This package provides data from 6 samples across 2 groups from 450k methylation arrays.

This package provides full genome sequences for Caenorhabditis elegans (Worm) as provided by UCSC (ce10, Oct 2010) and stored in Biostrings objects.

This package offers interactive Shiny displays for Bioconductor objects. In addition, this package empowers users to develop engaging visualizations and interfaces for working with Bioconductor data.

This package is a collection of Strand-seq data. The main purpose is to demonstrate functionalities of the breakpointR package.

Gcrma adjusts for background intensities in Affymetrix array data which include optical noise and non-specific binding (NSB). The main function gcrma converts background adjusted probe intensities to expression measures using the same normalization and summarization methods as a Robust Multiarray Average (RMA). Gcrma uses probe sequence information to estimate probe affinity to NSB. The sequence information is summarized in a more complex way than the simple GC content. Instead, the base types (A, T, G or C) at each position along the probe determine the affinity of each probe. The parameters of the position-specific base contributions to the probe affinity is estimated in an NSB experiment in which only NSB but no gene-specific binding is expected.

The tRNA package allows tRNA sequences and structures to be accessed and used for subsetting. In addition, it provides visualization tools to compare feature parameters of multiple tRNA sets and correlate them to additional data. The tRNA package uses GRanges objects as inputs requiring only few additional column data sets.

INSPEcT (INference of Synthesis, Processing and dEgradation rates in Time-Course experiments) analyses 4sU-seq and RNA-seq time-course data in order to evaluate synthesis, processing and degradation rates and assess via modeling the rates that determines changes in mature mRNA levels.

This is a package to support identification of markers of rare cell types by looking at genes whose expression is confined in small regions of the expression space.

This package muscat provides various methods and visualization tools for DS(differential splicing) analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated "pseudobulk" data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.

This package contains functions and classes that are needed by the arrayCGH packages.

This package provides functions to estimate a bipartite graph of protein complex membership using AP-MS data.

This package contains microarray gene expression data on 57 bladder samples from 5 batches. The data are used as an illustrative example for the sva package.

This package provides tools to identify cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection.

This is a comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. It provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier.

This package provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results.

This package provides methods and models for handling zero-inflated single cell assay data.

This package reads Bruker NMR data directories both zipped and unzipped. It provides automated and efficient signal processing for untargeted NMR metabolomics. It is able to interpolate the samples, detect outliers, exclude regions, normalize, detect peaks, align the spectra, integrate peaks, manage metadata and visualize the spectra. After spectra processing, it can apply multivariate analysis on extracted data. Efficient plotting with 1-D data is also available. Basic reading of 1D ACD/Labs exported JDX samples is also available.

This package provides datasets needed for ChAMP including a test dataset and blood controls for CNA analysis.

This package identifies regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling.

This is a package for segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome. Both tumor-normal paired and tumor-only analyses are supported.

The enrichplot package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analyses. All the visualization methods are developed based on ggplot2 graphics.

Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis.

This package contains functions for exploratory oligonucleotide array analysis.

This R package can annotate variants, compute amino acid coding changes and predict coding outcomes.