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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# immunogenetr <img src='man/figures/immunogenetr_sticker.png' align="right" width="120" height="139" style="height:139px; width:auto; max-width:120px;" alt="immunogenetr hex sticker" />
<!-- badges: start -->
[](https://app.codecov.io/gh/k96nb01/immunogenetr_package)
[](https://github.com/k96nb01/immunogenetr_package/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
immunogenetr is a comprehensive toolkit for clinical HLA informatics. It is built on tidyverse principles and makes use of genotype list string (GL string, https://glstring.org/) for storing and using HLA genotype data.
Specific functionalities of this library include:
- **Coercion of HLA data** in tabular format to and from GL string.
- **Calculation of matching and mismatching** in all directions, with multiple output formats.
- **Automatic formatting of HLA data** for searching within a GL string.
- **Truncation of molecular HLA data** to a specific number of fields.
- **Reading HLA genotypes in HML files** and extracting the GL string.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Citation](#citation)
- [License](#license)
- [Disclaimer](#disclaimer)
## Installation
You may install immunogenetr from CRAN with the below line of code:
```r
install.packages("immunogenetr")
```
## Usage
To demonstrate some functionality of `immunogenetr` we will use an internal dataset to perform match grades for a putative recipient/donor pair.
```r
library(immunogenetr)
library(tidyverse)
# The "HLA_typing_1" dataset is installed with immunogenetr, and contains high resolution typing at all classical
# HLA loci for ten individuals.
print(HLA_typing_1)
```
| patient|A1 |A2 |C1 |C2 |B1 |B2 |DRB345_1 |DRB345_2 |DRB1_1 |DRB1_2 |DQA1_1 |DQA1_2 |DQB1_1 |DQB1_2 |DPA1_1 |DPA1_2 |DPB1_1 |DPB1_2 |
|-------:|:-------|:-------|:-------|:-------|:-------|:-------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:-----------|
| 1|A\*24:02 |A\*29:02 |C\*07:04 |C\*16:01 |B\*44:02 |B\*44:03 |DRB5\*01:01 |DRB5\*01:01 |DRB1\*15:01 |DRB1\*15:01 |DQA1\*01:02 |DQA1\*01:02 |DQB1\*06:02 |DQB1\*06:02 |DPA1\*01:03 |DPA1\*01:03 |DPB1\*03:01 |DPB1\*04:01 |
