sequence_table_hplt

<p>sequence_table_hplt is a randomized test data set derived from a real major
histocompatibility complex (MHC) genotyping experiment. This table differs
from the sequence_table by having the nucleotide sequences replaced with
sequence names. Sample names have been anonymized from the real data set.</p>

datasets

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
descriptions of the required data formats are observed carefully.
The PapaDiv() function compares parent pairs in the data set and calculate their
joint MHC diversity, taking into account sequence variants that occur in both
parents.
The HpltFind() function infers putative haplotypes from families in the data
set.
The GetHpltTable() and GetHpltStats() functions evaluate the accuracy of
the haplotype inference.
The CreateHpltOccTable() function creates a binary (logical) haplotype-sequence
occurrence matrix from the output of HpltFind(), for easy overview of which
sequences are present in which haplotypes.
The HpltMatch() function compares haplotypes to help identify overlapping and
potentially identical types.
The NestTablesXL() function translates the output from HpltFind() to an Excel
workbook, that provides a convenient overview for evaluation and curating of the
inferred putative haplotypes.

Jacob Roved

MHCtools

Analysis of MHC Data in Non-Model Species

sequence_table_hplt function

<p>sequence_table_hplt is a data frame with 334 samples in rows and 329
  sequence variants in columns.</p>

sequence_table_hplt: Data sequence_table_hplt

Description

Usage

Arguments

Format