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pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) <https://doi.org/10.1093/bioinformatics/btn458>; Chen et al. (2010) <https://doi.org/10.1002/gepi.20532>; and Chen (2011) <https://doi.org/10.2202/1544-6115.1697>.
Pqsfinder detects DNA and RNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, pqsfinder is able to detect G4s folded from imperfect G-runs containing bulges or mismatches or G4s having long loops. Pqsfinder also assigns an integer score to each hit that was fitted on G4 sequencing data and corresponds to expected stability of the folded G4.
Platform Design Info for Affymetrix SoyGene-1_1-st.
Platform Design Info for Affymetrix RUSGene-1_0-st.
Base annotation databases for pig, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.
Package for the position related analysis of quantitative functional genomics data.
This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12.
This package provides a Bioconductor data package for the Grasso (2012) Prostate Cancer dataset.
Platform Design Info for Affymetrix RabGene-1_1-st.
Platform Design Info for Affymetrix MoGene-1_1-st-v1.
Platform Design Info for The Manufacturer's Name Mu11KsubB.
Platform Design Info for Affymetrix DroGene-1_1-st.
Implemented temporal PageRank analysis as defined by Rozenshtein and Gionis. Implemented multiplex PageRank as defined by Halu et al. Applied temporal and multiplex PageRank in gene regulatory network analysis.
Platform Design Info for The Manufacturer's Name Drosophila_2.
Affymetrix Affymetrix Porcine Array annotation data (chip porcine) assembled using data from public repositories.
This package provides a package for processing protein mass spectrometry data.
Platform Design Info for NimbleGen 2006-07-18_mm8_refseq_promoter.
Platform Design Info for Affymetrix MedGene-1_0-st.
Platform Design Info for The Manufacturer's Name HG-U133B.
Platform Design Info for Affymetrix HuGene-2_0-st.
Platform Design Info for The Manufacturer's Name Plasmodium_Anopheles.
This package provides a simple framework to facilitate the comparison of pipelines involving various steps and parameters. The `pipelineDefinition` class represents pipelines as, minimally, a set of functions consecutively executed on the output of the previous one, and optionally accompanied by step-wise evaluation and aggregation functions. Given such an object, a set of alternative parameters/methods, and benchmark datasets, the `runPipeline` function then proceeds through all combinations arguments, avoiding recomputing the same step twice and compiling evaluations on the fly to avoid storing potentially large intermediate data.
Platform Design Info for The Manufacturer's Name Yeast_2.
Significance assessment for distance measures of time-course protein profiles.