Detecting hidden batch factors through data-adaptive adjustment for biological effects

Batch effects are one of the major source of technical variations that affect the measurements in high-throughput studies such as RNA sequencing. It has been well established that batch effects can be caused by different experimental platforms, laboratory conditions, different sources of samples and personnel differences. These differences can confound the outcomes of interest and lead to spurious results. A critical input for batch correction algorithms is the knowledge of batch factors, which in many cases are unknown or inaccurate. Hence, the primary motivation of our paper is to detect hidden batch factors that can be used in standard techniques to accurately capture the relationship between gene expression and other modeled variables of interest.

Researchers from Nankai University introduce a new algorithm based on data-adaptive shrinkage and semi-Non-negative Matrix Factorization for the detection of unknown batch effects. They test their algorithm on three different datasets: (i) Sequencing Quality Control, (ii) Topotecan RNA-Seq and (iii) Single-cell RNA sequencing (scRNA-Seq) on Glioblastoma Multiforme. The researchers have demonstrated a superior performance in identifying hidden batch effects as compared to existing algorithms for batch detection in all three datasets. In the Topotecan study, they were able to identify a new batch factor that has been missed by the original study, leading to under-representation of differentially expressed genes. For scRNA-Seq, they demonstrated the power of this method in detecting subtle batch effects.

Detecting run numbers in scRNA-Seq

Consensus matrix on the batch matrix B with λ=0.001 and k = 13 for GBM dataset (GSE57872) using the DASC algorithm

Availability – DASC R package is available at https://github.com/zhanglabNKU/DASC.