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`FastPCA`: An R package for fast singular value decomposition

Year: 2026

Type: software

Abstract: The `FastPCA` package provides an interface to optimized matrix multiplication libraries (`libtorch`) for the purpose of singular value decomposition. Using `FastPCA` to perform randomized singular value decomposition (SVD) with the `torch` or `pytorch` backend drastically reduces the computational time compared to base R `prcomp` and other truncated singular value decomposition. Developed for biological data such as single-cell RNA-sequencing, spatial transcriptomics, or matrix-assisted laser desorption/ionization (MALDI imaging), `FastPCA` can efficiently and accurately identify the leading singular values in high-dimensional data.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18626772

Authors: Ward, Kimberly, Hayes, Mitchell, Peres, Lauren, Fridley, Brooke, Eschrich, Steven, Soupir, Alex

Institutions: Moffitt Cancer Center, Children's Mercy Hospital, Cvergenx (United States)

Language: English

Topic: Data Analysis with R

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Cancer Institute

OpenAlex ID: W7128769934

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