MAQC-III/SEQC Main Study ABCD titration experiment, ERCC spike-in profiles and gene-level estimates for a selected test set
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE48016 | GEO | 2015/04/03
SECONDARY ACCESSION(S): PRJNA208822
REPOSITORIES: GEO
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Project description:Comparison to RNA-Seq showed different strengths and weaknesses for different regimes of expression strength. At sufficient read-depth, both platforms seemed comparable for gene-level expression profiling. Comparison to RNA-Seq showed comparable accuracy and precision with microarrays for ERCC spike-ins at medium to high expression levels. At low expression levels, the array showed signal attenuation but better precision, while RNA-Seq maintained accuracy albeit with highly inflated variance. In summary, both platforms can be meaningfully applied in a similar range of expression strength. Assessment of (precision, accuracy, reproducibility, mutual information, titration order consistency, and known mixing ratio recovery) by measurement of known-ratio mixtures of two RNA reference samples. The array probes 776 complex genes representative of the AceView gene model annotation, as well as 92 ERCC spike-in controls.
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Project description:We present a more extensive and yet precise assessment to elucidate differences and similarities in performance at numerous aspects including signal range, sensitivity to fold-change, and fidelity with TaqMan qRT-PCR. There were three levels of data examined: entire data sets, data derived from gene name annotation oriented subset of 15442 RefSeq genes, and data derived from transcript pattern defined subset of 7034 RefSeq genes. Our results showed a fair degree of overall correlation between all 6 platforms evaluated; but, to varying degrees, two RNA-seq protocols outperformed three of the microarray platforms in most categories. Notably, a fourth microarray platform, Agilent, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments. Furthermore, 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80% concordance with the gold standard TaqMan assay in terms of fold-change accuracy. Our study suggests that the use of transcript patterns can enhance a number of the observed cross-platform correlations, indicating a potential usefulness for similar evaluations.
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Project description:We present a more extensive and yet precise assessment to elucidate differences and similarities in performance at numerous aspects including signal range, sensitivity to fold-change, and fidelity with TaqMan qRT-PCR. There were three levels of data examined: entire data sets, data derived from gene name annotation oriented subset of 15442 RefSeq genes, and data derived from transcript pattern defined subset of 7034 RefSeq genes. Our results showed a fair degree of overall correlation between all 6 platforms evaluated; but, to varying degrees, two RNA-seq protocols outperformed three of the microarray platforms in most categories. Notably, a fourth microarray platform, Agilent, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments. Furthermore, 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80% concordance with the gold standard TaqMan assay in terms of fold-change accuracy. Our study suggests that the use of transcript patterns can enhance a number of the observed cross-platform correlations, indicating a potential usefulness for similar evaluations.
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Project description:We present a more extensive and yet precise assessment to elucidate differences and similarities in performance at numerous aspects including signal range, sensitivity to fold-change, and fidelity with TaqMan qRT-PCR. There were three levels of data examined: entire data sets, data derived from gene name annotation oriented subset of 15442 RefSeq genes, and data derived from transcript pattern defined subset of 7034 RefSeq genes. Our results showed a fair degree of overall correlation between all 6 platforms evaluated; but, to varying degrees, two RNA-seq protocols outperformed three of the microarray platforms in most categories. Notably, a fourth microarray platform, Agilent, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments. Furthermore, 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80% concordance with the gold standard TaqMan assay in terms of fold-change accuracy. Our study suggests that the use of transcript patterns can enhance a number of the observed cross-platform correlations, indicating a potential usefulness for similar evaluations.
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Project description:We present a more extensive and yet precise assessment to elucidate differences and similarities in performance at numerous aspects including signal range, sensitivity to fold-change, and fidelity with TaqMan qRT-PCR. There were three levels of data examined: entire data sets, data derived from gene name annotation oriented subset of 15442 RefSeq genes, and data derived from transcript pattern defined subset of 7034 RefSeq genes. Our results showed a fair degree of overall correlation between all 6 platforms evaluated; but, to varying degrees, two RNA-seq protocols outperformed three of the microarray platforms in most categories. Notably, a fourth microarray platform, Agilent, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments. Furthermore, 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80% concordance with the gold standard TaqMan assay in terms of fold-change accuracy. Our study suggests that the use of transcript patterns can enhance a number of the observed cross-platform correlations, indicating a potential usefulness for similar evaluations.
