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Activity Number: 9
Type: Invited
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: WNAR
Abstract #314362 View Presentation
Title: A Big Data Approach for Integrative Analysis of Two Different High-Throughput Genomic Data Types
Author(s): Hongkai Ji* and Weiqiang Zhou and Bing He
Companies: Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health
Keywords: Data integration ; Big data ; Genomics ; High-throughput sequencing ; Gene expression ; ChIP-seq
Abstract:

Integrating information from multiple data sets is a useful way to improve the analysis of high-throughput genomic data. By combining different sources of information, one can more effectively eliminate false discoveries and increase the statistical power of signal detection. Statistical modeling is relatively easier when the data to be integrated are of the same type (e.g., two gene expression data sets). It is more complicated when two different data types need to be integrated (e.g., integrate gene expression data with ChIP-seq data) and both are high-dimensional. Here we propose a new approach for integrative analysis of two different high-throughput genomic data types. Our approach uses publicly available genomic data to model the correlation structure between the two high-dimensional data types. Using the learned model, one data type can be mapped to the space of the other data type and thus converting the problem of integrating two different data types into a problem of integrating data from the same data type for which powerful and flexible methods can then be developed. We demonstrate this approach using integrative analysis of gene expression and ChIP-seq data.


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