HLA typing 10x scRNA-seq data with Optitype

[Githubuser3838]

This is not exactly an issue, I wanted to share the procedure I used to successfully type a few samples sequenced with 10x Genomics, using Optitype. The results from Optitype were verified independently by conventional HLA typing.

The strategy is to filter the bam file from Cell Ranger for chromosome 6, and then, optionally, sub-sample the resulting file (if there are too many reads at the typing step and it crashes). We then make fastq files using 10x bamtofastq, and type with Optitype.
Sub-sampling may be necessary for single-cell 5’ assays, because the assay selects many reads from the first exons. For 3' assays, sub-sampling should not be used, because the assay doesn't select many reads from the first exons (comparatively), and Optitype relies mostly on exons 2 and 3 to differentiate HLA alleles.
The parameter -s for subsampling can be adjusted, but I got fast and consistent results with 0.001 (after sequencing 5000-10000 cells with 10x v2 chemistry, 5' assay). This depends on the dataset, it should be adjusted so that enough reads are kept after alignment to the reference, but not so many that it crashes at the typing step (typing worked well with about 1000-5000 reads after alignment). Subsampling can be repeated with a different seed to make sure typing is consistent.

I used a conda installation. I also used bwa mem for mapping onto the reference, as described by @armish at hammerlab/biokepi#419, since it solves memory issues that arise with razers3.

Here is the procedure:

Install Optitype and index the reference:

conda create -n optitype-conda
conda activate optitype-conda
conda install -c bioconda optitype
conda install -c bioconda bwa
conda install -c bioconda 10x_bamtofastq

REFRNA=~/miniconda3/envs/optitype-conda/bin/data/hla_reference_rna.fasta
OPTITYPE_HOME=~/miniconda3/envs/optitype-conda/bin

bwa index $REFRNA

Make chr6-filtered bam:

samtools view -t8 -bh sample_alignments.bam chr6 -o chr6.filtered.bam
or (depending on your bam file):
samtools view -t8 -bh sample_alignments.bam 6 -o chr6.filtered.bam

Sub-sample (optional):

samtools view -b -s 0.001 chr6.filtered.bam -o chr6.filtered.sub.bam

(To try a different sub-sampled set of reads, change the seed in the integer part of the -s parameter, e.g. 123.001)

Make fastq from R2 reads:

bamtofastq chr6.filtered.sub.bam ./bamtofastq
cd bamtofastq/*/
cat *_R2_001.fastq.gz > Sample_R2_001.fastq.gz
(R1 reads correspond to barcodes/UMI)

Run alignement and Optitype on generated fastq:

bwa mem $REFRNA Sample_R2_001.fastq.gz | samtools fastq -F4 > fished.Sample_R2_001.fastq
(should yield 1000-5000 reads for the typing step, although accurate results can be obtained with much less)

python $OPTITYPE_HOME/OptiTypePipeline.py -i fished.Sample_R2_001.fastq --rna -v -o ./