Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Cross-platform comparability of microarray data

ABSTRACT: To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms and methods to accommodate this variability. RNA expression data were generated in seven laboratories, comparing two standard RNA samples using twelve microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for most platforms within any laboratory was typically good, but reproducibility between platforms and across laboratories was generally poor. Reproducibility between laboratories dramatically increased when standardized protocols were implemented for RNA labeling, hybridization, microarray processing, data acquisition and data normalization. Nonetheless, concordance could be found across different laboratories and platforms when data were analyzed in terms of enriched Gene Ontology categories. These findings indicate that microarray results generated by multiple sites and platforms can be comparable, and that multi-investigator teams will maximize data comparability by adopting a common platform and a common set of procedures to generate compatible data. Keywords: other

ORGANISM(S): Mus musculus

SUBMITTER: Ivan Rusyn

PROVIDER: E-GEOD-2458 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Standardizing global gene expression analysis between laboratories and across platforms.

Bammler Theodore T Beyer Richard P RP Bhattacharya Sanchita S Boorman Gary A GA Boyles Abee A Bradford Blair U BU Bumgarner Roger E RE Bushel Pierre R PR Chaturvedi Kabir K Choi Dongseok D Cunningham Michael L ML Deng Shibing S Dressman Holly K HK Fannin Rickie D RD Farin Fredrico M FM Freedman Jonathan H JH Fry Rebecca C RC Harper Angel A Humble Michael C MC Hurban Patrick P Kavanagh Terrance J TJ Kaufmann William K WK Kerr Kathleen F KF Jing Li L Lapidus Jodi A JA Lasarev Michael R MR Li Jianying J Li Yi-Ju YJ Lobenhofer Edward K EK Lu Xinfang X Malek Renae L RL Milton Sean S Nagalla Srinivasa R SR O'malley Jean P JP Palmer Valerie S VS Pattee Patrick P Paules Richard S RS Perou Charles M CM Phillips Ken K Qin Li-Xuan LX Qiu Yang Y Quigley Sean D SD Rodland Matthew M Rusyn Ivan I Samson Leona D LD Schwartz David A DA Shi Yan Y Shin Jung-Lim JL Sieber Stella O SO Slifer Susan S Speer Marcy C MC Spencer Peter S PS Sproles Dean I DI Swenberg James A JA Suk William A WA Sullivan Robert C RC Tian Ru R Tennant Raymond W RW Todd Signe A SA Tucker Charles J CJ Van Houten Bennett B Weis Brenda K BK Xuan Shirley S Zarbl Helmut H

Nature methods 20050421 5

To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms, and methods to accommodate this variability. RNA expression data were generated in seven laboratories, which compared two standard RNA samples using 12 microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for ...[more]

PMID: 15846362

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Project description:Leiomyosarcoma (LMS) is a soft tissue tumor with a significant degree of morphologic and molecular heterogeneity. We employed integrative molecular profiling to discover and characterize molecular subtypes of LMS. Gene expression profiling was performed on 51 LMS samples. Unsupervised clustering demonstrated 3 reproducible LMS clusters. Array comparative genomic hybridization (aCGH) was performed on 20 LMS samples and demonstrated that the molecular subtypes defined by gene-expression showed distinct genomic changes. Tumors from the "muscle-enriched" cluster showed significantly increased copy number changes (p=0.04). Most muscle-enriched cases showed loss at 16q24 which contains FANCA, known to play an important role in DNA repair, and loss at 1p36 which contains PRDM16, whose loss promotes muscle differentiation. Immunohistochemistry was performed on LMS tissue microarrays (n= 377) for five markers with high levels of mRNA in the muscle-enriched cluster (ACTG2, CASQ2, SLMAP,CFL2, MYLK) and demonstrated significantly correlated expression of the 5 proteins (all pairwise p < 0.005). Expression of the 5 markers was associated with improved disease-specific survival (DSS) in a multivariate Cox regression analysis (p < 0.04). In this analysis that combined gene expression profiling, aCGH and immunohistochemistry, we characterized distinct molecular LMS subtypes, provided insight into their pathogenesis, and identified prognostic biomarkers. This set contains microarray slides from expression studies. VARIABLES: Phenotype: Tumour morphology Age: age at the time of the primary lesion diagnosis (*:Age of diagnosis at the time of tumor resection) Individual Compound Based Treatment: Patient received treatment (CT: exposure to chemotherapy, RT: exposure to radiation therapy) Organism Part: Primary tumour site (UT: uterine origin, ST: soft tissue origin; **:clinically most likely anatomic origin of the tumor) Disease State: Tumour status (P: primary tumor, M: metastatic tumor, LR: local recurrence, N/A: information not available.) Sex/Mating type: M(ale)/F(emale) Experiment type: aCGH or Expression type II A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied.

Dataset Information

Cross-platform comparability of microarray data

Publications

Standardizing global gene expression analysis between laboratories and across platforms.

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