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Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses

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 th...

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Autores principales: Xue, Angli, Yazar, Seyhan, Neavin, Drew, Powell, Joseph E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9948363/
https://www.ncbi.nlm.nih.gov/pubmed/36823676
http://dx.doi.org/10.1186/s13059-023-02873-5
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author Xue, Angli
Yazar, Seyhan
Neavin, Drew
Powell, Joseph E.
author_facet Xue, Angli
Yazar, Seyhan
Neavin, Drew
Powell, Joseph E.
author_sort Xue, Angli
collection PubMed
description 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). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02873-5.
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spelling pubmed-99483632023-02-24 Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses Xue, Angli Yazar, Seyhan Neavin, Drew Powell, Joseph E. Genome Biol Research 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). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02873-5. BioMed Central 2023-02-23 /pmc/articles/PMC9948363/ /pubmed/36823676 http://dx.doi.org/10.1186/s13059-023-02873-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Xue, Angli
Yazar, Seyhan
Neavin, Drew
Powell, Joseph E.
Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses
title Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses
title_full Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses
title_fullStr Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses
title_full_unstemmed Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses
title_short Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses
title_sort pitfalls and opportunities for applying latent variables in single-cell eqtl analyses
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9948363/
https://www.ncbi.nlm.nih.gov/pubmed/36823676
http://dx.doi.org/10.1186/s13059-023-02873-5
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