ArticleBioTechniques2013
Sequential projection pursuit principal component analysis--dealing with missing data associated with new -omics technologies.
Article in BioTechniques, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Investigation of Influences on Indoor and Outdoor SVOC Exposure.International journal of environmental research and public health · 2025Article
- Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions.Journal of proteome research · 2024Article
- Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration.bioRxiv : the preprint server for biology · 2023Article
- Proteogenomic and metabolomic characterization of human glioblastoma.Cancer cell · 2021Article
- Comparing identified and statistically significant lipids and polar metabolites in 15-year old serum and dried blood spot samples for longitudinal studies.Rapid communications in mass spectrometry : RCM · 2017Article
- Effects of imputation on correlation: implications for analysis of mass spectrometry data from multiple biological matrices.Briefings in bioinformatics · 2017Article
- An integrative imputation method based on multi-omics datasets.BMC bioinformatics · 2016Article
- Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.Journal of proteome research · 2015Review
- A Statistical Analysis of the Effects of Urease Pre-treatment on the Measurement of the Urinary Metabolome by Gas Chromatography-Mass Spectrometry.Metabolomics : Official journal of the Metabolomic Society · 2014Article
- Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile.Metabolites · 2013Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Principal Component Analysis (PCA) is a common exploratory tool used to evaluate large complex data sets. The resulting lower-dimensional representations are often valuable for pattern visualization, clustering, or classification of the data. However, PCA cannot be applied directly to many -omics data sets generated by newer technologies such as label-free mass spectrometry due to large numbers of non-random missing values. Here we present a sequential projection pursuit PCA (sppPCA) method for defining principal components in the presence of missing data. Our results demonstrate that this approach generates robust and informative low-dimensional data representations compared to commonly used imputation approaches.
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Registered trials
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.