Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed

TL;DR

This paper introduces methods to correct gene expression data for unwanted variation using control genes and replicates, enabling unsupervised analysis without losing signals of interest, and demonstrates their effectiveness on multiple datasets.

Contribution

The paper presents novel correction techniques that do not require observing the factor of interest, leveraging control genes and replicates to estimate unwanted variation.

Findings

Methods effectively remove unwanted variation while preserving signals.

Proposed techniques outperform existing correction methods.

Approach is validated on three gene expression datasets.

Abstract

When dealing with large scale gene expression studies, observations are commonly contaminated by unwanted variation factors such as platforms or batches. Not taking this unwanted variation into account when analyzing the data can lead to spurious associations and to missing important signals. When the analysis is unsupervised, e.g., when the goal is to cluster the samples or to build a corrected version of the dataset - as opposed to the study of an observed factor of interest - taking unwanted variation into account can become a difficult task. The unwanted variation factors may be correlated with the unobserved factor of interest, so that correcting for the former can remove the latter if not done carefully. We show how negative control genes and replicate samples can be used to estimate unwanted variation in gene expression, and discuss how this information can be used to correct the expression data or build estimators for unsupervised problems. The proposed methods are then evaluated on three gene expression datasets. They generally manage to remove unwanted variation without losing the signal of interest and compare favorably to state of the art corrections.