| 2|A\*02:01 |A\*11:05 |C\*07:01 |C\*07:02 |B\*07:02 |B\*08:01 |DRB3\*01:01 |DRB4\*01:03 |DRB1\*03:01 |DRB1\*04:01 |DQA1\*03:03 |DQA1\*05:01 |DQB1\*02:01 |DQB1\*03:01 |DPA1\*01:03 |DPA1\*01:03 |DPB1\*04:01 |DPB1\*04:01 |
| 3|A\*02:01 |A\*26:18 |C\*02:02 |C\*03:04 |B\*27:05 |B\*54:01 |DRB3\*02:02 |DRB4\*01:03 |DRB1\*04:04 |DRB1\*14:54 |DQA1\*01:04 |DQA1\*03:01 |DQB1\*03:02 |DQB1\*05:02 |DPA1\*01:03 |DPA1\*02:02 |DPB1\*02:01 |DPB1\*05:01 |
| 4|A\*29:02 |A\*30:02 |C\*06:02 |C\*07:01 |B\*08:01 |B\*13:02 |DRB4\*01:03 |DRB4\*01:03 |DRB1\*04:01 |DRB1\*07:01 |DQA1\*02:01 |DQA1\*03:01 |DQB1\*02:02 |DQB1\*03:02 |DPA1\*01:03 |DPA1\*02:01 |DPB1\*01:01 |DPB1\*16:01 |
| 5|A\*02:05 |A\*24:02 |C\*07:18 |C\*12:03 |B\*35:03 |B\*58:01 |DRB3\*02:02 |DRB3\*02:02 |DRB1\*03:01 |DRB1\*14:54 |DQA1\*01:04 |DQA1\*05:01 |DQB1\*02:01 |DQB1\*05:03 |DPA1\*01:03 |DPA1\*02:01 |DPB1\*10:01 |DPB1\*124:01 |
| 6|A\*01:01 |A\*24:02 |C\*07:01 |C\*14:02 |B\*49:01 |B\*51:01 |DRB3\*03:01 |DRBX\*NNNN |DRB1\*08:01 |DRB1\*13:02 |DQA1\*01:02 |DQA1\*04:01 |DQB1\*04:02 |DQB1\*06:04 |DPA1\*01:03 |DPA1\*01:04 |DPB1\*04:01 |DPB1\*15:01 |
| 7|A\*03:01 |A\*03:01 |C\*03:03 |C\*16:01 |B\*15:01 |B\*51:01 |DRB4\*01:01 |DRBX\*NNNN |DRB1\*01:01 |DRB1\*07:01 |DQA1\*01:01 |DQA1\*02:01 |DQB1\*02:02 |DQB1\*05:01 |DPA1\*01:03 |DPA1\*01:03 |DPB1\*04:01 |DPB1\*04:01 |
| 8|A\*01:01 |A\*32:01 |C\*06:02 |C\*07:02 |B\*08:01 |B\*37:01 |DRB3\*02:02 |DRB5\*01:01 |DRB1\*03:01 |DRB1\*15:01 |DQA1\*01:02 |DQA1\*05:01 |DQB1\*02:01 |DQB1\*06:02 |DPA1\*01:03 |DPA1\*02:01 |DPB1\*04:01 |DPB1\*14:01 |
| 9|A\*03:01 |A\*30:01 |C\*07:02 |C\*12:03 |B\*07:02 |B\*38:01 |DRB3\*01:01 |DRB5\*01:01 |DRB1\*03:01 |DRB1\*15:01 |DQA1\*01:02 |DQA1\*05:01 |DQB1\*02:01 |DQB1\*06:02 |DPA1\*01:03 |DPA1\*01:03 |DPB1\*04:01 |DPB1\*04:01 |
| 10|A\*02:05 |A\*11:01 |C\*07:18 |C\*16:02 |B\*51:01 |B\*58:01 |DRB3\*03:01 |DRB5\*01:01 |DRB1\*13:02 |DRB1\*15:01 |DQA1\*01:02 |DQA1\*01:03 |DQB1\*06:01 |DQB1\*06:09 |DPA1\*01:03 |DPA1\*01:03 |DPB1\*02:01 |DPB1\*104:01 |
immunogenetr uses genotype list strings (GL strings) for most functions, including the matching and mismatching functions. To easily convert the genotypes found in "HLA_typing_1" to GL strings we can use the `HLA_columns_to_GLstring` function:
```r
HLA_typing_1_GLstring <- HLA_typing_1 %>%
mutate(GL_string = HLA_columns_to_GLstring(., HLA_typing_columns = A1:DPB1_2), .after = patient) %>%
