Package includes functions to analyze and mask microarray expression data.

MiDAS is a R package for immunogenetics data transformation and statistical analysis. MiDAS accepts input data in the form of HLA alleles and KIR types, and can transform it into biologically meaningful variables, enabling HLA amino acid fine mapping, analyses of HLA evolutionary divergence, KIR gene presence, as well as validated HLA-KIR interactions. Further, it allows comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS closes a gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to T cell, Natural Killer cell, and disease biology.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was MG-U74Av2\_probe\_tab.

Modified quantile normalization for omics or other matrix-like data distorted in location and scale.

Store minor allele frequency data from the Exome Aggregation Consortium (ExAC release 1.0 subset of nonTCGA exomes) for the human genome version hs37d5.

This package provides a package containing an environment representing the Medicago.cdf file.

Computes Mantel cluster correlations from a (p x n) numeric data matrix (e.g. microarray gene-expression data).

Data sets for the book Modern Statistics for Modern Biology', S.P. Holmes and W. Huber.

The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile.

This package provides tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies.

miaDash provides a Graphical User Interface for the exploration of microbiome data. This way, no knowledge of programming is required to perform analyses. Datasets can be imported, manipulated, analysed and visualised with a user-friendly interface.

This package provides a collection of datasets to accompany the R package MOFA and illustrate running and analysing MOFA models.

This package provides a package containing an environment representing the MG_U74C.cdf file.

Store minor allele frequency data from the Genome Aggregation Database (gnomAD exomes release 2.1) for the human genome version GRCh38.

This package provides a package containing an environment representing the Maize.cdf file.

This is a package for the discovery of regulatory regions from Bis-seq data.

This package provides functions for the analysis of data generated by the multiplex substrate profiling by mass spectrometry for proteases (MSP-MS) method. Data exported from upstream proteomics software is accepted as input and subsequently processed for analysis. Tools for statistical analysis, visualization, and interpretation of the data are provided.

This package provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them.

Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K .idat files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values.

This package provides a model for semi-supervised prioritisation of genes integrating network data, phenotypes and additional prior knowledge about TP and TN gene labels from the literature or experts.

The purpose of ncGTW is to help XCMS for LC-MS data alignment. Currently, ncGTW can detect the misaligned feature groups by XCMS, and the user can choose to realign these feature groups by ncGTW or not.

# NetActivity enables to compute gene set scores from previously trained sparsely-connected autoencoders. The package contains a function to prepare the data (`prepareSummarizedExperiment`) and a function to compute the gene set scores (`computeGeneSetScores`). The package `NetActivityData` contains different pre-trained models to be directly applied to the data. Alternatively, the users might use the package to compute gene set scores using custom models.

Nucleosome positioning for Tiling Arrays and NGS experiments.

This package provides univariate and multivariate statistics for feature prioritization in untargeted LC-MS metabolomics research.