dStruct identifies differentially reactive regions from RNA structurome profiling data. dStruct is compatible with a broad range of structurome profiling technologies, e.g., SHAPE-MaP, DMS-MaPseq, Structure-Seq, SHAPE-Seq, etc. See Choudhary et al., Genome Biology, 2019 for the underlying method.

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.

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.

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

This package provides a package containing an environment representing the DrosGenome1.CDF file.

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.

The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage.

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.

dominoSignal is a package developed to analyze cell signaling through ligand - receptor - transcription factor networks in scRNAseq data. It takes as input information transcriptomic data, requiring counts, z-scored counts, and cluster labels, as well as information on transcription factor activation (such as from SCENIC) and a database of ligand and receptor pairings (such as from CellPhoneDB). This package creates an object storing ligand - receptor - transcription factor linkages by cluster and provides several methods for exploring, summarizing, and visualizing the analysis.

This package contains implementation of DecontX (Yang et al. 2020), a decontamination algorithm for single-cell RNA-seq, and DecontPro (Yin et al. 2023), a decontamination algorithm for single cell protein expression data. DecontX is a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. DecontPro is a Bayesian method that estimates the level of contamination from ambient and background sources in CITE-seq ADT dataset and decontaminate the dataset.

Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm.

Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge.

DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values.

This package contains 9 data objects supporting functionality and examples of the Bioconductor package DMRcate.

This package provides a convenient way to access the LINCS Signatures available in the iLINCS database. These signatures include Consensus Gene Knockdown Signatures, Gene Overexpression signatures and Chemical Perturbagen Signatures. It also provides a way to enter your own transcriptomic signatures and identify concordant and discordant signatures in the LINCS database.

Duplication rate quality control for RNA-Seq datasets.

The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot.

Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements.

Damsel provides an end to end analysis of DamID data. Damsel takes bam files from Dam-only control and fusion samples and counts the reads matching to each GATC region. edgeR is utilised to identify regions of enrichment in the fusion relative to the control. Enriched regions are combined into peaks, and are associated with nearby genes. Damsel allows for IGV style plots to be built as the results build, inspired by ggcoverage, and using the functionality and layering ability of ggplot2. Damsel also conducts gene ontology testing with bias correction through goseq, and future versions of Damsel will also incorporate motif enrichment analysis. Overall, Damsel is the first package allowing for an end to end analysis with visual capabilities. The goal of Damsel was to bring all the analysis into one place, and allow for exploratory analysis within R.

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.

DiffLogo is an easy-to-use tool to visualize motif differences.

The functions support identification and annotation of hotspot residues in proteins. These are individual amino acids that accumulate mutations at a much higher rate than their surrounding regions.

DoReMiTra is an R data package providing access to curated transcriptomic datasets related to blood radiation, with a focus on neutron, x-ray, and gamma ray studies. It is designed to facilitate radiation biology research and support data exploration and reproducibility in radiation transcriptomics. All datasets are provided as SummarizedExperiment objects, allowing seamless integration with the Bioconductor ecosystem.

DEMAND predicts Drug MoA by interrogating a cell context specific regulatory network with a small number (N >= 6) of compound-induced gene expression signatures, to elucidate specific proteins whose interactions in the network is dysregulated by the compound.