ArticleBMC bioinformatics2017
Incorporating biological information in sparse principal component analysis with application to genomic data.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A new CCCH-type zinc finger-related lncRNA signature predicts the prognosis of clear cell renal cell carcinoma patients.Frontiers in genetics · 2022Pooled it
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Identification of a C2H2 zinc finger-related lncRNA prognostic signature and its association with the immune microenvironment in clear cell renal cell carcinoma.Translational andrology and urology · 2025Article
- AWGE-ESPCA: An edge sparse PCA model based on adaptive noise elimination regularization and weighted gene network for Hermetia illucens genomic data analysis.PLoS computational biology · 2025Article
- Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression.Cell reports methods · 2024Article
- PGC-1α regulates the interplay between oxidative stress, senescence and autophagy in the ageing retina important in age-related macular degeneration.Journal of cellular and molecular medicine · 2024Article
- Immunological characteristics of immunogenic cell death genes and malignant progression driving roles of TLR4 in anaplastic thyroid carcinoma.BMC cancer · 2023Article
- Identification of N7-methylguanosine-related lncRNAs for the risk stratification of hepatocellular carcinoma.Journal of gastrointestinal oncology · 2023Article
- Identification and validation of fatty acid metabolism-related lncRNA signatures as a novel prognostic model for clear cell renal cell carcinoma.Scientific reports · 2023Article
- Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma.Bioscience reports · 2021Article
- A novel autophagy-related lncRNA prognostic risk model for breast cancer.Journal of cellular and molecular medicine · 2021Article
- Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components.International journal of molecular sciences · 2020Article
- Identification and validation of stemness-related lncRNA prognostic signature for breast cancer.Journal of translational medicine · 2020Article
- Bayesian generalized biclustering analysis via adaptive structured shrinkage.Biostatistics (Oxford, England) · 2020Article
- Knowledge-Guided Statistical Learning Methods for Analysis of High-Dimensional -Omics Data in Precision Oncology.JCO precision oncology · 2019Review
- A Predictive Model Assessing Genetic Susceptibility Risk at Workplace.International journal of environmental research and public health · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundSparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA. In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological information in variable selection.
resultsOur simulation studies suggest that, compared to existing sparse PCA methods, the proposed methods achieve higher sensitivity and specificity when the graph structure is correctly specified, and are fairly robust to misspecified graph structures. Application to a glioblastoma gene expression dataset identified pathways that are suggested in the literature to be related with glioblastoma.
conclusionsThe proposed sparse PCA methods Fused and Grouped sparse PCA can effectively incorporate prior biological information in variable selection, leading to improved feature selection and more interpretable principal component loadings and potentially providing insights on molecular underpinnings of complex diseases.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.