Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA o
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BiocManager::install("pcaMethods")method="robustPca").NA) in incomplete datasets using Probabilistic PCA (method="ppca") or Nipals (method="nipals").ExpressionSet or matrix using a unified pca() interface.scry or GLM-PCA instead because they model the count distribution directly.prcomp or irlba because they are faster and computationally lighter.data.frame, or an ExpressionSet (from the Biobase package).NA.prep() function with scale="none" and center=TRUE) prior to running PCA.prep() function.Q2() function.kEstimate().kEstimate().prep() function to handle centering and scaling before running pca().Q2() function to evaluate the optimal number of principal components to retain.NA and use method="ppca", or use method="robustPca" directly on the complete data.kEstimate() with cross-validation to ensure the chosen method and number of PCs are appropriate.scale(..., center=TRUE, scale=FALSE) before injecting outliers.while loop checking if sum(abs(Q2)) > 1 to re-run the Q2() function if PPCA becomes unstable.pca(), causing the algorithms to fail. Fix: Ensure the input matrix or ExpressionSet contains only numeric values.impute.knn) of missing microarray data.This skill is the knowledge layer — when, why, and how to use pcamethods. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.
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The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
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This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
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apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
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