This package provide a method for doing gene set analysis based on multiple omics data.

Data package containing a multi-sample multi-group spatial dataset in SpatialExperiment Bioconductor object format.

This package provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way.

The package provides a comprehensive mapping table of nine different Metabolite ID formats and their common name. The data has been collected and merged from four publicly available source, including HMDB, Comptox Dashboard, ChEBI, and the graphite Bioconductor R package.

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

This package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%).

This package is a implementation of biclustering ensemble method MoSBi (Molecular signature Identification from Biclustering). MoSBi provides standardized interfaces for biclustering results and can combine their results with a multi-algorithm ensemble approach to compute robust ensemble biclusters on molecular omics data. This is done by computing similarity networks of biclusters and filtering for overlaps using a custom error model. After that, the louvain modularity it used to extract bicluster communities from the similarity network, which can then be converted to ensemble biclusters. Additionally, MoSBi includes several network visualization methods to give an intuitive and scalable overview of the results. MoSBi comes with several biclustering algorithms, but can be easily extended to new biclustering algorithms.

Store minor allele frequency data from NHLBI TOPMed for the human genome version hg38.

Affymetrix mogene20 annotation data (chip mogene20sttranscriptcluster) assembled using data from public repositories.

Simulate a multigeneration methylation case versus control experiment with inheritance relation using a real control dataset.

Translating mature miRNA names to different miRBase versions, sequence retrieval, checking names for validity and detecting miRBase version of a given set of names (data from http://www.mirbase.org/).

This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types.

This package provides a package containing an environment representing the miRNA-2_0.cdf file.

gene target tabale of miRNA for human/mouse used for MiRaGE package.

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 miRNA-1\_0\_probe\_tab.

Two-stage measurement error model for correlation estimation with smaller bias than the usual sample correlation.

Example data for M3Drop package.

FHCRC Genomics Shared Resource Mu22v3 Annotation Data (Mu22v3) assembled using data from public repositories.

Indole-3-acetaldoxime (IAOx) represents an early intermediate of the biosynthesis of a variety of indolic secondary metabolites including the phytoanticipin indol-3-ylmethyl glucosinolate and the phytoalexin camalexin (3-thiazol-2'-yl-indole). Arabidopsis thaliana cyp79B2 cyp79B3 double knockout plants are completely impaired in the conversion of tryptophan to indole-3-acetaldoxime and do not accumulate IAOx-derived metabolites any longer. Consequently, comparative analysis of wild-type and cyp79B2 cyp79B3 plant lines has the potential to explore the complete range of IAOx-derived indolic secondary metabolites.

Data objects necessary to some mCSEA package functions. There are also example data objects to illustrate mCSEA package functionality.

Affymetrix Affymetrix MG_U74Cv2 Array annotation data (chip mgu74cv2) assembled using data from public repositories.

The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE.

multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner.

This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by otherBioConductor packages.