Haidong Yi1, Ayush T Raman2,3, Han Zhang1,4, Genevera I Allen5, Zhandong Liu2,3. 1. College of Computer and Control Engineering, Nankai University, Tianjin 300350, China. 2. Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA. 3. Department of Pediatrics, Neurological Research Institute, Baylor College of Medicine, Houston, TX 77030, USA. 4. Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China. 5. Department of Statistics, Rice University, Houston, TX, 77030 USA.
Abstract
Motivation: 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. Results: We introduce a new algorithm based on data-adaptive shrinkage and semi-Non-negative Matrix Factorization for the detection of unknown batch effects. We test our algorithm on three different datasets: (i) Sequencing Quality Control, (ii) Topotecan RNA-Seq and (iii) Single-cell RNA sequencing (scRNA-Seq) on Glioblastoma Multiforme. We 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, we 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, we demonstrated the power of our method in detecting subtle batch effects. Availability and implementation: DASC R package is available via Bioconductor or at https://github.com/zhanglabNKU/DASC. Contact: zhanghan@nankai.edu.cn or zhandonl@bcm.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: 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. Results: We introduce a new algorithm based on data-adaptive shrinkage and semi-Non-negative Matrix Factorization for the detection of unknown batch effects. We test our algorithm on three different datasets: (i) Sequencing Quality Control, (ii) Topotecan RNA-Seq and (iii) Single-cell RNA sequencing (scRNA-Seq) on Glioblastoma Multiforme. We 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, we 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, we demonstrated the power of our method in detecting subtle batch effects. Availability and implementation: DASC R package is available via Bioconductor or at https://github.com/zhanglabNKU/DASC. Contact: zhanghan@nankai.edu.cn or zhandonl@bcm.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Monica Benito; Joel Parker; Quan Du; Junyuan Wu; Dong Xiang; Charles M Perou; J S Marron Journal: Bioinformatics Date: 2004-01-01 Impact factor: 6.937