Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc.

This package implements quantile smoothing. It contains a dataset used to produce human chromosomal ideograms for plotting purposes and a collection of arrays that contains data of chromosome 14 of 3 colorectal tumors. The package provides functions for painting chromosomal icons, chromosome or chromosomal idiogram and other types of plots. Quantsmooth offers options like converting chromosomal ids to their numeric form, retrieving the human chromosomal length from NCBI data, retrieving regions of interest in a vector of intensities using quantile smoothing, determining cytoband position based on the location of the probe, and other useful tools.

M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1.

This is a manifest package for Illumina's EPIC methylation arrays.

Skeletal myoblasts undergo a well-characterized sequence of morphological and transcriptional changes during differentiation. In this experiment, primary human skeletal muscle myoblasts (HSMM) were expanded under high mitogen conditions (GM) and then differentiated by switching to low-mitogen media (DM). RNA-Seq libraries were sequenced from each of several hundred cells taken over a time-course of serum-induced differentiation. Between 49 and 77 cells were captured at each of four time points (0, 24, 48, 72 hours) following serum switch using the Fluidigm C1 microfluidic system. RNA from each cell was isolated and used to construct mRNA-Seq libraries, which were then sequenced to a depth of ~4 million reads per library, resulting in a complete gene expression profile for each cell.

The MsFeature package defines functionality for Mass Spectrometry features. This includes functions to group (LC-MS) features based on some of their properties, such as retention time (coeluting features), or correlation of signals across samples. This package hence can be used to group features, and its results can be used as an input for the QFeatures package which allows aggregating abundance levels of features within each group. This package defines concepts and functions for base and common data types, implementations for more specific data types are expected to be implemented in the respective packages (such as e.g. xcms).

The package implements a method for normalising microarray intensities, and works for single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription.

This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data.

AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. AbseqR empowers the users of abseqPy with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output.

This package enables you to read and manipulate genome intervals and signals. It provides functionality similar to command-line tool suites within R, enabling interactive analysis and visualization of genome-scale data.

The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively.

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.

This package implements sampling, iteration, and input of FASTQ files. It includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats.

This package provides full genome sequences for Mus musculus (Mouse) as provided by UCSC (mm9, July 2007) and stored in Biostrings objects.

In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing.

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

This package provides tools for calculating the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data.

Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation.

This package provides imlementations of PCA, PLS, and OPLS for multivariate analysis and feature selection of omics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients).

This package provides functions to ease the transition between Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow.

This package ADAMgui is a graphical user interface (GUI) for the ADAM package. The ADAMgui package provides two shiny-based applications that allows the user to study the output of the ADAM package files through different plots. It's possible, for example, to choose a specific group of functionally associated genes (GFAG) and observe the gene expression behavior with the plots created with the GFAGtargetUi function. Features such as differential expression and fold change can be easily seen with aid of the plots made with the GFAGpathUi function.

This package provides a set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations.

The SparseArray package is an infrastructure package that provides an array-like container for efficient in-memory representation of multidimensional sparse data in R. The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data, the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they support most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN.

This package lets you carry out network-based gene set analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. It implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>.

This package r-chromvar determines variation in chromatin accessibility across sets of annotations or peaks. r-chromvar is designed primarily for single-cell or sparse chromatin accessibility data like single cell assay for transposase-accessible chromatin using sequencing (scATAC-seq or sparse bulk ATAC or deoxyribonuclease sequence (DNAse-seq) experiments.