Xu, X., Zhang, Y., Williams, J., Antoniou, E., McCombie, W. R., Wu, S., Zhu, W., Davidson, N. O., Denoya, P., Li, E. (June 2013) Parallel comparison of Illumina RNA-Seq and Affymetrix microarray platforms on transcriptomic profiles generated from 5-aza-deoxy-cytidine treated HT-29 colon cancer cells and simulated datasets. BMC Bioinformatics, 14 Sup. S1. ISSN 1471-2105 (Electronic)1471-2105 (Linking)
Preview |
PDF (Paper)
McCombie BMC Bioinformatics 2013.pdf - Published Version Download (2MB) | Preview |
Abstract
BACKGROUND: High throughput parallel sequencing, RNA-Seq, has recently emerged as an appealing alternative to microarray in identifying differentially expressed genes (DEG) between biological groups. However, there still exists considerable discrepancy on gene expression measurements and DEG results between the two platforms. The objective of this study was to compare parallel paired-end RNA-Seq and microarray data generated on 5-azadeoxy-cytidine (5-Aza) treated HT-29 colon cancer cells with an additional simulation study. METHODS: We first performed general correlation analysis comparing gene expression profiles on both platforms. An Errors-In-Variables (EIV) regression model was subsequently applied to assess proportional and fixed biases between the two technologies. Then several existing algorithms, designed for DEG identification in RNA-Seq and microarray data, were applied to compare the cross-platform overlaps with respect to DEG lists, which were further validated using qRT-PCR assays on selected genes. Functional analyses were subsequently conducted using Ingenuity Pathway Analysis (IPA). RESULTS: Pearson and Spearman correlation coefficients between the RNA-Seq and microarray data each exceeded 0.80, with 66%~68% overlap of genes on both platforms. The EIV regression model indicated the existence of both fixed and proportional biases between the two platforms. The DESeq and baySeq algorithms (RNA-Seq) and the SAM and eBayes algorithms (microarray) achieved the highest cross-platform overlap rate in DEG results from both experimental and simulated datasets. DESeq method exhibited a better control on the false discovery rate than baySeq on the simulated dataset although it performed slightly inferior to baySeq in the sensitivity test. RNA-Seq and qRT-PCR, but not microarray data, confirmed the expected reversal of SPARC gene suppression after treating HT-29 cells with 5-Aza. Thirty-three IPA canonical pathways were identified by both microarray and RNA-Seq data, 152 pathways by RNA-Seq data only, and none by microarray data only. CONCLUSIONS: These results suggest that RNA-Seq has advantages over microarray in identification of DEGs with the most consistent results generated from DESeq and SAM methods. The EIV regression model reveals both fixed and proportional biases between RNA-Seq and microarray. This may explain in part the lower cross-platform overlap in DEG lists compared to those in detectable genes.
| Item Type: | Paper |
|---|---|
| Subjects: | diseases & disorders > cancer bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification bioinformatics > genomics and proteomics > genetics & nucleic acid processing bioinformatics > genomics and proteomics Investigative techniques and equipment diseases & disorders > cancer > cancer types > colon cancer diseases & disorders > cancer > cancer types > colon cancer Investigative techniques and equipment > assays > next generation sequencing bioinformatics > genomics and proteomics > genetics & nucleic acid processing > transcriptomes |
| CSHL Authors: | |
| Communities: | CSHL Cancer Center Program > Cancer Genetics CSHL labs > McCombie lab CSHL Cancer Center Shared Resources > DNA Sequencing Service |
| Depositing User: | Matt Covey |
| Date: | 28 June 2013 |
| Date Deposited: | 20 Sep 2013 13:53 |
| Last Modified: | 05 Nov 2015 15:45 |
| PMCID: | PMC3697991 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/28585 |
Actions (login required)
 |
Administrator's edit/view item |