Many two-colour hybridizations suffer from a dye bias that is both gene-specific and slide-specific. The former depends on the content of the nucleotide used for labeling; the latter depends on the labeling percentage. The slide-dependency was hitherto not recognized, and made addressing the artefact impossible. Given a reasonable number of dye-swapped pairs of hybridizations, or of same vs. same hybridizations, both the gene- and slide-biases can be estimated and corrected using the GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009), doi:10.1038/msb.2009.21).

Data package providing example DNA methylation files used in the DMRsegal vignette and examples. Includes a sorted beta matrix as a tab-delimited, bgzip-compressed file and a matching phenotype table. The data contains 10 healthy and 10 cancer samples, and preprocessing has already been performed on the beta values.

Assorted files generated from droplet-based single-cell protocols, to be used for testing functions in DropletUtils. Primarily intended for storing files that directly come out of processing pipelines like 10X Genomics CellRanger software, prior to the formation of a SingleCellExperiment object. Unlike other packages, this is not designed to provide objects that are immediately ready for analysis.

This package provides a reproducible and modular workflow for absolute microbial quantification using spike-in controls. Supports both single spike-in taxa and synthetic microbial communities with user-defined spike-in volumes and genome copy numbers. Compatible with phyloseq and TreeSummarizedExperiment (TSE) data structures. The package implements methods for spike-in validation, preprocessing, scaling factor estimation, absolute abundance conversion, bias correction, and normalization. Facilitates downstream statistical analyses with DESeq2', edgeR', and other Bioconductor-compatible methods. Visualization tools are provided via ggplot2', ggtree', and related packages. Includes detailed vignettes, case studies, and function-level documentation to guide users through experimental design, quantification, and interpretation.

dominatRData is a data package useful for showcasing dominatR examples. dominatR is an R package for quantifying and visualizing feature dominance in datasets. dominatR makes use of entropy-based triangular projections and compositional comparison metrics.

Convert between different data formats used by differential gene expression analysis tools.

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 plotting functions for results from the derfinder package. This helps separate the graphical dependencies required for making these plots from the core functionality of derfinder.

DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864.

distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group.

DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results.

The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences.

The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated.

Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available.

Data package which provides default drug and disease expression profiles for the DvD package.

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.

dominatR is an R package for quantifying and visualizing feature dominance in datasets. dominatR applies concepts drawn from physics such as center of mass and shannon's entropy to effectively visualize features (e.g. genes) that are present within a specific context or condition. The package integrates, dataframes, matrices and SummerizedExperiment objects and is able to perform common genomic normalization methods. The key aspect is the generation of plots that serve to highlight context-relevant feature dominance.

This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred viewpoint region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments.

DoRothEA is a gene regulatory network containing signed transcription factor (TF) - target gene interactions. DoRothEA regulons, the collection of a TF and its transcriptional targets, were curated and collected from different types of evidence for both human and mouse. A confidence level was assigned to each TF-target interaction based on the number of supporting evidence.

This package implements methods and an evaluation framework to infer differential co-expression/association networks. Various methods are implemented and can be evaluated using simulated datasets. Inference of differential co-expression networks can allow identification of networks that are altered between two conditions (e.g., health and disease).

Affymetrix Affymetrix Drosophila_2 Array annotation data (chip drosophila2) assembled using data from public repositories.

DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components.

The damidBind package provides a straightforward formal analysis pipeline to analyse and explore differential DamID binding, gene transcription or chromatin accessibility between two conditions. The package imports processed data from DamID-seq experiments, either as external raw files in the form of binding bedGraphs and GFF/BED peak calls, or as internal lists of GRanges objects. After optionally normalising data, combining peaks across replicates and determining per-replicate peak occupancy, the package links bound loci to nearby genes. For RNA Polymerase DamID data, the package calculates occupancy over genes, and optionally calcualates the FDR of significantly-enriched gene occupancy. damidBind then uses either limma (for conventional log2 ratio DamID binding data) or NOIseq (for counts-based CATaDa chromatin accessibility data) to identify differentially-enriched regions, or differentially epxressed genes, between two conditions. The package provides a number of visualisation tools (volcano plots, Gene Ontology enrichment plots via ClusterProfiler and proportional Venn diagrams via BioVenn for downstream data exploration and analysis. An powerful, interactive IGV genome browser interface (powered by Shiny and igvShiny) allows users to rapidly and intuitively assess significant differentially-bound regions in their genomic context.

DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data.