MHCtools
type: Item
software version identifier: 1.5.1
software version identifier: 1.0.0
software version identifier: 1.1.0
software version identifier: 1.1.1
software version identifier: 1.2.0
software version identifier: 1.2.1
software version identifier: 1.3.0
software version identifier: 1.4.0
software version identifier: 1.4.1
software version identifier: 1.4.2
software version identifier: 1.4.3
software version identifier: 1.5.0
software version identifier: 1.5.2
software version identifier: 1.5.3
CRAN project: MHCtools
instance of: R package
programmed in: R
maintained by: Jacob Roved
last update: 2023-07-08T00:00:00Z
description: Fifteen tools for bioinformatics processing and analysis of major histocompatibility complex (MHC) data. The functions are tailored for amplicon data sets that have been filtered using the dada2 method (for more information on dada2, visit <https://benjjneb.github.io/dada2/> ), but even other types of data sets can be analyzed. The ReplMatch() function matches replicates in data sets in order to evaluate genotyping success. The GetReplTable() and GetReplStats() functions perform such an evaluation. The CreateFas() function creates a fasta file with all the sequences in the data set. The CreateSamplesFas() function creates individual fasta files for each sample in the data set. The DistCalc() function calculates Grantham, Sandberg, or p-distances from pairwise comparisons of all sequences in a data set, and mean distances of all pairwise comparisons within each sample in a data set. The function additionally outputs five tables with physico-chemical z-descriptor values (based on Sandberg et al. 1998) for each amino acid position in all sequences in the data set. These tables may be useful for further downstream analyses, such as estimation of MHC supertypes. The BootKmeans() function is a wrapper for the kmeans() function of the 'stats' package, which allows for bootstrapping. Bootstrapping k-estimates may be desirable in data sets, where e.g. BIC- vs. k-values do not produce clear inflection points ("elbows"). BootKmeans() performs multiple runs of kmeans() and estimates optimal k-values based on a user-defined threshold of BIC reduction. The method is an automated and bootstrapped version of visually inspecting elbow plots of BIC- vs. k-values. The ClusterMatch() function is a tool for evaluating whether different k-means() clustering models identify similar clusters, and summarize bootstrap model stats as means for different estimated values of k. It is designed to take files produced by the BootKmeans() function as input, but other data can be analysed if the des…
author: Jacob Roved
copyright license: MIT license
copyright license: File License
imports: stats
imports: utils
imports: mgcv
imports: grDevices
imports: graphics
imports: openxlsx
MaRDI profile type: Software
depends on software: R
description: Analysis of MHC Data in Non-Model Species