ArticleScientific reports2017
Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Exploring and mitigating shortcomings in single-cell differential expression analysis with a new statistical paradigm.Genome biology · 2025Article
- Using pseudotime derivative on single-cell RNA sequencing data to identify genes undergoing cell cycle regulation.Bioinformatics advances · 2025Article
- Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.Genome research · 2024Article
- A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- The effect of data transformation on low-dimensional integration of single-cell RNA-seq.BMC bioinformatics · 2024Article
- Immune-interacting lymphatic endothelial subtype at capillary terminals drives lymphatic malformation.The Journal of experimental medicine · 2023Article
- The Role of Systems Biology in Deciphering Asthma Heterogeneity.Life (Basel, Switzerland) · 2022Review
- AC-PCoA: Adjustment for confounding factors using principal coordinate analysis.PLoS computational biology · 2022Article
- CCPE: cell cycle pseudotime estimation for single cell RNA-seq data.Nucleic acids research · 2022Article
- UNIFYING AND GENERALIZING METHODS FOR REMOVING UNWANTED VARIATION BASED ON NEGATIVE CONTROLS.Statistica Sinica · 2021Article
- Derangement of cell cycle markers in peripheral blood mononuclear cells of asthmatic patients as a reliable biomarker for asthma control.Scientific reports · 2021Article
- SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection.Briefings in bioinformatics · 2021Article
- Employing core regulatory circuits to define cell identity.The EMBO journal · 2021Review
- Efficient and effective control of confounding in eQTL mapping studies through joint differential expression and Mendelian randomization analyses.Bioinformatics (Oxford, England) · 2021Article
- Recovering Spatially-Varying Cell-Specific Gene Co-expression Networks for Single-Cell Spatial Expression Data.Frontiers in genetics · 2021Article
- Data-based RNA-seq simulations by binomial thinning.BMC bioinformatics · 2020Article
- scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment.Bioinformatics (Oxford, England) · 2020Article
- Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis.Genome research · 2020Article
- Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis.Genome biology · 2019Article
- Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq.eLife · 2019Article
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2 authors.
Funding
Abstract
Single cell RNA sequencing (scRNAseq) technique is becoming increasingly popular for unbiased and high-resolutional transcriptome analysis of heterogeneous cell populations. Despite its many advantages, scRNAseq, like any other genomic sequencing technique, is susceptible to the influence of confounding effects. Controlling for confounding effects in scRNAseq data is a crucial step for accurate downstream analysis. Here, we present a novel statistical method, which we refer to as scPLS (single cell partial least squares), for robust and accurate inference of confounding effects. scPLS takes advantage of the fact that genes in a scRNAseq study often can be naturally classified into two sets: a control set of genes that are free of effects of the predictor variables and a target set of genes that are of primary interest. By modeling the two sets of genes jointly using the partial least squares regression, scPLS is capable of making full use of the data to improve the inference of confounding effects. With extensive simulations and comparisons with other methods, we demonstrate the effectiveness of scPLS. Finally, we apply scPLS to analyze two scRNAseq data sets to illustrate its benefits in removing technical confounding effects as well as for removing cell cycle effects.
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