# Note the syntax for the `HLA_columns_to_GLstring` arguments - when this function is used inside
# of a `mutate` function to make a new column in a data frame, "." is used in the first argument
# to tell the function to use the working data frame as the source of the HLA typing columns.
select(patient, GL_string)
print(HLA_typing_1_GLstring)
```
| patient|GL_string |
|-------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1| `HLA-A*24:02+HLA-A*29:02^HLA-C*07:04+HLA-C*16:01^HLA-B*44:02+HLA-B*44:03^HLA-DRB3*01:01+HLA-DRB3*01:01^HLA-DRB1*15:01+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*01:02^HLA-DQB1*06:02+HLA-DQB1*06:02^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*03:01+HLA-DPB1*04:01` |
| 2| `HLA-A*02:01+HLA-A*11:05^HLA-C*07:01+HLA-C*07:02^HLA-B*07:02+HLA-B*08:01^HLA-DRB3*01:01+HLA-DRB3*01:03^HLA-DRB1*03:01+HLA-DRB1*04:01^HLA-DQA1*03:03+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*03:01^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` |
| 3| `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` |
| 4| `HLA-A*29:02+HLA-A*30:02^HLA-C*06:02+HLA-C*07:01^HLA-B*08:01+HLA-B*13:02^HLA-DRB3*01:03+HLA-DRB3*01:03^HLA-DRB1*04:01+HLA-DRB1*07:01^HLA-DQA1*02:01+HLA-DQA1*03:01^HLA-DQB1*02:02+HLA-DQB1*03:02^HLA-DPA1*01:03+HLA-DPA1*02:01^HLA-DPB1*01:01+HLA-DPB1*16:01` |
| 5| `HLA-A*02:05+HLA-A*24:02^HLA-C*07:18+HLA-C*12:03^HLA-B*35:03+HLA-B*58:01^HLA-DRB3*02:02+HLA-DRB3*02:02^HLA-DRB1*03:01+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*05:03^HLA-DPA1*01:03+HLA-DPA1*02:01^HLA-DPB1*10:01+HLA-DPB1*124:01` |
| 6| `HLA-A*01:01+HLA-A*24:02^HLA-C*07:01+HLA-C*14:02^HLA-B*49:01+HLA-B*51:01^HLA-DRB3*03:01^HLA-DRB1*08:01+HLA-DRB1*13:02^HLA-DQA1*01:02+HLA-DQA1*04:01^HLA-DQB1*04:02+HLA-DQB1*06:04^HLA-DPA1*01:03+HLA-DPA1*01:04^HLA-DPB1*04:01+HLA-DPB1*15:01` |
| 7| `HLA-A*03:01+HLA-A*03:01^HLA-C*03:03+HLA-C*16:01^HLA-B*15:01+HLA-B*51:01^HLA-DRB3*01:01^HLA-DRB1*01:01+HLA-DRB1*07:01^HLA-DQA1*01:01+HLA-DQA1*02:01^HLA-DQB1*02:02+HLA-DQB1*05:01^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` |
| 8| `HLA-A*01:01+HLA-A*32:01^HLA-C*06:02+HLA-C*07:02^HLA-B*08:01+HLA-B*37:01^HLA-DRB3*02:02+HLA-DRB3*01:01^HLA-DRB1*03:01+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*06:02^HLA-DPA1*01:03+HLA-DPA1*02:01^HLA-DPB1*04:01+HLA-DPB1*14:01` |
| 9| `HLA-A*03:01+HLA-A*30:01^HLA-C*07:02+HLA-C*12:03^HLA-B*07:02+HLA-B*38:01^HLA-DRB3*01:01+HLA-DRB3*01:01^HLA-DRB1*03:01+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*06:02^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` |
| 10| `HLA-A*02:05+HLA-A*11:01^HLA-C*07:18+HLA-C*16:02^HLA-B*51:01+HLA-B*58:01^HLA-DRB3*03:01+HLA-DRB3*01:01^HLA-DRB1*13:02+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*01:03^HLA-DQB1*06:01+HLA-DQB1*06:09^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*02:01+HLA-DPB1*104:01` |
The "HLA_typing_1_GLstring" data frame now contains a row with a GL string for each individual, containing their full HLA genotype in a single string. Let's select one individual to act as a recipient, and one to act as a donor.
```r
# Select one case each for recipient and donor.
HLA_typing_1_GLstring_recipient <- HLA_typing_1_GLstring %>%
filter(patient == 7) %>%
rename(GL_string_recipient = GL_string, case = patient)
HLA_typing_1_GLstring_donor <- HLA_typing_1_GLstring %>%
filter(patient == 9) %>%
rename(GL_string_donor = GL_string) %>%
select(-patient)
# Combine the tables so recipient and donor are on the same row.
HLA_typing_1_recip_donor <- bind_cols(
HLA_typing_1_GLstring_recipient,
HLA_typing_1_GLstring_donor
)
print(HLA_typing_1_recip_donor)
```
| case|GL_string_recipient|GL_string_donor|
|----:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 7| `HLA-A*03:01+HLA-A*03:01^HLA-C*03:03+HLA-C*16:01^HLA-B*15:01+HLA-B*51:01^HLA-DRB3*01:01^HLA-DRB1*01:01+HLA-DRB1*07:01^HLA-DQA1*01:01+HLA-DQA1*02:01^HLA-DQB1*02:02+HLA-DQB1*05:01^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` | `HLA-A*03:01+HLA-A*30:01^HLA-C*07:02+HLA-C*12:03^HLA-B*07:02+HLA-B*38:01^HLA-DRB3*01:01+HLA-DRB3*01:01^HLA-DRB1*03:01+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*06:02^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` |
We now have a data frame with a recipient and donor HLA genotype on one row. Let's try out some of the mismatching functions on this data.
