Thioacetamid (TAA) and Wy-14643 (Wy). Depending on the administered compound, livers were taken after 3, 7, or 14 days for histopathological evaluation. From the five animals per treatment group three animals were selected based on the histopathological findings and subjected to molecular profiling using Affymetrix RG-230A arrays (mRNA expression), Agilent G4473A arrays (miRNA expression) and Zeptosens ZeptoMARK reverse arrays (protein expression).
2014-08-18 | E-GEOD-53082 | biostudies-arrayexpress
Project description:In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels. Male Wistar rats were treated by oral gavage with the eight nongenotoxic hepatocarcinogens Phenobarbital sodium (PB), Piperonylbutoxide (PBO), Dehydroepiandrosterone (DHEA), Acetamide (AA), Methapyrilene HCl (MPy), Methylcarbamate (Mcarb), Diethylstilbestrol (DES) and Ethionine (ETH), the two genotoxic carcinogens C.I Direct Black (CIDB) and dimethylnitrosamine (DMN), the two non-hepatocarcinogens Cefuroxime (CFX) and Nifedipine (Nif), and the three compounds with undefined carcinogenic class Cyproterone acetate (CPA), Thioacetamid (TAA) and Wy-14643 (Wy). Depending on the administered compound, livers were taken after 3, 7, or 14 days for histopathological evaluation. From the five animals per treatment group three animals were selected based on the histopathological findings and subjected to molecular profiling using Affymetrix RG-230A arrays (mRNA expression), Agilent G4473A arrays (miRNA expression) and Zeptosens ZeptoMARK reverse arrays (protein expression).
2014-08-18 | E-GEOD-53084 | biostudies-arrayexpress
Project description:Gene expression microarrays have made a profound impact in biomedical research. The diversity of platforms and analytical methods has made comparison of data from multiple platforms very challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and in house platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by QRT-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent pre-processing, commercial arrays were more consistent than in-house arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms. This SuperSeries is composed of the SubSeries listed below.
2006-06-01 | GSE4854 | GEO
Project description:Gene expression signatures can predict the activation of oncogenic pathways and other phenotypes of interest via quantitative models that combine the expression levels of multiple genes. However, as the number of platforms to measure genome-wide gene expression proliferates, there is an increasing need to develop models that can be ported across diverse platforms. Because of the range of technologies that measure gene expression, the resulting signal values can vary greatly. To understand how this variation can affect the prediction of gene expression signatures, we have investigated the ability of gene expression signatures to predict pathway activation across Affymetrix and Illumina microarrays. We hybridized the same RNA samples to both platforms and compared the resultant gene expression readings, as well as the signature predictions. Using a new approach to map probes across platforms, we found that the genes in the signatures from the two platforms were highly similar, and that the predictions they generated were also strongly correlated. This demonstrates that our method can map probes from Affymetrix and Illumina microarrays, and that this mapping can be used to predict gene expression signatures across platforms.
| S-EPMC3828325 | biostudies-literature
Project description:Microarray expression profiling has become a valuable tool in the evaluation of the genetic consequences of metabolic disease. Although 3’-biased gene expression microarray platforms were the first generation to have widespread availability, newer platforms are gradually emerging that have more up-to-date content and/or higher cost efficiency. Deciphering the relative strengths and weaknesses of these various platforms for metabolic pathway level analyses can be daunting. We sought to determine the practical strengths and weaknesses of four leading commercially-available expression array platforms relative to biologic investigations, as well as assess the feasibility of cross-platform data integration for purposes of biochemical pathway analyses. METHODS: Liver RNA from B6.Alb/cre,Pdss2loxP/loxP mice having primary Coenzyme Q deficiency was extracted either at baseline or following treatment with an antioxidant/antihyperlipidemic agent, probucol. Target RNA samples were prepared and hybridized to Affymetrix 430 2.0, Affymetrix Gene 1.0 ST, Affymetrix Exon 1.0 ST, and Illumina Mouse WG-6 expression arrays. Probes on all platforms were re-mapped to coding sequences in the current version of the mouse genome. Data processing and statistical analysis were performed by R/Bioconductor functions, and pathway analyses were carried out by KEGG Atlas and GSEA. RESULTS: Expression measurements were generally consistent across platforms. However, intensive probe-level comparison suggested that differences in probe locations were a major source of inter-platform variance. In addition, genes expressed at low or intermediate levels had lower inter-platform reproducibility than highly expressed genes. All platforms showed similar patterns of differential expression between sample groups, with ‘steroid biosynthesis’ consistently identified as the most down-regulated metabolic pathway by probucol treatment. CONCLUSIONS: This work offers a timely guide for metabolic disease investigators to enable informed end-user decisions regarding choice of expression microarray platform best-suited to specific research project goals. Successful cross-platform integration of biochemical pathway expression data is also demonstrated, especially for well-annotated and highly-expressed genes. However, integration of gene-level expression data is limited by individual platform probe design and the expression level of target genes. Cross-platform analyses of biochemical pathway data will require additional data processing and novel computational bioinformatics tools to address unique statistical challenges.
2010-01-11 | GSE18677 | GEO