hamming_distance_digit1: Compute the Hamming distance between two samples

In NCBI-Hackathons/Integrating-HLA-typing-methods-and-RNA-seq: HLA typing clustering and visualization based on specific similarity metrics

Description

Computes the Hamming distance for two samples using both alleles. See details section for more information about the Hamming distance.

Usage

hamming_distance_digit1(allele)

Arguments

allele

a tibble object with 3 mandatory columns: "SampleName", "AlleleName" and "AlleleGroup".

Details

The Hamming distance is given by min(s1A1 != s2A1 + s1A2 != s2A2, s1A1 != s2A2 + s1A2 != s2A1) where s1 and s2 are two samples and A1 and A2 are there alleles.

Value

a tibble object with one row containing the minimum Hamming distance and a logical column indicating whether same allele was used. If the distance is the same for both, then NA is returned.

Author(s)

Santiago Medina, Nissim Ranade

Examples

## Create a demo dataset
demoData <- data.frame(SampleName=c("s1", "s1", "s2", "s2"),
        AlleleName=c(1, 2, 1, 2), AlleleGroup=c(1, 3, 1, 5))

## Transform to tibble object
demoDataTibble <- dplyr::as_tibble(demoData)

## Calculate the Hamming distance on the demo dataset
HLAClustRView:::hamming_distance_digit1(demoDataTibble)

NCBI-Hackathons/Integrating-HLA-typing-methods-and-RNA-seq documentation built on Nov. 23, 2019, 1:57 a.m.