Details

Autor(en) / Beteiligte

• Zhuang, Haotian
• Wang, Huimin
• Ji, Zhicheng
• Martelli, Pier Luigi

Titel

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

Ist Teil von

• Bioinformatics, 2022-05, Vol.38 (10), p.2949-2951

Ort / Verlag

England: Oxford University Press

Erscheinungsjahr

2022

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Abstract Summary Principal component analysis is widely used in analyzing single-cell genomic data. Selecting the optimal number of principal components (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 and cell types. The perpendicular line method with 30 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 Implementation findPC R package is freely available at https://github.com/haotian-zhuang/findPC. Supplementary information Supplementary data are available at Bioinformatics online.