This package provides tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA).

The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences.

To give the exactly p-value and q-value of MeDIP-seq and MRE-seq data for different samples comparation.

R objects describing the MEEBO set.

Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test.

This package provides a collection of Breast Cancer Transcriptomic Datasets that are part of the MetaGxData package compendium.

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset.

MiDAS is a R package for immunogenetics data transformation and statistical analysis. MiDAS accepts input data in the form of HLA alleles and KIR types, and can transform it into biologically meaningful variables, enabling HLA amino acid fine mapping, analyses of HLA evolutionary divergence, KIR gene presence, as well as validated HLA-KIR interactions. Further, it allows comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS closes a gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to T cell, Natural Killer cell, and disease biology.

This package provides a package containing an environment representing the MG_U74C.cdf file.

Codelink UniSet Mouse I Bioarray (~10 000 mouse gene targets) annotation data (chip m10kcod) assembled using data from public repositories.

Store minor allele frequency data from NHLBI TOPMed for the human genome version hg19.

This package was created by frmaTools version 1.13.0.

This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data.

The MsQuality provides functionality to calculate quality metrics for mass spectrometry-derived, spectral data at the per-sample level. MsQuality relies on the mzQC framework of quality metrics defined by the Human Proteom Organization-Proteomics Standards Initiative (HUPO-PSI). These metrics quantify the quality of spectral raw files using a controlled vocabulary. The package is especially addressed towards users that acquire mass spectrometry data on a large scale (e.g. data sets from clinical settings consisting of several thousands of samples). The MsQuality package allows to calculate low-level quality metrics that require minimum information on mass spectrometry data: retention time, m/z values, and associated intensities. MsQuality relies on the Spectra package, or alternatively the MsExperiment package, and its infrastructure to store spectral data. Additionally, MsQuality supports Chromatograms objects from the Chromatograms package for chromatographic quality metrics.

Predicts the sex of samples in gene expression microarray datasets.

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.

This package provides a collection of Ovarian Cancer Transcriptomic Datasets that are part of the MetaGxData package compendium.

The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db.

Standard methods for analysis of mutation data following error- corrected sequencing (ECS) for the purpose of mutagencity assessment. Functions include importing the mutation lists provided by a variant caller, and a set of analytical tools for statistical testing and visualization of mutation data; comparison to COSMIC and/or germline signatures; etc.

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/.

MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences.

This package was created by frmaTools version 1.19.3 and hgu133ahsentrezgcdf version 19.0.0.

mitology allows to study the mitochondrial activity throught high-throughput RNA-seq data. It is based on a collection of genes whose proteins localize in to the mitochondria. From these, mitology provides a reorganization of the pathways related to mitochondria activity from Reactome and Gene Ontology. Further a ready-to-use implementation of MitoCarta3.0 pathways is included.

This package was automatically created by package AnnotationForge version 1.7.17. The exon-level probeset genome location was retrieved from Netaffx using AffyCompatible.