This package exposes an annotation database generated from Ensembl.

This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits.

This package provides a library of core pre-processing and normalization routines.

The AffiXcan package imputes a genetically regulated expression (GReX) for a set of genes in a sample of individuals, using a method based on the total binding affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known.

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.

This package performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

This package adductomicsR processes data generated by the second stage of mass spectrometry (MS2) to identify potentially adducted peptides from spectra that has been corrected for mass drift and retention time drift and quantifies level mass spectral peaks from first stage of mass spectrometry (MS1) data.

This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome.

This package provides genome wide annotations for Zebrafish, primarily based on mapping using Entrez Gene identifiers.

The parody package provides routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics.

This package can do non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of T- and F-statistics (including T-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with T-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted P-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments.

This package loads a TxDb object, which is an R interface to prefabricated databases contained in this package. This package provides the TxDb object of Mouse data as provided by UCSC (mm10, December 2011) based on the knownGene track.

The aim of XINA is to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions. XINA imports multiple datasets, tags dataset in silico, and combines the data for subsequent subgrouping into multiple clusters. The result is a single output depicting the variation across all conditions. XINA not only extracts coabundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as Kyoto encyclopedia of genes and genomes (KEGG) to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs.

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

Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs).

This package provides dataset samples (Affymetrix: Expression, Gene, Exon, SNP; NimbleGen: Expression, Tiling) to be used with the oligo package.

This package provides a database of PolyPhen predictions for Homo sapiens dbSNP build 131.

This package provides memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences.

This package provides full genome sequences for Mus musculus (Mouse) as provided by UCSC (mm10, December 2011) and stored in Biostrings objects.

This package provides per-exon and per-gene read counts computed for selected genes from RNA-seq data that were presented in the article 'Conservation of an RNA regulatory map between Drosophila and mammals' by Brooks et al., Genome Research 2011.

The package provides functionality that can be useful for the analysis of the high-density tiling microarray data (such as from Affymetrix genechips) or for measuring the transcript abundance and the architecture. The main functionalities of the package are:

1. the class segmentation for representing partitionings of a linear series of data;

2. the function segment for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact;

3. the function confint for calculating confidence intervals using the strucchange package;

4. the function plotAlongChrom for generating pretty plots;

5. the function normalizeByReference for probe-sequence dependent response adjustment from a (set of) reference hybridizations.

Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C.

The MBECS provides a set of functions to evaluate and mitigate unwated noise due to processing in batches. To that end it incorporates a host of batch correcting algorithms (BECA) from various packages. In addition it offers a correction and reporting pipeline that provides a preliminary look at the characteristics of a data-set before and after correcting for batch effects.

This package can do differential expression analysis of RNA-seq expression profiles with biological replication. It implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. It be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, SAGE and CAGE.