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Project description:We present a more extensive and yet precise assessment to elucidate differences and similarities in performance at numerous aspects including signal range, sensitivity to fold-change, and fidelity with TaqMan qRT-PCR. There were three levels of data examined: entire data sets, data derived from gene name annotation oriented subset of 15442 RefSeq genes, and data derived from transcript pattern defined subset of 7034 RefSeq genes. Our results showed a fair degree of overall correlation between all 6 platforms evaluated; but, to varying degrees, two RNA-seq protocols outperformed three of the microarray platforms in most categories. Notably, a fourth microarray platform, Agilent, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments. Furthermore, 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80% concordance with the gold standard TaqMan assay in terms of fold-change accuracy. Our study suggests that the use of transcript patterns can enhance a number of the observed cross-platform correlations, indicating a potential usefulness for similar evaluations.
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Project description:We present a more extensive and yet precise assessment to elucidate differences and similarities in performance at numerous aspects including signal range, sensitivity to fold-change, and fidelity with TaqMan qRT-PCR. There were three levels of data examined: entire data sets, data derived from gene name annotation oriented subset of 15442 RefSeq genes, and data derived from transcript pattern defined subset of 7034 RefSeq genes. Our results showed a fair degree of overall correlation between all 6 platforms evaluated; but, to varying degrees, two RNA-seq protocols outperformed three of the microarray platforms in most categories. Notably, a fourth microarray platform, Agilent, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments. Furthermore, 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80% concordance with the gold standard TaqMan assay in terms of fold-change accuracy. Our study suggests that the use of transcript patterns can enhance a number of the observed cross-platform correlations, indicating a potential usefulness for similar evaluations.
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Project description:Microarray technology has had a profound impact on gene expression research. Some studies have questioned whether similar expression results are obtained when the same RNA samples are analyzed on different platforms. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and analysis issues. We demonstrate the consistency of results within a platform across test sites as well as the high level of cross-platform concordance in terms of genes identified as differentially expressed. The MAQC study provides a rich resource that will help build consensus on the use of microarrays in research, clinical and regulatory settings. Manuscripts related to the MAQC project have been published in Nature Biotechnology, 24(9), September, 2006. More information about the MAQC project can be found at http://edkb.fda.gov/MAQC/. Keywords: Cross-platform comparison
2006-09-08 | GSE5350 | GEO
Project description:The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and analysis issues. We demonstrate the consistency of results within a platform across test sites as well as the high level of cross-platform concordance in terms of genes identified as differentially expressed. The MAQC study provides a rich resource that will help build consensus on the use of microarrays in research, clinical and regulatory settings. Manuscripts related to the MAQC project have been published in Nature Biotechnology, 24(9), September, 2006. More information about the MAQC project can be found at http://edkb.fda.gov/MAQC/.<br><br>Expression data from two distinct reference RNA samples (A and B) in four titration pools were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Sample A = Stratagene Universal Human Reference RNA (UHRR, Catalog #740000), Sample B = Ambion Human Brain Reference RNA (HBRR, Catalog #6050), Sample C = Samples A and B mixed at 75%:25% ratio (A:B); and Sample D = Samples A and B mixed at 25%:75% ratio (A:B). In general, each microarray platform was tested at three sites and each sample was tested in five replicates at each test site. Samples (hybridizations) were named according to the following convention: Platform_Testsite_SampleRelicate. For example, AFX_2_B1 represents the hybridization (array) from platform AFX processed by test site 2 for the first replicate of sample B. Assignment of platform code: ABI = Applied Biosystems (microarray); AFX = Affymetrix; AG1 = Agilent one-color; AGL = Agilent two-color; GEH = GE Healthcare; ILM = Illumina; NCI = NCI two-color (Operon oligos); EPP = Eppendorf; TAQ = TaqMan (Applied Biosystems); QGN = QuantiGene (Panomics); GEX = StaRT-PCR (Gene Express); H25K = TeleChem two-color; H25K1 = TeleChem one-color; BIO = CapitalBio two-color (Operon oligos); BIO1 = CapitalBio one-color (Operon oligos); OPN = Operon two-color (Operon oligos); NMC = Norwegian Microarray Consortium two-color (Operon oligos).
2006-12-04 | E-TABM-132 | biostudies-arrayexpress