Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA236097 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Microarray is a powerful technique that has been used extensively for genome-wide gene expression analysis. Several different microarray technologies are available, but lack of standardization makes it challenging to compare and integrate data from different platforms. Furthermore, batch related biases within datasets are common, but are often not tackled prior to the data analysis, potentially affecting the end results. In the current study, a set of 234 breast cancer samples were analyzed on two different microarray platforms. The aim was to compare and evaluate the reproducibility and accuracy of gene expression measurements obtained from our in-house 29K array platform with data from Agilent SurePrint G3 microarray platform. The 29K dataset contained known batch-effects associated with the fabrication procedure. We here demonstrate how the use of ComBat batch adjustments method can unmask true biological signals by successfully overcoming systematic technical variations caused by differences between fabrication batches and microarray platforms. Paired correlation analysis revealed a high level of consistency between data obtained from the 29K gene expression platform and Agilent SurePrint G3 platform, which could be further improved by ComBat batch adjustment. Particularly high-variance genes were found to be highly reproducibly expressed across platforms. Furthermore, high concordance rates were observed both for prediction of estrogen receptor status and intrinsic molecular breast cancer subtype classification, two clinical important parameters. In conclusion, the current study emphasizes the importance of utilizing proper batch adjustment methods to reduce systematically technical bias when comparing and integrating data from different fabrication batches and microarray platforms.
2015-06-01 | GSE54275 | GEO
Project description:Cross platform microarray analysis for robust identification of differentially expressed genes
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Project description:Cross-platform reproducibility of oligonucleotide microarray expression profiles
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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:High throughput sequencing is a powerful tool to investigate complex cellular phenotypes in functional genomics studies. Sequencing of transcriptional molecules, RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared to traditional expression analysis based on microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in analysis of RNA-seq data and to cross-compare the results with those obtained through a microarray platform. We used the well-characterized Saccharomyces cervevisiae strain CEN.PK 113-7D grown under two different physiological conditions (batch and chemostat) as a case study. In our work, we addressed the influence of genetic variability on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and Tophat), the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and noiSeq) and we explored the consistency between the two main approaches for RNA-seq: reference mapping and de novo assembly. High reproducibility in data generated through RNA-seq among different biological replicates (correlation ≥ 0.99) and high consistency with the results identified with RNA-seq and microarray data analysis (correlation ≥ 0.91) were observed. The results from differential gene expression identification as well as the results of integrated analysis based on the different methods are in good agreement. Overall, our study provides a useful and comprehensive comparison of the workflow for transcriptome analysis using RNA-seq technique.
2012-09-14 | GSE37599 | GEO
Project description:High throughput sequencing is a powerful tool to investigate complex cellular phenotypes in functional genomics studies. Sequencing of transcriptional molecules, RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared to traditional expression analysis based on microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in analysis of RNA-seq data and to cross-compare the results with those obtained through a microarray platform. We used the well-characterized Saccharomyces cervevisiae strain CEN.PK 113-7D grown under two different physiological conditions (batch and chemostat) as a case study. In our work, we addressed the influence of genetic variability on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and Tophat), the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and noiSeq) and we explored the consistency between the two main approaches for RNA-seq: reference mapping and de novo assembly. High reproducibility in data generated through RNA-seq among different biological replicates (correlation M-bM-^IM-% 0.99) and high consistency with the results identified with RNA-seq and microarray data analysis (correlation M-bM-^IM-% 0.91) were observed. The results from differential gene expression identification as well as the results of integrated analysis based on the different methods are in good agreement. Overall, our study provides a useful and comprehensive comparison of the workflow for transcriptome analysis using RNA-seq technique. Microarray ananlysis were perfomed from the same RNA extraction then compare the result with RNA-seq analysis
2012-09-14 | E-GEOD-37599 | 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. Keywords: cross platform microarrays
2006-06-01 | GSE4833 | GEO
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. Keywords: cross platform microarrays
2006-06-01 | GSE4832 | GEO
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. Keywords: cross platform microarrays
2006-06-01 | GSE4831 | GEO