This package provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).

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.

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.

ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. The ATACseqQC package was developed to help users to quickly assess whether their ATAC-seq experiment is successful. It includes diagnostic plots of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints.

This package provides utilities for flow cytometry data.

The goal of sansSouci is to perform post hoc inference: in a multiple testing context, sansSouci provides statistical guarantees on possibly user-defined and/or data-driven sets of hypotheses.

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.

This package provides full genome sequences for Danio rerio (Zebrafish) as provided by UCSC (danRer11, May 2017) and stored in Biostrings objects.

This package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. It also contains functions for identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data like gene expression/RNA sequencing/methylation/brain imaging data that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise.

This package is a collection of gene expression data from a breast cancer study published in Wang et al. 2005 and Minn et al 2007.

This package implements functions for combinatorial and differential analysis of ChIP-seq data. It includes uni- and multivariate peak-calling, export to genome browser viewable files, and functions for enrichment analyses.

This package provides the data for the gene expression enrichment analysis conducted in the package ABAEnrichment. The package includes three datasets which are derived from the Allen Brain Atlas:

1. Gene expression data from Human Brain (adults) averaged across donors,

2. Gene expression data from the Developing Human Brain pooled into five age categories and averaged across donors, and

3. a developmental effect score based on the Developing Human Brain expression data.

All datasets are restricted to protein coding genes.

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 global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms.

This package provides methods for visualizing large multivariate datasets using static and interactive scatterplot matrices, parallel coordinate plots, volcano plots, and litre plots. It includes examples for visualizing RNA-sequencing datasets and differentially expressed genes.

This package provides a function that performs gene-permuting of a gene-set enrichment analysis (GSEA) calculation with or without the absolute filtering. Without filtering, users can perform (original) two-tailed or one-tailed absolute GSEA.

This package segments single- and multi-track copy number data by a penalized least squares regression method.

Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner.

This package provides raw data objects to be used for blood cell proportion estimation in minfi and similar packages. The FlowSorted.Blood.EPIC object is based in samples assayed by Brock Christensen and colleagues; for details see Salas et al. 2018. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110554.

This package can be used to test two sets of gene lists and visualize the results.

MMUPHin is an R package for meta-analysis tasks of microbiome cohorts. It has function interfaces for:

• covariate-controlled batch- and cohort effect adjustment;

• meta-analysis differential abundance testing;

• meta-analysis unsupervised discrete structure (clustering) discovery;

• meta-analysis unsupervised continuous structure discovery.

The AffyRNADegradation package helps with the assessment and correction of RNA degradation effects in Affymetrix 3 expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation.

This package provides routines for parsing Affymetrix data files based upon file format information. The primary focus is on accessing the CEL and CDF file formats.

This package is an R program for the subset-based analysis of heterogeneous traits and disease subtypes. ASSET allows the user to search through all possible subsets of z-scores to identify the subset of traits giving the best meta-analyzed z-score. Further, it returns a p-value adjusting for the multiple-testing involved in the search. It also allows for searching for the best combination of disease subtypes associated with each variant.