Package provides miRNASeq datasets from The Cancer Genome Atlas Project for all available cohorts types from http://gdac.broadinstitute.org/. Data format is explained here https://wiki.nci.nih.gov/display/TCGA/miRNASeq#miRNASeq-DataOverview Data from 2015-11-01 snapshot.

Base annotation databases for rhesus, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.

RegioneReloaded is a package that allows simultaneous analysis of associations between genomic region sets, enabling clustering of data and the creation of ready-to-publish graphs. It takes over and expands on all the features of its predecessor regioneR. It also incorporates a strategy to improve p-value calculations and normalize z-scores coming from multiple analysis to allow for their direct comparison. RegioneReloaded builds upon regioneR by adding new plotting functions for obtaining publication-ready graphs.

rfaRm provides a client interface to the Rfam database of RNA families. Data that can be retrieved include RNA families, secondary structure images, covariance models, sequences within each family, alignments leading to the identification of a family and secondary structures in the dot-bracket format.

This package provides a package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset.

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 Rat230\_2\_probe\_tab.

This package contains code to illustrate the Using R and Bioconductor for proteomics data analysis and Visualisation of proteomics data using R and Bioconductor manuscripts. The vignettes describe the code and data needed to reproduce the examples and figures described in the paper and functionality for proteomics visualisation. It also contain various function to discover R software for mass spectrometry and proteomics.

This package provides tools to harmonize bulk RNA-seq matrices, optionally apply batch correction, and train cross-validated classification models using ranger, glmnet, or xgboost. Supports leakage-safe feature selection, permutation importance, SHAP-based interpretability, and calibration methods (Platt or isotonic). Provides stability metrics across folds, embeddings (PCA/UMAP), ROC visualization, SHAP dependence plots, and tidy ranked-gene tables for downstream analysis.

RepViz enables the view of a genomic region in a simple and efficient way. RepViz allows simultaneous viewing of both intra- and intergroup variation in sequencing counts of the studied conditions, as well as their comparison to the output features (e.g. identified peaks) from user selected data analysis methods.The RepViz tool is primarily designed for chromatin data such as ChIP-seq and ATAC-seq, but can also be used with other sequencing data such as RNA-seq, or combinations of different types of genomic data.

R-package with shiny interface, provides a framework for the analysis of transcriptomics, proteomics and/or metabolomics data. The interface offers a guided experience for the user, from the definition of the experimental design to the integration of several omics table together. A report can be generated with all settings and analysis results.

R interface to the MELTING 5 program (https://www.ebi.ac.uk/biomodels/tools/melting/) to compute melting temperatures of nucleic acid duplexes along with other thermodynamic parameters.

This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling.

Data such as is contained in the two R data files in this package are required for the RITAN package examples. Users are highly encouraged to use their own or additional resources in conjunction with RITANdata. See the RITAN vignettes and RITAN.md for more information, such as gathering more up-to-date annotation data.

RolDE detects longitudinal differential expression between two conditions in noisy high-troughput data. Suitable even for data with a moderate amount of missing values.RolDE is a composite method, consisting of three independent modules with different approaches to detecting longitudinal differential expression. The combination of these diverse modules allows RolDE to robustly detect varying differences in longitudinal trends and expression levels in diverse data types and experimental settings.

This package provides a package containing an environment representing the RatToxFX.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 Rhesus\_probe\_tab.

Improves simultaneous inference under dependence of tests by estimating a collapsed null distribution through resampling. Accounting for the dependence between tests increases the power while reducing the variability of the false discovery proportion. This dependence is common in genomics applications, e.g. when combining flow cytometry measurements with microbiome sequence counts.

The package provides functions to read raw RT-qPCR data of different platforms.

This package wraps the functionality of the Thermo Fisher Scientic RawFileReader .NET 8.0 assembly. Within the R environment, spectra and chromatograms are represented by S3 objects. The package provides basic functions to download and install the required third-party libraries. The package is developed, tested, and used at the Functional Genomics Center Zurich, Switzerland.

Bioconductor-native infrastructure for handling large nanoporetech modkit bedMethyl pileup files from ONT data using HDF5Array and DelayedArray.

RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions.

This package provides a classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now.

The package analyzes the Curve ROC, identificates it among different types of Curve ROC and calculates the area under de curve through the method that is most accuracy. This package is able to standarizate proper and improper pAUC.

RedeR combines an R package with a stand-alone Java application for interactive visualization and manipulation of nested networks. Graph, node, and edge attributes can be configured using either graphical or command-line methods, following igraph syntax rules.