OnlinePCA.jl: A Julia Package for Out-of-core and Sparse Principal Component Analysis
Abstract
Principal Component Analysis (PCA) is a widely used dimensionality reduction technique (Meng, 2016;Stein-O'Brien, 2018), but becomes computationally prohibitive for large data matrices.Recent advances in single-cell omics have led to datasets with millions of cells, for which standard PCA implementations often fail to scale.OnlinePCA.jl is a Julia package that addresses this challenge by providing scalable PCA algorithms (https://github.com/rikenbit/OnlinePCA.jl).
Fields of Study & Subjects
Statistical Methods and Applications
Matrix Theory and Algorithms
Statistical Methods and Inference
Publication details
- Journal / source
- The Journal of Open Source Software
- Volume
- 11
- Issue
- 117
- Pages
- 9343–9343
- Publication date
- 29 January 2026