Leveraging global gene expression patterns to predict expression of unmeasured genes.

ABSTRACT:

Background

Large collections of paraffin-embedded tissue represent a rich resource to test hypotheses based on gene expression patterns; however, measurement of genome-wide expression is cost-prohibitive on a large scale. Using the known expression correlation structure within a given disease type (in this case, high grade serous ovarian cancer; HGSC), we sought to identify reduced sets of directly measured (DM) genes which could accurately predict the expression of a maximized number of unmeasured genes.

Results

We developed a greedy gene set selection (GGS) algorithm which returns a DM set of user specified size based on a specific correlation threshold (|rP|) and minimum number of DM genes that must be correlated to an unmeasured gene in order to infer the value of the un

SUBMITTER: Rudd J 

PROVIDER: S-EPMC4678722 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature