Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots.

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.

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

ExperimentHub package containing datasets for use in the TENET package's vignette and function examples. These include a variety of different objects to illustrate different datasets used in TENET functions. Where applicable, all datasets are aligned to the hg38 human genome.

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

Exposes an annotation databases generated from BioMart by exposing these as TxDb objects. This package is for Arabidopsis thaliana (taxID: 3702). The BioMart plantsmart release number is 51.

The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC).

This package provides methods to create complex IGV genome browser sessions and dynamic IGV reports in HTML pages.

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

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

tLOH, or transcriptomicsLOH, assesses evidence for loss of heterozygosity (LOH) in pre-processed spatial transcriptomics data. This tool requires spatial transcriptomics cluster and allele count information at likely heterozygous single-nucleotide polymorphism (SNP) positions in VCF format. Bayes factors are calculated at each SNP to determine likelihood of potential loss of heterozygosity event. Two plotting functions are included to visualize allele fraction and aggregated Bayes factor per chromosome. Data generated with the 10X Genomics Visium Spatial Gene Expression platform must be pre-processed to obtain an individual sample VCF with columns for each cluster. Required fields are allele depth (AD) with counts for reference/alternative alleles and read depth (DP).

tidyFlowCore bridges the gap between flow cytometry analysis using the flowCore Bioconductor package and the tidy data principles advocated by the tidyverse. It provides a suite of dplyr-, ggplot2-, and tidyr-like verbs specifically designed for working with flowFrame and flowSet objects as if they were tibbles; however, your data remain flowCore data structures under this layer of abstraction. tidyFlowCore enables intuitive and streamlined analysis workflows that can leverage both the Bioconductor and tidyverse ecosystems for cytometry data.

Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics.

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.

Single-cell RNA-seq data for 1.3 million brain cells from E18 mice, generated by 10X Genomics.

Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) <doi: 10.1038/nprot.2013.150>. Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data.

This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality.

RNA-seq count data from Pickrell et al. (2010) employed to illustrate the use of the Poisson-Tweedie family of distributions with the tweeDEseq package.

The TMSig package contains tools to prepare, analyze, and visualize named lists of sets, with an emphasis on molecular signatures (such as gene or kinase sets). It includes fast, memory efficient functions to construct sparse incidence and similarity matrices and filter, cluster, invert, and decompose sets. Additionally, bubble heatmaps can be created to visualize the results of any differential or molecular signatures analysis.

The package imports the result of tRNAscan-SE as a GRanges object.

This package provides a package containing an environment representing the Tomato.cdf file.

This package performs a Gene Set Analysis with the approach adopted by PADOG on the genes that are reported as translationally regulated (ie. exhibit a significant change in TE) by the DeltaTE package. It can be used on its own to see the impact of translation regulation on gene sets, but it is also integrated as an additional analysis method within ReactomeGSA, where results are further contextualised in terms of pathways and directionality of the change.

Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features.

The Cancer Genome Atlas (TCGA) is applying genomics technologies to over 20 different types of cancer. This package contains a small set of 450k array data in idat format.