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HisCoM-PCA:基于主成分分析用于通路分析的层次结构成分分析软件。

HisCoM-PCA: software for hierarchical structural component analysis for pathway analysis based using principal component analysis.

作者信息

Jiang Nan, Lee Sungyoung, Park Taesung

机构信息

Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.

Center for Precision Medicine, Seoul National University Hospital, Seoul 08826 , Korea.

出版信息

Genomics Inform. 2020 Mar;18(1):e11. doi: 10.5808/GI.2020.18.1.e11. Epub 2020 Mar 31.

Abstract

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

摘要

在全基因组关联研究中,基于通路的分析已被广泛开展,以增强对单核苷酸多态性关联结果的解读。我们提出了一种用于常见变异通路分析的新型层次结构成分模型(HisCoM)(用于常见变异通路分析的HisCoM [HisCoM-PCA]),该模型用于识别与性状相关的通路。HisCoM-PCA基于主成分分析(PCA)对每个基因中的单核苷酸多态性进行降维,并基于用于通路分析的HisCoM。在本研究中,我们开发了一款用于常见变异层次通路分析的HisCoM-PCA软件。HisCoM-PCA软件具有多个特点。PCA步骤中的各种主成分得分选择标准可由希望通过不同阈值在每个基因水平汇总常见变异的用户指定。此外,多个公共通路数据库和定制的通路信息可用于进行通路分析。我们期望HisCoM-PCA软件将有助于用户进行强大的通路分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/42bf/7120349/ec06d30e2419/gi-2020-18-1-e11f1.jpg

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