Statistics Seminar: Rachel Wang (Berkeley) -- New gene coexpression measures in large heterogenous samples using count statistics

Abstract:
With the advent of high-throughput technologies making large-scale gene expression 
data readily available, developing appropriate computational tools to process these 
data and distill insights into systems biology has been an important part of the 
Big Data challenge. Gene coexpression is one of the earliest techniques developed 
that is still widely in use for functional annotation, pathway analysis and, most 
importantly, the reconstruction of gene regulatory networks, based on gene 
expression data. However, most coexpression measures do not specifically account 
for local features in expression profiles. For example, it is very likely that the 
patterns of gene association may change or only exist in a subset of the samples, 
especially when the samples are pooled from a range of experiments. We propose two 
new gene coexpression statistics based on counting local patterns of gene 
expression ranks to take into account the potentially diverse nature of gene 
interactions. In particular, one of our statistics is designed for time-course 
data with local dependence structures, such as time series coupled over a 
subregion of the time domain. We provide asymptotic analysis of their distributions 
and power, and evaluate their performance against a wide range of existing 
coexpression measures on simulated and real data. Our new statistics are fast to 
compute, robust against outliers, and show comparable if not better general 
performance. They have the important advantage of detecting subtle functional 
relationships that could be easily missed by other methods while remaining sensitive 
to common types of dependence relationships.