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Department of Statistics Research Seminar

Identification of Differentially Expressed Genes Adjusting the Non-Uniformity in RNA-Seq Data

Monday, May 6, 2013 3:55 PM - 5:00 PM

Tao Yu, National University of Singapore

In the past few years, RNA-Seq has been widely used in genomics research, leading to tons of numerical data outputs.

Non-uniformity is dramatic in RNA-Seq count data. Recent studies have shown that the non-uniformity is a systematic bias rather than random noise, which can be modeled based on local sequences. In a study with different experimental groups, control and treatment say, we may have a number of replicates in each group. To the best of our knowledge, existing statistical methods are not able to analyze such data effectively, bearing in mind of the non-uniformity in RNA-Seq data.

In this paper, we propose a new model and establish an efficient computational algorithm to deal with data of this kind. Our method jointly estimates the expression levels of all genes and automatically identifies the differentially expressed genes between groups.

Furthermore, compared with the existing approach, our implemented algorithm is computationally much more efficient.


Milam Hall (campus map)
019
Free
Judith Burks
1 541 737 3883
judy.burks at oregonstate.edu
College of Science, Statistics Department
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