# Covariance-based sample selection for heterogeneous data: Applications to gene expression and autism risk gene detection

## TL;DR

This paper introduces COBS, a covariance-based sample selection method that enhances gene risk detection in heterogeneous brain tissue data by selecting more homogeneous samples, improving the power of downstream analysis.

## Contribution

The paper presents COBS, a novel method for selecting homogeneous samples based on covariance, improving gene detection in heterogeneous datasets for autism research.

## Key findings

- COBS improves DAWN's prediction of autism risk genes.
- Sample selection based on covariance enhances detection power.
- Method validated using data from two different time points.

## Abstract

Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, "guilt by association" methods such as DAWN have been developed to identify these autism risk genes. Previous research analyzes the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue is known to affect the gene expression's covariance, previous research have focused only on a specific subset of samples to avoid the issue of heterogeneity. This leads to a potential loss of power when detecting risk genes. In this article, we develop a new method called COBS (COvariance-Based sample Selection) to find a larger and more homogeneous subset of samples that share the same population covariance matrix for the downstream DAWN analysis. To demonstrate COBS's effectiveness, we utilize genetic risk scores from two sequential data freezes obtained in 2014 and 2019. We show COBS improves DAWN's ability to predict risk genes detected in the newer data freeze when utilizing the risk scores of the older data freeze as input.