ArticleProceedings of the National Academy of Sciences of the United States of America2016
Simultaneous dimension reduction and adjustment for confounding variation.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Multidimensional scaling informed by F-statistic: Visualizing grouped microbiome data with inference.PLoS computational biology · 2026Article
- PARE: A framework for removal of confounding effects from any distance-based dimension reduction method.PLoS computational biology · 2024Article
- INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis.PLoS genetics · 2024Article
- Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables.Biostatistics (Oxford, England) · 2023Article
- Evaluating machine learning algorithms to Predict 30-day Unplanned REadmission (PURE) in Urology patients.BMC medical informatics and decision making · 2023Article
- PLIN2-induced ectopic lipid accumulation promotes muscle ageing in gregarious locusts.Nature ecology & evolution · 2023Article
- Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches.Frontiers in bioengineering and biotechnology · 2023Article
- Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality.BMC bioinformatics · 2022Article
- AC-PCoA: Adjustment for confounding factors using principal coordinate analysis.PLoS computational biology · 2022Article
- Dynamic Meta-data Network Sparse PCA for Cancer Subtype Biomarker Screening.Frontiers in genetics · 2022Article
- Atypical genomic cortical patterning in autism with poor early language outcome.Science advances · 2021Article
- RA3 is a reference-guided approach for epigenetic characterization of single cells.Nature communications · 2021Article
- Aging features of the migratory locust at physiological and transcriptional levels.BMC genomics · 2021Article
- scMC learns biological variation through the alignment of multiple single-cell genomics datasets.Genome biology · 2021Article
- aPCoA: covariate adjusted principal coordinates analysis.Bioinformatics (Oxford, England) · 2020Article
- Benchmarking principal component analysis for large-scale single-cell RNA-sequencing.Genome biology · 2020Article
- Core transcriptional signatures of phase change in the migratory locust.Protein & cell · 2019Article
- Reverse GWAS: Using genetics to identify and model phenotypic subtypes.PLoS genetics · 2019Article
- Transcriptomic Landscape of von Economo Neurons in Human Anterior Cingulate Cortex Revealed by Microdissected-Cell RNA Sequencing.Cerebral cortex (New York, N.Y. : 1991) · 2019Article
- Integrative functional genomic analysis of human brain development and neuropsychiatric risks.Science (New York, N.Y.) · 2018Article
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Authors and funding
13 authors.
Funding
Abstract
Dimension reduction methods are commonly applied to high-throughput biological datasets. However, the results can be hindered by confounding factors, either biological or technical in origin. In this study, we extend principal component analysis (PCA) to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding (AC) variation. We show that AC-PCA can adjust for (i) variations across individual donors present in a human brain exon array dataset and (ii) variations of different species in a model organism ENCODE RNA sequencing dataset. Our approach is able to recover the anatomical structure of neocortical regions and to capture the shared variation among species during embryonic development. For gene selection purposes, we extend AC-PCA with sparsity constraints and propose and implement an efficient algorithm. The methods developed in this paper can also be applied to more general settings. The R package and MATLAB source code are available at https://github.com/linzx06/AC-PCA.
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