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High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation.

It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data.

A fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs.

Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles.

CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions.

This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data.

Recurrent breakpoint gene detection on copy number aberration profiles.

This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software.

Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects.

The package helps with the assessment and correction of RNA degradation effects in Affymetrix 3' expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation.

apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.

An R package for subset-based analysis of heterogeneous traits and disease subtypes. The package allows the user to search through all possible subsets of z-scores to identify the subset of traits giving the best meta-analyzed z-score. Further, it returns a p-value adjusting for the multiple-testing involved in the search. It also allows for searching for the best combination of disease subtypes associated with each variant.

For RNA sequencing count data, BADER fits a Bayesian hierarchical model. The algorithm returns the posterior probability of differential expression for each gene between two groups A and B. The joint posterior distribution of the variables in the model can be returned in the form of posterior samples, which can be used for further down-stream analyses such as gene set enrichment.

Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search

Provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform.

Biclustering Analysis and Results Exploration.

The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.1101/525709v2 and https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of module repertoire analyses can be represented in a fingerprint format, where red and blue spots indicate increases or decreases in module activity. These spots are subsequently represented either on a grid, with each position being assigned to a given module, or in a heatmap where the samples are arranged in columns and the modules in rows.

Package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg.

Interactvive graphics in a web browser from R, using websockets and JSON.

With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples.

The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).

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.

A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs.

This package implements a Naive Bayes classifier for accurately differentiating true polyadenylation sites (pA sites) from oligo(dT)-mediated 3' end sequencing such as PAS-Seq, PolyA-Seq and RNA-Seq by filtering out false polyadenylation sites, mainly due to oligo(dT)-mediated internal priming during reverse transcription. The classifer is highly accurate and outperforms other heuristic methods.

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).

clustSIGNAL: clustering of Spatially Informed Gene expression with Neighbourhood Adapted Learning. A tool for adaptively smoothing and clustering gene expression data. clustSIGNAL uses entropy to measure heterogeneity of cell neighbourhoods and performs a weighted, adaptive smoothing, where homogeneous neighbourhoods are smoothed more and heterogeneous neighbourhoods are smoothed less. This not only overcomes data sparsity but also incorporates spatial context into the gene expression data. The resulting smoothed gene expression data is used for clustering and could be used for other downstream analyses.

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.

This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge.

This package is an extension to CellNOptR. It contains additional functionality needed to simulate and train a prior knowledge network to experimental data using constrained fuzzy logic (cFL, rather than Boolean logic as is the case in CellNOptR). Additionally, this package will contain functions to use for the compilation of multiple optimization results (either Boolean or cFL).

Logic based ordinary differential equation (ODE) add-on to CellNOptR.

A normalization and copy number variation calling procedure for whole exome DNA sequencing data. CODEX relies on the availability of multiple samples processed using the same sequencing pipeline for normalization, and does not require matched controls. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and targeting and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data.

Gene Set Enrichment Analysis of P-value based statistics for outlier gene detection in dataset merged from multiple studies

This explorative ordination method combines quasi-likelihood estimation, compositional regression models and latent variable models for integrative visualization of several omics datasets. Both unconstrained and constrained integration are available. The results are shown as interpretable, compositional multiplots.

Single Cell Fluidigm Spot Detector.

algorithm for determining cluster count and membership by stability evidence in unsupervised analysis

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

Correspondence analysis (CA) is a matrix factorization method, and is similar to principal components analysis (PCA). Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale. The corral package implements CA for dimensionality reduction of a single matrix of single-cell data, as well as a multi-table adaptation of CA that leverages data-optimized scaling to align data generated from different sequencing platforms by projecting into a shared latent space. corral utilizes sparse matrices and a fast implementation of SVD, and can be called directly on Bioconductor objects (e.g., SingleCellExperiment) for easy pipeline integration. The package also includes additional options, including variations of CA to address overdispersion in count data (e.g., Freeman-Tukey chi-squared residual), as well as the option to apply CA-style processing to continuous data (e.g., proteomic TOF intensities) with the Hellinger distance adaptation of CA.

CrispRVariants provides tools for analysing the results of a CRISPR-Cas9 mutagenesis sequencing experiment, or other sequencing experiments where variants within a given region are of interest. These tools allow users to localize variant allele combinations with respect to any genomic location (e.g. the Cas9 cut site), plot allele combinations and calculate mutation rates with flexible filtering of unrelated variants.

Tools for export and import classification trees and clusters to other programs

Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models

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.

Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples.

Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes.

Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093.

This package predicts a drug’s primary target(s) or secondary target(s) by integrating large-scale genetic and drug screens from the Cancer Dependency Map project run by the Broad Institute. It further investigates whether the drug specifically targets the wild-type or mutated target forms. To show how to use this package in practice, we provided sample data along with step-by-step example.

Identifying distinct subpopulations through multiscale time series analysis

DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions.

performing all the steps of gene expression meta-analysis considering the possible existence of missing genes. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO datasets and show graphical representations of the results.

This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values.

This package provides functionality for performing divergence analysis as presented in Dinalankara et al, "Digitizing omics profiles by divergence from a baseline", PANS 2018. This allows the user to simplify high dimensional omics data into a binary or ternary format which encapsulates how the data is divergent from a specified baseline group with the same univariate or multivariate features.