SMS scnews item created by John Ormerod at Fri 25 Jul 2014 1103
Type: Seminar
Distribution: World
Expiry: 2 Aug 2014
Calendar1: 1 Aug 2014 1400-1500
CalLoc1: Carslaw 173
Auth: jormerod@pjormerod4.pc (assumed)

# 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.


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