Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.

Affymetrix mta10 annotation data (chip mta10probeset) assembled using data from public repositories.

This package provides a collection of pancreatic Cancer transcriptomic datasets that are part of the MetaGxData package compendium. This package contains multiple pancreas cancer datasets that have been downloaded from various resources and turned into SummarizedExperiment objects. The details of how the authors normalized the data can be found in the experiment data section of the objects. Additionally, the location the data was obtained from can be found in the url variables of the experiment data portion of each SE.

martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork.

MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework).

Calculates a single number for a whole sequence that reflects the propensity of a DNA binding protein to interact with it. The DNA binding protein has to be described with a PFM matrix, for example gotten from Jaspar.

Affymetrix Affymetrix Mu19KsubA Array annotation data (chip mu19ksuba) assembled using data from public repositories.

Test datasets from the MACS3 test examples are use in the examples of the `MACSr` package. All 9 datasets are uploaded to the `ExperimentHub`. The original data can be found at: https://github.com/macs3-project/MACS/.

Base-level (i.e. cytosine-level) counts for a collection of public bisulfite-seq datasets (e.g., WGBS and RRBS), provided as SummarizedExperiment objects with sample- and base-level metadata.

This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis.

MBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions.

Agilent annotation data (chip mgug4104a) assembled using data from public repositories.

Affymetrix Affymetrix MG_U74C Array annotation data (chip mgu74c) assembled using data from public repositories.

The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method.

The main functions for methylGSA are methylglm and methylRRA. methylGSA implements logistic regression adjusting number of probes as a covariate. methylRRA adjusts multiple p-values of each gene by Robust Rank Aggregation. For more detailed help information, please see the vignette.

Gut 16S sequencing expression data from 992 healthy and moderate-to-severe diarrhetic samples used in Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition'.

MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments.

Store minor allele frequency data from the Exome Aggregation Consortium (ExAC release 1.0 subset of nonTCGA exomes) for the human genome version hs37d5.

Store minor allele frequency data from the Exome Aggregation Consortium (ExAC release 1.0 subset of nonTCGA exomes) for the human genome version GRCh38.

This package provides a package containing an environment representing the Mu6500subC.CDF file.

Affymetrix Affymetrix MG_U74Bv2 Array annotation data (chip mgu74bv2) assembled using data from public repositories.

Gene expression data for the two breast cancer cohorts published by Glas and Buyse in 2006. This cohorts were used to implement and validate the mammaPrint breast cancer test.

This package estimates epigenetic age in skeletal muscle, using DNA methylation data generated with the Illumina Infinium technology (HM27, HM450 and HMEPIC).

Data Package automatically created on Sun Nov 19 15:59:29 2006.