Data driven strategy to find hidden groups of patients with complex diseases using clinical data. ClustAll facilitates the unsupervised identification of multiple robust stratifications. ClustAll, is able to overcome the most common limitations found when dealing with clinical data (missing values, correlated data, mixed data types).

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 provides model data and functions for easily using machine learning models that use data from the DNA methylome to classify cancer type and phenotype from a sample. The primary motivation for the development of this package is to abstract away the granular and accessibility-limiting code required to utilize machine learning models in R. Our package provides this abstraction for RandomForest, e1071 Support Vector, Extreme Gradient Boosting, and Tensorflow models. This is paired with an ExperimentHub component, which contains models developed for epigenetic cancer classification and predicting phenotypes. This includes CNS tumor classification, Pan-cancer classification, race prediction, cell of origin classification, and subtype classification models. The package links to our models on ExperimentHub. The package currently supports HM450, EPIC, EPICv2, MSA, and MM285.

This package contains functionality to run differential gene co-expression across two different conditions. The algorithm is inspired by Voigt et al. 2017 and finds Conserved, Specific and Differentiated genes (hence the name CSD). This package include efficient and variance calculation by bootstrapping and Welford's algorithm.

CPSM provides a comprehensive computational pipeline for predicting survival probability and risk groups in cancer patients. The package includes steps for data preprocessing, training/test split, and normalization. It enables feature selection using univariate survival analysis and computes a LASSO-based prognostic index (PI) score. CPSM supports the development of predictive models using various feature sets and offers a suite of visualization tools, including survival curves based on predicted probabilities, barplots for predicted mean and median survival times, KM plots overlaid with individual survival predictions, and nomograms for estimating 1-, 3-, 5-, and 10-year survival probabilities. This makes CPSM a versatile tool for survival analysis in cancer research.

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.

This package provides a package containing an environment representing the Celegans.CDF file.

Cross-Species Investigation and Analysis (CoSIA) is a package that provides researchers with an alternative methodology for comparing across species and tissues using normal wild-type RNA-Seq Gene Expression data from Bgee. Using RNA-Seq Gene Expression data, CoSIA provides multiple visualization tools to explore the transcriptome diversity and variation across genes, tissues, and species. CoSIA uses the Coefficient of Variation and Shannon Entropy and Specificity to calculate transcriptome diversity and variation. CoSIA also provides additional conversion tools and utilities to provide a streamlined methodology for cross-species comparison.

Affymetrix Affymetrix Chicken Array annotation data (chip chicken) assembled using data from public repositories.

This package provides S4 classes for general nucleases, CRISPR nucleases, CRISPR nickases, and base editors.Several CRISPR-specific genome arithmetic functions are implemented to help extract genomic coordinates of spacer and protospacer sequences. Commonly-used CRISPR nuclease objects are provided that can be readily used in other packages. Both DNA- and RNA-targeting nucleases are supported.

The Broad Institute's Connectivity Map (cmap02) is a "large reference catalogue of gene-expression data from cultured human cells perturbed with many chemicals and genetic reagents", containing more than 7000 gene expression profiles and 1300 small molecules.

It fits correlation motif model to multiple studies to detect study specific differential expression patterns.

Single Cell Fluidigm Spot Detector.

CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses.

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

CCPlotR is an R package for visualising results from tools that predict cell-cell interactions from single-cell RNA-seq data. These plots are generic and can be used to visualise results from multiple tools such as Liana, CellPhoneDB, NATMI etc.

Spatial homogeneous regions (SHRs) in tissues are domains that are homogenous with respect to cell type composition. We present a method for identifying SHRs using spatial transcriptomics data, and demonstrate that it is efficient and effective at finding SHRs for a wide variety of tissue types. concordex relies on analysis of k-nearest-neighbor (kNN) graphs. The tool is also useful for analysis of non-spatial transcriptomics data, and can elucidate the extent of concordance between partitions of cells derived from clustering algorithms, and transcriptomic similarity as represented in kNN graphs.

This data package contains chimp and human brain data extracted from the ArrayExpress accession E-AFMX-2. Both human and chimp RNAs were run on human hgu95av2 Affymetrix arrays. It is a useful dataset for tutorials.

This package performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found.

Sorted and indexed BAM files for ChIP-seq libraries, for use in the chipseqDB workflow. BAM indices are also included.

An annotation package for use with ChemmineR. This package includes data from DrugBank. DUD data can be downloaded using the "DUD()" function in ChemmineR.

Perform Canonical correlation between two forms of high demensional genetic data, and associate the first compoent of each form of data with a specific biologically interesting pattern of associations with multiple endpoints. A probe level analysis is also implemented.

Affymetrix Affymetrix Canine Array annotation data (chip canine) assembled using data from public repositories.

Data package which provides default drug profiles for the DrugVsDisease package as well as associated gene lists and data clusters used by the DrugVsDisease package.