Image Rna-Seq Data Analysis in Clouds

• Activity Number: 596
• Type: Topic Contributed
• Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Junhai Jiang*+ and Nan Lin and Momiao Xiong
• Companies: UTSPH and UTSPH and University of Texas Health Science Center at Houston
• Keywords: Image Genetics ; RNA-seq ; Cloud Computing

Abstract

Large-scale integrated genetic and imaging data analysis is a new approach used to uncover the individual variability and mechanism of disease development. This approach, which has not been well developed, has the potential to open a new avenue for dissecting genetic structure of complex disease and personal medicine. Joint analysis of imaging and RNA-seq data will identify genes significantly associated with tumors and provide useful information on target therapy. However, an image may contain 100k-1M voxels and RNA-seq data may include number of reads in 100 M genomic position. As both imaging- and RNA-seq-domain observations include a huge number of variables, joint image and RNA-seq analysis on such Big Data represents a computational challenge that cannot be addressed with conventional computational techniques. To address this challenge, we use cloud computing techniques to develop sophisticated algorithms for joint analysis of imaging and RNA-seq data running on Amazon cloud computer. The developed method has been applied to image and RNA-seq data of ovarian cancer downloaded from TCGA datasets. We identify 124 significantly associated genes.