Define coerce methods for microarray data objects.

The curatedTBData is an R package that provides standardized, curated tuberculosis(TB) transcriptomic studies. The initial release of the package contains 49 studies. The curatedTBData package allows users to access tuberculosis trancriptomic efficiently and to make efficient comparison for different TB gene signatures across multiple datasets.

Genomic coordinates of CTCF binding sites, with strand orientation (directionality of binding). Position weight matrices (PWMs) from JASPAR, HOCOMOCO, CIS-BP, CTCFBSDB, SwissRegulon, Jolma 2013, were used to uniformly predict CTCF binding sites using FIMO (default settings) on human (hg18, hg19, hg38, T2T) and mouse (mm9, mm10, mm39) genome assemblies. Extra columns include motif/PWM name (e.g., MA0139.1), score, p-value, q-value, and the motif sequence. It is recommended to filter FIMO-predicted sites by 1e-6 p-value threshold instead of using the default 1e-4 threshold. Experimentally obtained CTCF-bound cis-regulatory elements from ENCODE SCREEN and predicted CTCF sites from CTCFBSDB are also included. Selected data are lifted over from a different genome assembly as we demonstrated liftOver is a viable option to obtain CTCF coordinates in different genome assemblies. CTCF sites obtained using JASPAR's MA0139.1 PWM and filtered at 1e-6 p-value threshold are recommended.

The curatedMetagenomicData package provides standardized, curated human microbiome data for novel analyses. It includes gene families, marker abundance, marker presence, pathway abundance, pathway coverage, and relative abundance for samples collected from different body sites. The bacterial, fungal, and archaeal taxonomic abundances for each sample were calculated with MetaPhlAn3, and metabolic functional potential was calculated with HUMAnN3. The manually curated sample metadata and standardized metagenomic data are available as (Tree)SummarizedExperiment objects.

Package to retrieve and visualize data from the Comparative Toxicogenomics Database (http://ctdbase.org/). The downloaded data is formated as DataFrames for further downstream analyses.

Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations.

This is a package for analysis of case-control data in genetic epidemiology. It provides a set of statistical methods for evaluating gene-environment (or gene-genes) interactions under multiplicative and additive risk models, with or without assuming gene-environment (or gene-gene) independence in the underlying population.

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 Chicken\_probe\_tab.

This package implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype.

Dimension Reduction for Array CGH Data with Minimal Information Loss.

CluMSID is a tool that aids the identification of features in untargeted LC-MS/MS analysis by the use of MS2 spectra similarity and unsupervised statistical methods. It offers functions for a complete and customisable workflow from raw data to visualisations and is interfaceable with the xmcs family of preprocessing packages.

This package provides functions that predict clinical outcomes using single cell data (such as flow cytometry data, RNA single cell sequencing data) without the requirement of cell gating or clustering.

Affymetrix clariomshuman annotation data (chip clariomshumantranscriptcluster) assembled using data from public repositories.

Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included.

In-silico cleavage of polypeptide sequences. The cleavage rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html.

CYPRESS is a cell-type-specific power tool. This package aims to perform power analysis for the cell-type-specific data. It calculates FDR, FDC, and power, under various study design parameters, including but not limited to sample size, and effect size. It takes the input of a SummarizeExperimental(SE) object with observed mixture data (feature by sample matrix), and the cell-type mixture proportions (sample by cell-type matrix). It can solve the cell-type mixture proportions from the reference free panel from TOAST and conduct tests to identify cell-type-specific differential expression (csDE) genes.

Affymetrix clariomdhuman annotation data (chip clariomdhumanprobeset) assembled using data from public repositories.

COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data.

clustComp is a package that implements several techniques for the comparison and visualisation of relationships between different clustering results, either flat versus flat or hierarchical versus flat. These relationships among clusters are displayed using a weighted bi-graph, in which the nodes represent the clusters and the edges connect pairs of nodes with non-empty intersection; the weight of each edge is the number of elements in that intersection and is displayed through the edge thickness. The best layout of the bi-graph is provided by the barycentre algorithm, which minimises the weighted number of crossings. In the case of comparing a hierarchical and a non-hierarchical clustering, the dendrogram is pruned at different heights, selected by exploring the tree by depth-first search, starting at the root. Branches are decided to be split according to the value of a scoring function, that can be based either on the aesthetics of the bi-graph or on the mutual information between the hierarchical and the flat clusterings. A mapping between groups of clusters from each side is constructed with a greedy algorithm, and can be additionally visualised.

Data from publicly available databases (GTEx, CCLE, TCGA and ENCODE) that go with CTexploreR in order to re-define a comprehensive and thoroughly curated list of CT genes and their main characteristics.

Base annotation databases for chicken, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.

This package implements classes and methods for large-scale SNP association studies.

The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network.

This R package provides an R Shiny application that enables the user to generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics. Create cancer studies and edit its metadata. Upload mutation data of a patient that will be concatenated to the data_mutation_extended.txt file of the study. Create and edit clinical patient data, sample data, and timeline data. Create custom timeline tracks for patients.