```r
HLA_typing_1_recip_donor_mismatches <- HLA_typing_1_recip_donor %>%
mutate(A_MM_GvH = HLA_mismatch_logical(
GL_string_recipient,
GL_string_donor,
"HLA-A",
direction = "GvH"),
.after = case) %>%
mutate(A_MM_HvG = HLA_mismatch_logical(
GL_string_recipient,
GL_string_donor,
"HLA-A",
direction = "HvG"),
.after = A_MM_GvH)
print(HLA_typing_1_recip_donor_mismatches)
```
| case|A_MM_GvH |A_MM_HvG |GL_string_recipient |GL_string_donor |
|----:|:--------|:--------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 7|TRUE |TRUE | `HLA-A*03:01+HLA-A*03:01^HLA-C*03:03+HLA-C*16:01^HLA-B*15:01+HLA-B*51:01^HLA-DRB3*01:01^HLA-DRB1*01:01+HLA-DRB1*07:01^HLA-DQA1*01:01+HLA-DQA1*02:01^HLA-DQB1*02:02+HLA-DQB1*05:01^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` | `HLA-A*03:01+HLA-A*30:01^HLA-C*07:02+HLA-C*12:03^HLA-B*07:02+HLA-B*38:01^HLA-DRB3*01:01+HLA-DRB3*01:01^HLA-DRB1*03:01+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*06:02^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` |
The `HLA_mismatch_logical` function determines if there are any mismatches at a particular locus. We've determined that at the HLA-A locus there are not any mismatches in the graft-versus-host direction, but are in the host-versus-graft direction. We can use the `HLA_mismatched_alleles` function to tell us what those mismatches are:
```r
HLA_typing_1_recip_donor_mismatched_allles <- HLA_typing_1_recip_donor %>%
mutate(A_HvG_MMs = HLA_mismatched_alleles(
GL_string_recipient,
GL_string_donor,
"HLA-A",
direction = "HvG"),
.after = case)
print(HLA_typing_1_recip_donor_mismatched_allles)
```
| case|A_HvG_MMs |GL_string_recipient |GL_string_donor |
|----:|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 7|HLA-A\*30:01 | `HLA-A*03:01+HLA-A*03:01^HLA-C*03:03+HLA-C*16:01^HLA-B*15:01+HLA-B*51:01^HLA-DRB3*01:01^HLA-DRB1*01:01+HLA-DRB1*07:01^HLA-DQA1*01:01+HLA-DQA1*02:01^HLA-DQB1*02:02+HLA-DQB1*05:01^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` | `HLA-A*03:01+HLA-A*30:01^HLA-C*07:02+HLA-C*12:03^HLA-B*07:02+HLA-B*38:01^HLA-DRB3*01:01+HLA-DRB3*01:01^HLA-DRB1*03:01+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*06:02^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` |
The `HLA_mismatched_alleles` function reported that the "HLA-A*30:01" allele was mismatched in the HvG direction. Sometimes, however, we simply want to know how many mismatches are at a particular locus. We can do that with the `HLA_mismatch_number` function:
```r
# Determine the number of bidirectional mismatches at several loci.
HLA_typing_1_recip_donor_MM_number <- HLA_typing_1_recip_donor %>%
mutate(ABCDRB1_MM = HLA_mismatch_number(
GL_string_recipient,
GL_string_donor,
c("HLA-A", "HLA-B", "HLA-C", "HLA-DRB1"),
direction = "bidirectional"),
.after = case)
print(HLA_typing_1_recip_donor_MM_number)
```
| case|ABCDRB1_MM |GL_string_recipient |GL_string_donor |
|----:|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 7|HLA-A=1 | `HLA-A*03:01+HLA-A*03:01^HLA-C*03:03+HLA-C*16:01^HLA-B*15:01+HLA-B*51:01^HLA-DRB3*01:01^HLA-DRB1*01:01+HLA-DRB1*07:01^HLA-DQA1*01:01+HLA-DQA1*02:01^HLA-DQB1*02:02+HLA-DQB1*05:01^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` | `HLA-A*03:01+HLA-A*30:01^HLA-C*07:02+HLA-C*12:03^HLA-B*07:02+HLA-B*38:01^HLA-DRB3*01:01+HLA-DRB3*01:01^HLA-DRB1*03:01+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*06:02^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` |
We might want to calculate an HLA match summary for stem cell transplantation. We can use the `HLA_match_summarry_HCT` function for this:
