Fast Principal Component Analysis of Large-Scale Genome-Wide Data
By Gad Abraham, Michael Inouye. bioRxiv, 2014
321 citations recorded by the source corpus.
Principal component analysis (PCA) is routinely used to analyze genome-wide single-nucleotide polymorphism (SNP) data, for detecting population structure and potential outliers. However, the size of SNP datasets has increased immensely in recent years and PCA of large datasets has become a time consuming task. We have developed flashpca, a highly efficient PCA implementation based on randomized algorithms, which delivers identical accuracy in extracting the top principal components compared with existing tools, in substantially less time. We demonstrate the utility of flashpca on both HapMap3 and on a large Immunochip dataset. For the latter, flashpca performed PCA of 15,000 individuals up to 125 times faster than existing tools, with identical results, and PCA of 150,000 individuals using flashpca completed in 4 hours. The increasing size of SNP datasets will make tools such as flashpca essential as traditional approaches will not adequately scale. This approach will also help to scale other applications that leverage PCA or eigen-decomposition to substantially larger datasets.
Subfields: Artificial Intelligence, Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/abb329902da905e8d19f2928b0d2d501052fa1f2
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Citations in the corpus
- Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr (2018, methodology): “For computing principal component analysis (PCA), commonly performed to account for population stratification in association, a fast mode named FastPCA has been added to the software EIGENSOFT, and FlashPCA has been replaced by FlashPCA2 (Abraham and Inouye, 2014; Abraham et al. , 2016; Galinsky et ”
- A resource-efficient tool for mixed model association analysis of large-scale data (2019, methodology): “If PCs are not available, we would recommend efficient PCA tools such as fastPCA or FlashPCA 63,64 .”
- Application of t-SNE to Human Genetic Data (2017, background): “Of course, in specialized applications such as sparse matrix, approximate results, or partial results, the computational complexity can be improved [49, 50, 51].”
- Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry (2018, methodology): “PCs were calculated using the flashPCA software (31).”
- Allelic Landscape of Human Blood Cell Trait Variation and Links (2016, methodology): “REAGENT or RESOURCE SOURCE IDENTIFIER Software and Algorithms flashpca (Abraham and Inouye, 2014) https://github.com/gabraham/flashpca R 3.1.2 (R Core Team, 2014) https://www.r-project.org/ biomaRt Bioconductor https://bioconductor.org/packages/release/bioc/html/biomaRt.html data.table The R…”