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

• Published in: BMC bioinformatics Vol. 14; no. Suppl 9; p. S1
• Main Authors: Xu, Xiao, Zhang, Yuanhao, Williams, Jennie, Antoniou, Eric, McCombie, W Richard, Wu, Song, Zhu, Wei, Davidson, Nicholas O, Denoya, Paula, Li, Ellen
• Format: Journal Article
• Language: English
• Published: London BioMed Central 28.06.2013; Springer Nature B.V
• Subjects: Algorithms; Azacitidine; Bias; Bioinformatics; Biomedical and Life Sciences; Colon; Colonic Neoplasms - genetics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Correlation analysis; Correlation coefficient; DNA methylation; Gene expression; Gene Expression Profiling - methods; Genomes; HT29 Cells; Humans; Laboratories; Life Sciences; Medical research; Meetings; Methodology; Methodology Article; Methods; Microarrays; Oligonucleotide Array Sequence Analysis - methods; Polymerase chain reaction; Regression Analysis; RNA, Neoplasm - genetics; Sensitivity and Specificity; Sequence Analysis, RNA - methods; Differentially Express Gene; Differentially Express Gene List; Platform Comparison; Fixed Bias; Proportional Bias
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-14-S9-S1

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.