```r
# The match_grade argument of "Xof8" will return the number of matches at the HLA-A, B, C, and DRB1 loci.
HLA_typing_1_recip_donor_8of8_matching <- HLA_typing_1_recip_donor %>%
mutate(ABCDRB1_matching = HLA_match_summary_HCT(
GL_string_recipient,
GL_string_donor,
direction = "bidirectional",
match_grade = "Xof8"),
.after = case)
print(HLA_typing_1_recip_donor_8of8_matching)
```
| case| ABCDRB1_matching|GL_string_recipient |GL_string_donor |
|----:|----------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 7| 1| `HLA-A*03:01+HLA-A*03:01^HLA-C*03:03+HLA-C*16:01^HLA-B*15:01+HLA-B*51:01^HLA-DRB3*01:01^HLA-DRB1*01:01+HLA-DRB1*07:01^HLA-DQA1*01:01+HLA-DQA1*02:01^HLA-DQB1*02:02+HLA-DQB1*05:01^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` | `HLA-A*03:01+HLA-A*30:01^HLA-C*07:02+HLA-C*12:03^HLA-B*07:02+HLA-B*38:01^HLA-DRB3*01:01+HLA-DRB3*01:01^HLA-DRB1*03:01+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*06:02^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` |
Clearly, this recipient and donor are not a great match. Let's see how we could use this workflow to find the best-matched donor from several options. To do this, we'll choose a case from "HLA_typing_1" and compare it to all the cases in that data set:
```r
# Select one case to be the recipient.
HLA_typing_1_GLstring_candidate <- HLA_typing_1_GLstring %>%
filter(patient == 3) %>%
select(GL_string) %>%
rename(GL_string_recip = GL_string)
# Join the recipient to the 10-donor list and perform matching
HLA_typing_1_GLstring_donors <- HLA_typing_1_GLstring %>%
rename(GL_string_donor = GL_string, donor = patient) %>%
cross_join(HLA_typing_1_GLstring_candidate) %>%
mutate(ABCDRB1_matching = HLA_match_summary_HCT(
GL_string_recip,
GL_string_donor,
direction = "bidirectional",
match_grade = "Xof8"),
.after = donor) %>%
arrange(desc(ABCDRB1_matching))
print(HLA_typing_1_GLstring_donors)
```
| donor| ABCDRB1_matching|GL_string_donor |GL_string_recip |
|-----:|----------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3| 8| `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` | `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` |
| 2| 1| `HLA-A*02:01+HLA-A*11:05^HLA-C*07:01+HLA-C*07:02^HLA-B*07:02+HLA-B*08:01^HLA-DRB3*01:01+HLA-DRB3*01:03^HLA-DRB1*03:01+HLA-DRB1*04:01^HLA-DQA1*03:03+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*03:01^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` | `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` |
| 5| 1| `HLA-A*02:05+HLA-A*24:02^HLA-C*07:18+HLA-C*12:03^HLA-B*35:03+HLA-B*58:01^HLA-DRB3*02:02+HLA-DRB3*02:02^HLA-DRB1*03:01+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*05:03^HLA-DPA1*01:03+HLA-DPA1*02:01^HLA-DPB1*10:01+HLA-DPB1*124:01` | `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` |
| 1| 0| `HLA-A*24:02+HLA-A*29:02^HLA-C*07:04+HLA-C*16:01^HLA-B*44:02+HLA-B*44:03^HLA-DRB3*01:01+HLA-DRB3*01:01^HLA-DRB1*15:01+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*01:02^HLA-DQB1*06:02+HLA-DQB1*06:02^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*03:01+HLA-DPB1*04:01` | `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` |
