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README.md
In immunogenetr: A Comprehensive Toolkit for Clinical HLA Informatics
immunogenetr 
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
You may install immunogenetr from CRAN with the below line of code:
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.
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:
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.
# 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.
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:
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:
# 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:
# 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:
# 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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immunogenetr documentation built on Aug. 21, 2025, 5:52 p.m.
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immunogenetr
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
You may install immunogenetr from CRAN with the below line of code:
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.
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:
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.
# 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.
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:
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:
# 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:
# 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:
# 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.
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