Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data.

ABSTRACT:

Motivation

Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when prepr

SUBMITTER: Hsieh PH 

PROVIDER: S-EPMC10598588 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature