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immunogenetr

An R library for HLA informatics

https://github.com/k96nb01/immunogenetr_package

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Keywords

hla hla-typing informatics r tidyverse
Last synced: 6 months ago · JSON representation

Repository

An R library for HLA informatics

Basic Info
  • Host: GitHub
  • Owner: k96nb01
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 7.92 MB
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Topics
hla hla-typing informatics r tidyverse
Created over 1 year ago · Last pushed 6 months ago
Metadata Files
Readme Changelog

README.Rmd 

---
output: github_document
---



```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# immunogenetr 



[![codecov](https://codecov.io/gh/k96nb01/immunogenetr_package/graph/badge.svg?token=16D4U43VET)](https://app.codecov.io/gh/k96nb01/immunogenetr_package)
[![R-CMD-check](https://github.com/k96nb01/immunogenetr_package/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/k96nb01/immunogenetr_package/actions/workflows/R-CMD-check.yaml)


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)
- [License](#license)

## 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.


## License

This project is licensed under the GNU General Public License v3.0.

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A Comprehensive Toolkit for Clinical HLA Informatics

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DESCRIPTION cran