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This package provides a Bioconductor data package for the Grasso (2012) Prostate Cancer dataset.
Platform Design Info for Affymetrix RheGene-1_0-st.
The package provides `rlang` data masks for the SummarizedExperiment class. The enables the evaluation of unquoted expression in different contexts of the SummarizedExperiment object with optional access to other contexts. The goal for `plyxp` is for evaluation to feel like a data.frame object without ever needing to unwind to a rectangular data.frame.
Platform Design Info for The Manufacturer's Name MOE430A.
Platform Design Info for The Manufacturer's Name HG-U133A_tag.
Platform Design Info for The Manufacturer's Name Canine_2.
Platform Design Info for NimbleGen 2006-07-18_mm8_refseq_promoter.
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.
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>.
Platform Design Info for The Manufacturer's Name HG-U133A_2.
Algorithm and tools for in silico pack-TYPE transposon discovery. Filters a given genome for properties unique to DNA transposons and provides tools for the investigation of returned matches. Sequences are input in DNAString format, and ranges are returned as a dataframe (in the format returned by as.dataframe(GRanges)).
Platform Design Info for The Manufacturer's Name Barley1.
Regularization and score distributions for position count matrices.
This package contains the data employed in the vignette of the PathNet package. These data belong to the following publication: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J., Source Code for Biology and Medicine 2012 Sep 24;7(1):10.
This package provides a Bioconductor data package for the Ross-Adams (2015) Prostate Cancer dataset.
Platform Design Info for Affymetrix MoGene-2_1-st.
Platform Design Info for NimbleGen hg18_60mer_expr.
Store UCSC phyloP mm39 conservation scores AnnotationHub Resource Metadata. Provide provenance and citation information for UCSC phyloP mm39 conservation score AnnotationHub resources. Illustrate in a vignette how to access those resources.
Platform Design Info for Affymetrix MoGene-1_0-st-v1.
This package provides a package containing an environment representing the Plasmodium_Anopheles.cdf file.
Platform Design Info for The Manufacturer's Name HG-U133A.
Platform Design Info for Affymetrix RaGene-2_0-st.
Platform Design Info for Affymetrix MarGene-1_1-st.
The phenomis package provides methods to perform post-processing (i.e. quality control and normalization) as well as univariate statistical analysis of single and multi-omics data sets. These methods include quality control metrics, signal drift and batch effect correction, intensity transformation, univariate hypothesis testing, but also clustering (as well as annotation of metabolomics data). The data are handled in the standard Bioconductor formats (i.e. SummarizedExperiment and MultiAssayExperiment for single and multi-omics datasets, respectively; the alternative ExpressionSet and MultiDataSet formats are also supported for convenience). As a result, all methods can be readily chained as workflows. The pipeline can be further enriched by multivariate analysis and feature selection, by using the ropls and biosigner packages, which support the same formats. Data can be conveniently imported from and exported to text files. Although the methods were initially targeted to metabolomics data, most of the methods can be applied to other types of omics data (e.g., transcriptomics, proteomics).