pcaMethods - a bioconductor package providing PCA methods for incomplete data

By Wolfram Stacklies, Henning Redestig, Matthias Scholz, Dirk Walther, Joachim Selbig. Bioinform., 2007

1,101 citations recorded by the source corpus.

UNLABELLED pcaMethods is a Bioconductor compliant library for computing principal component analysis (PCA) on incomplete data sets. The results can be analyzed directly or used to estimate missing values to enable the use of missing value sensitive statistical methods. The package was mainly developed with microarray and metabolite data sets in mind, but can be applied to any other incomplete data set as well. AVAILABILITY http://www.bioconductor.org

View Paper: https://www.semanticscholar.org/paper/3b875d8eec3689319e719c82506b6580fddd5e01

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Citations in the corpus

SomaticSignatures: inferring mutational signatures from single-nucleotide variants (2014, methodology): “We provide convenient access to implementations for NMF and PCA (Gaujoux et Seoighe, 2010; Stacklies et al., 2007), and users can apply own functions with alternative decomposition methods through the API. sigs = identifySignatures(m, nSig, method) The user interface and library of plotting…”
Soil heterogeneity buffers community response to climate change in species‐rich grassland (2011, methodology): “…cm 2 ) or the inner five (100 cm 2 ) measurements, plus the center depth and pH sample. pH values for the 120 microsites established in 2008 that lacked pH surveys were estimated before PCA analysis using a Bayesian PCA missing value estimator in the R pcaMethods package (Stacklies et al., 2007).”
Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis (2016, methodology): “For example, users can automatically exclude features with too many missing values, or use various methods to perform missing value estimation such as replacing missing values using the mean/median, Probabilistic PCA (PPCA), Bayesian PCA (BPCA), or Singular Value Decomposition (SVD) (Stacklies et al”
Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN] (2014, methodology): “PCA was performed using Bayesian principal componentanalysis (the “ bpca ” function) asimplemented intheR package pcaMethods (Stacklies et al., 2007).”
Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling (2016, methodology): “All the computations were performed using built-in R 3.0.2 functions except KNN which was performed using the package ‘‘impute’’, BPCA which was performed using the package ‘‘pcaMethods’’(Stacklies et al. 2007) and RF which was performed using the package ‘‘missForest’’.”
MetaboAnalyst: a web server for metabolomic data analysis and interpretation (2009, methodology): “We also implemented a variety of methods which enable users to manually or automatically perform missing value exclusion, missing value replacement, as well as missing value imputation by Probabilistic PCA (PPCA), Bayesian PCA (BPCA) and Singular Value Decomposition Imputation (SVDImpute) (15,16).”
Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies. (2016, methodology): “To date, numerous methods exists and are available to any practitioner, either as independent packages [6, 7, 8], or through dedicated pipeline packages such as MSnbase [9].”
missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016, methodology): “In R an implementation was provided by Stacklies et al. (2007) in the pcaMethods package.”