RT Journal Article
SR Electronic
T1 Enabling cross-study analysis of RNA-Sequencing data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 110734
DO 10.1101/110734
A1 Qingguo Wang
A1 Joshua Armenia
A1 Chao Zhang
A1 Alexander V. Penson
A1 Ed Reznik
A1 Liguo Zhang
A1 Angelica Ochoa
A1 Benjamin E. Gross
A1 Christine A. Iacobuzio-Donahue
A1 Doron Betel
A1 Barry S. Taylor
A1 Jianjiong Gao
A1 Nikolaus Schultz
YR 2017
UL http://biorxiv.org/content/early/2017/02/27/110734.abstract
AB Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data. While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources poses a great challenge, due to differences in sample and data processing. Here, we present a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment and gene expression quantification as well as batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA) and have successfully corrected for study-specific biases, enabling comparative analysis across studies. The normalized data are available for download via GitHub (at https://github.com/mskcc/RNAseqDB).