DNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. The package allows to predict, visualize and encode DNA shape features for statistical learning.

The ddPCRclust algorithm can automatically quantify the CPDs of non-orthogonal ddPCR reactions with up to four targets. In order to determine the correct droplet count for each target, it is crucial to both identify all clusters and label them correctly based on their position. For more information on what data can be analyzed and how a template needs to be formatted, please check the vignette.

Duplication rate quality control for RNA-Seq datasets.

This package provides `dplyr` verbs (`mutate`, `select`, `filter`, etc...) supporting `S4Vectors::DataFrame` objects. Importantly, this is achieved without conversion to an intermediate `tibble`. Adds grouping infrastructure to `DataFrame` which is respected by the transformation verbs.

Based on the standard DataFrame metaphor, we are trying to implement the feature of delayed operation on the DelayedDataFrame, with a slot of lazyIndex, which saves the mapping indexes for each column of DelayedDataFrame. Methods like show, validity check, [/[[ subsetting, rbind/cbind are implemented for DelayedDataFrame to be operated around lazyIndex. The listData slot stays untouched until a realization call e.g., DataFrame constructor OR as.list() is invoked.

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.

Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.

Mass-spectrometry based UPS proteomics data sets from Ramus C, Hovasse A, Marcellin M, Hesse AM, Mouton-Barbosa E, Bouyssie D, Vaca S, Carapito C, Chaoui K, Bruley C, Garin J, Cianferani S, Ferro M, Dorssaeler AV, Burlet-Schiltz O, Schaeffer C, Coute Y, Gonzalez de Peredo A. Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods. Data Brief. 2015 Dec 17;6:286-94 and Giai Gianetto, Q., Combes, F., Ramus, C., Bruley, C., Coute, Y., Burger, T. (2016). Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments. Proteomics, 16(1), 29-32.

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

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.

This package implements a DelayedArray of random values where the realization of the sampled values is delayed until they are needed. Reproducible sampling within any subarray is achieved by chunking where each chunk is initialized with a different random seed and stream. The usual distributions in the stats package are supported, along with scalar, vector and arrays for the parameters.

ChIA-PET example datasets and additional data for use with the diffloop package.

The R package decemedip is a novel computational paradigm developed for inferring the relative abundances of cell types and tissues measure by methylated DNA immunoprecipitation sequencing (MeDIP-Seq). This paradigm allows using reference data from other technologies such as microarray or WGBS.

DeeDeeExperiment is an S4 class extending the SingleCellExperiment class, designed to integrate and manage omics analysis results. It introduces two dedicated slots to store Differential Expression Analysis (DEA) results and Functional Enrichment Analysis (FEA) results, providing a structured approach for downstream analysis.

The goal of DELocal is to identify DE genes compared to their neighboring genes from the same chromosomal location. It has been shown that genes of related functions are generally very far from each other in the chromosome. DELocal utilzes this information to identify DE genes comparing with their neighbouring genes.

Analyze microarray data.

This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy. It provides estimates for each gene within each sample.

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.

The DNEA R package is the latest implementation of the Differential Network Enrichment Analysis algorithm and is the successor to the Filigree Java-application described in Iyer et al. (2020). The package is designed to take as input an m x n expression matrix for some -omics modality (ie. metabolomics, lipidomics, proteomics, etc.) and jointly estimate the biological network associations of each condition using the DNEA algorithm described in Ma et al. (2019). This approach provides a framework for data-driven enrichment analysis across two experimental conditions that utilizes the underlying correlation structure of the data to determine feature-feature interactions.

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.

The package DAPAR is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required (see `Prostar` package).

This package provides a tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance.

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

This package draws protein schematics from Uniprot API output. From the JSON returned by the GET command, it creates a dataframe from the Uniprot Features API. This dataframe can then be used by geoms based on ggplot2 and base R to draw protein schematics.