tidySingleCellExperiment is an adapter that abstracts the SingleCellExperiment container in the form of a tibble'. This allows *tidy* data manipulation, nesting, and plotting. For example, a tidySingleCellExperiment is directly compatible with functions from tidyverse packages `dplyr` and `tidyr`, as well as plotting with `ggplot2` and `plotly`. In addition, the package provides various utility functions specific to single-cell omics data analysis (e.g., aggregation of cell-level data to pseudobulks).

This packages contains data to be used with the tofsims package.

This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages.

The tuberculosis R/Bioconductor package features tuberculosis gene expression data for machine learning. All human samples from GEO that did not come from cell lines, were not taken postmortem, and did not feature recombination have been included. The package has more than 10,000 samples from both microarray and sequencing studies that have been processed from raw data through a hyper-standardized, reproducible pipeline.

This data package contains timecourse gene expression data sets. The first dataset, from Shoemaker et al, consists of microarray samples from lung tissue of mice exposed to different influenzy strains from 14 timepoints. The two other datasets are leaf and root samples from sorghum crops exposed to pre- and post-flowering drought stress and a control condition, sampled across the plants lifetime.

R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expressions are altered.

traseR performs GWAS trait-associated SNP enrichment analyses in genomic intervals using different hypothesis testing approaches, also provides various functionalities to explore and visualize the results.

Exposes an annotation databases generated from BioMart by exposing these as TxDb objects.

Analyze thermal proteome profiling (TPP) experiments with varying temperatures (TR) or compound concentrations (CCR).

This is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. It can perform unsupervised feature extraction. It uses tensor decomposition. It is applicable to gene expression, DNA methylation, and histone modification etc. It can perform multiomics analysis. It is also potentially applicable to single cell omics data sets.

TTMap is a clustering method that groups together samples with the same deviation in comparison to a control group. It is specially useful when the data is small. It is parameter free.

`tidyCoverage` framework enables tidy manipulation of collections of genomic tracks and features using `tidySummarizedExperiment` methods. It facilitates the extraction, aggregation and visualization of genomic coverage over individual or thousands of genomic loci, relying on `CoverageExperiment` and `AggregatedCoverage` classes. This accelerates the integration of genomic track data in genomic analysis workflows.

Single-cell RNA-seq data for on PBMC cells, generated by 10X Genomics.

Exposes an annotation databases generated from BioMart by exposing these as TxDb objects.

This package implements an interactive, scientific analysis pipeline for high-dimensional cytometry data built using tidy data principles. It is specifically designed to play well with both the tidyverse and Bioconductor software ecosystems, with functionality for reading/writing data files, data cleaning, preprocessing, clustering, visualization, modeling, and other quality-of-life functions. tidytof implements a "grammar" of high-dimensional cytometry data analysis.

The twoddpcr package takes Droplet Digital PCR (ddPCR) droplet amplitude data from Bio-Rad's QuantaSoft and can classify the droplets. A summary of the positive/negative droplet counts can be generated, which can then be used to estimate the number of molecules using the Poisson distribution. This is the first open source package that facilitates the automatic classification of general two channel ddPCR data. Previous work includes definetherain (Jones et al., 2014) and ddpcRquant (Trypsteen et al., 2015) which both handle one channel ddPCR experiments only. The ddpcr package available on CRAN (Attali et al., 2016) supports automatic gating of a specific class of two channel ddPCR experiments only.

This package provides methods to perform trajectory analysis based on a minimum spanning tree constructed from cluster centroids. Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented.

Starting from one SBML file, it extracts information from each listOfCompartments, listOfSpecies and listOfReactions element by saving them into data frames. Each table provides one row for each entity (i.e. either compartment, species, reaction or speciesReference) and one set of columns for the attributes, one column for the content of the notes subelement and one set of columns for the content of the annotation subelement.

transomics2cytoscape generates a file for 3D transomics visualization by providing input that specifies the IDs of multiple KEGG pathway layers, their corresponding Z-axis heights, and an input that represents the edges between the pathway layers. The edges are used, for example, to describe the relationships between kinase on a pathway and enzyme on another pathway. This package automates creation of a transomics network as shown in the figure in Yugi.2014 (https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape automation (https://doi.org/10.1186/s13059-019-1758-4).

It searches for relevant associations of transcription factors with a transcription factor target, in specific genomic regions. It also allows to evaluate the Importance Index distribution of transcription factors (and combinations of transcription factors) in association rules.

This package provides raw files recorded on different Liquid Chromatography Mass Spectrometry (LC-MS) instruments. All included MS instruments are manufactured by Thermo Fisher Scientific and belong to the Orbitrap Tribrid or Q Exactive Orbitrap family of instruments. Despite their common origin and shared hardware components, e.g., Orbitrap mass analyser, the above instruments tend to write data in different "dialects" in a shared binary file format (.raw). The intention behind tartare is to provide complex but slim real-world files that can be used to make code robust with respect to this diversity. In other words, it is intended for enhanced unit testing. The package is considered to be used with the rawrr package and the Spectra MsBackends.

TargetScan miRNA target predictions for human assembled using data from the TargetScan website. TargetScan predicts biological targets of miRNAs by searching for the presence of conserved 8mer and 7mer sites that match the seed region of each miRNA. Also identified are sites with mismatches in the seed region that are compensated by conserved 3 pairing. In mammals, predictions are ranked based on the predicted efficacy of targeting as calculated using the context scores of the sites.

This package provides a package containing an environment representing the Test3.CDF file.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was Test3\_probe\_tab.