| 4| 0| `HLA-A*29:02+HLA-A*30:02^HLA-C*06:02+HLA-C*07:01^HLA-B*08:01+HLA-B*13:02^HLA-DRB3*01:03+HLA-DRB3*01:03^HLA-DRB1*04:01+HLA-DRB1*07:01^HLA-DQA1*02:01+HLA-DQA1*03:01^HLA-DQB1*02:02+HLA-DQB1*03:02^HLA-DPA1*01:03+HLA-DPA1*02:01^HLA-DPB1*01:01+HLA-DPB1*16:01` | `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` |
| 6| 0| `HLA-A*01:01+HLA-A*24:02^HLA-C*07:01+HLA-C*14:02^HLA-B*49:01+HLA-B*51:01^HLA-DRB3*03:01^HLA-DRB1*08:01+HLA-DRB1*13:02^HLA-DQA1*01:02+HLA-DQA1*04:01^HLA-DQB1*04:02+HLA-DQB1*06:04^HLA-DPA1*01:03+HLA-DPA1*01:04^HLA-DPB1*04:01+HLA-DPB1*15:01` | `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` |
| 7| 0| `HLA-A*03:01+HLA-A*03:01^HLA-C*03:03+HLA-C*16:01^HLA-B*15:01+HLA-B*51:01^HLA-DRB3*01:01^HLA-DRB1*01:01+HLA-DRB1*07:01^HLA-DQA1*01:01+HLA-DQA1*02:01^HLA-DQB1*02:02+HLA-DQB1*05:01^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` | `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` |
| 8| 0| `HLA-A*01:01+HLA-A*32:01^HLA-C*06:02+HLA-C*07:02^HLA-B*08:01+HLA-B*37:01^HLA-DRB3*02:02+HLA-DRB3*01:01^HLA-DRB1*03:01+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*06:02^HLA-DPA1*01:03+HLA-DPA1*02:01^HLA-DPB1*04:01+HLA-DPB1*14:01` | `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` |
| 9| 0| `HLA-A*03:01+HLA-A*30:01^HLA-C*07:02+HLA-C*12:03^HLA-B*07:02+HLA-B*38:01^HLA-DRB3*01:01+HLA-DRB3*01:01^HLA-DRB1*03:01+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*05:01^HLA-DQB1*02:01+HLA-DQB1*06:02^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*04:01+HLA-DPB1*04:01` | `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` |
| 10| 0| `HLA-A*02:05+HLA-A*11:01^HLA-C*07:18+HLA-C*16:02^HLA-B*51:01+HLA-B*58:01^HLA-DRB3*03:01+HLA-DRB3*01:01^HLA-DRB1*13:02+HLA-DRB1*15:01^HLA-DQA1*01:02+HLA-DQA1*01:03^HLA-DQB1*06:01+HLA-DQB1*06:09^HLA-DPA1*01:03+HLA-DPA1*01:03^HLA-DPB1*02:01+HLA-DPB1*104:01` | `HLA-A*02:01+HLA-A*26:18^HLA-C*02:02+HLA-C*03:04^HLA-B*27:05+HLA-B*54:01^HLA-DRB3*02:02+HLA-DRB3*01:03^HLA-DRB1*04:04+HLA-DRB1*14:54^HLA-DQA1*01:04+HLA-DQA1*03:01^HLA-DQB1*03:02+HLA-DQB1*05:02^HLA-DPA1*01:03+HLA-DPA1*02:02^HLA-DPB1*02:01+HLA-DPB1*05:01` |
We can see that donor 3 is the only donor with an 8/8 match for the recipient.
## Citation
If you use immunogenetr in your research, please cite:
Coskun B, Brown NK. Immunogenetr: A comprehensive toolkit for clinical HLA informatics. *Human Immunology*. 2026;87(1):111619. doi:[10.1016/j.humimm.2025.111619](https://doi.org/10.1016/j.humimm.2025.111619)
You can also get the citation from R with `citation("immunogenetr")`.
## License
This project is licensed under the GNU General Public License v3.0.
## Disclaimer
This library is intended for research use. Any application making use of this package in a clinical setting will need to be independently validated according to local regulations.
</div>
</div>