ERStruct: a fast Python package for inferring the number of top principal components from whole genome sequencing data

BMC Bioinformatics

Software

Zhonghua Liu¹  Gao Wang²  Yuyang Xu³  Minhao Yao³  Jinghan Yang³ 

[1] Department of Biostatistics, Columbia University, New York, NY, USA;Department of Neurology, Gertrude. H. Sergievsky Center, Columbia University, New York, NY, USA;Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong SAR, China;

关键词: Population structure;    Principal component;    Random matrix theory;    Sequencing data;    Spectral analysis;   

DOI  :  10.1186/s12859-023-05305-0

 received in 2022-11-07, accepted in 2023-04-25,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundLarge-scale multi-ethnic DNA sequencing data is increasingly available owing to decreasing cost of modern sequencing technologies. Inference of the population structure with such sequencing data is fundamentally important. However, the ultra-dimensionality and complicated linkage disequilibrium patterns across the whole genome make it challenging to infer population structure using traditional principal component analysis based methods and software.ResultsWe present the ERStruct Python Package, which enables the inference of population structure using whole-genome sequencing data. By leveraging parallel computing and GPU acceleration, our package achieves significant improvements in the speed of matrix operations for large-scale data. Additionally, our package features adaptive data splitting capabilities to facilitate computation on GPUs with limited memory.ConclusionOur Python package ERStruct is an efficient and user-friendly tool for estimating the number of top informative principal components that capture population structure from whole genome sequencing data.

【 授权许可】

CC BY   
© The Author(s) 2023

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