ArticleGenome biology2023
Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 13 citations in OpenAlex.
- Article
- Mapping genetic regulation of gene expression to cellular contexts identifies long non-coding RNAs associated with brain disorders.Nature communications · 2026Article
- Decreased degree of adipocyte differentiation in visceral adipose tissue contributes to metabolic dysfunction-associated steatotic liver disease.Nature communications · 2026Article
- Single-Cell Gene Expression and eQTL Analyses in the Human Retina, RPE, and Choroid in Macular Degeneration.bioRxiv : the preprint server for biology · 2026Article
- distQTL: distribution quantitative trait loci identification by population-scale single-cell data.NAR genomics and bioinformatics · 2025Article
- mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis.Nature communications · 2025Article
- distQTL: Distribution Quantitative Trait Loci Identification by Population-Scale Single-Cell Data.bioRxiv : the preprint server for biology · 2025Article
- Scalable eQTL mapping using single-nucleus RNA-sequencing of recombined gametes from a small number of individuals.PLoS biology · 2025Article
- Methods and Insights from Single-Cell Expression Quantitative Trait Loci.Annual review of genomics and human genetics · 2023Review
- Single-cell genomics meets human genetics.Nature reviews. Genetics · 2023Review
- Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses.Genome biology · 2023Article
- Expression quantitative trait locus studies in the era of single-cell omics.Frontiers in genetics · 2023Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using latent variables in gene expression data can help correct unobserved confounders and increase statistical power for expression quantitative trait Loci (eQTL) detection. The probabilistic estimation of expression residuals (PEER) and principal component analysis (PCA) are widely used methods that can remove unwanted variation and improve eQTL discovery power in bulk RNA-seq analysis. However, their performance has not been evaluated extensively in single-cell eQTL analysis, especially for different cell types. Potential challenges arise due to the structure of single-cell RNA-seq data, including sparsity, skewness, and mean-variance relationship. Here, we show by a series of analyses that PEER and PCA require additional quality control and data transformation steps on the pseudo-bulk matrix to obtain valid latent variables; otherwise, it can result in highly correlated factors (Pearson's correlation r = 0.63 ~ 0.99). Incorporating valid PFs/PCs in the eQTL association model would identify 1.7 ~ 13.3% more eGenes. Sensitivity analysis showed that the pattern of change between the number of eGenes detected and fitted PFs/PCs varied significantly in different cell types. In addition, using highly variable genes to generate latent variables could achieve similar eGenes discovery power as using all genes but save considerable computational resources (~ 6.2-fold faster).
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