findPC: An R package to automatically select number of principal components in single-cell analysis

Zhuang, H.; Ji, Z.

doi:10.1101/2021.10.15.464460

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bioRxiv · 10.1101/2021.10.15.464460

findPC: An R package to automatically select number of principal components in single-cell analysis

Zhuang, H.·Ji, Z.

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Abstract

SummaryPrincipal component analysis (PCA) is widely used in analyzing single-cell genomic data. Selecting the optimal number of PCs is a crucial step for downstream analyses. The elbow method is most commonly used for this task, but it requires one to visually inspect the elbow plot and manually choose the elbow point. To address this limitation, we developed six methods to automatically select the optimal number of PCs based on the elbow method. We evaluated the performance of these methods on real single-cell RNA-seq data from multiple human and mouse tissues. The perpendicular line method with 20 PCs has the best overall performance, and its results are highly consistent with the numbers of PCs identified manually. We implemented the six methods in an R package, findPC, that objectively selects the number of PCs and can be easily incorporated into any automatic analysis pipeline. Availability and ImplementationfindPC R package is freely available at https://github.com/haotian-zhuang/findPC Contactzhicheng.ji@duke.edu Supplementary informationSupplementary data are available at Bioinformatics online.

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Zhuang, H., Ji, Z.. 2021-10-16. findPC: An R package to automatically select number of principal components in single-cell analysis. https://doi.org/10.1101/2021.10.15.464460

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