Demultiplexing requires two count matrices (variant-by-cell) of reads or UMIs for each variant in each cell: AD for alternative allele and DP depth (i.e., summary of alternative and reference alleles). These two matrices can be obtained by genotyping a list of variants in each cell. We provide a guideline for cellular genotyping with a recommendation of cellSNP-lite that is also developed by us.

Once the genotypes for each cell have been obtained, e.g., in VCF format, or two sparse matrices AD and DP, we can apply Vireo for demultiplexing.

Demultiplexing for donors

By default, Vireo works without any known genotype information for pooled samples. However, if any of genotype of these samples are known or can be obtained, e.g., by bulk RNA-seq, exome-seq, it is still useful to add them, not only allowing us to align the deconvoluted samples to its identity, but also can benefits the doublets identification, especially if the coverage or the loaded cells per sample is low.

Depending the availability of genotype information, we provide four strategies to demultiplex scRNA-seq data.

  1. without any genotype:

    vireo -c $CELL_DATA -N $n_donor -o $OUT_DIR
  2. with genotype for all samples (genoTag: GT, GP, or PL; default is PL, please choose the existing one)

    vireo -c $CELL_DATA -d $DONOR_GT_FILE -o $OUT_DIR

    Optionally, -N can be provided if it is samller than that in DONOR_GT_FILE for finding the relevant subset of donors.


    For efficient loading of donor VCF file, we recommend subset it bcftools view donor.vcf.gz -R cellSNP.cells.vcf.gz -Oz -o sub.vcf.gz

    You can also add -s or -S for subsetting samples.

    Make sure you only keep informative SNPs, e.g., by filtering out SNPs with too much missing values or the gentoypes too similar across donors.

  3. with genotype for part of the samples (n_donor is larger than that in DONOR_GT_FILE)

    vireo -c $CELL_DATA -d $DONOR_GT_FILE -o $OUT_DIR -N $n_donor
  4. with genotype but not confident (or only for subset of SNPs)

    vireo -c $CELL_DATA -d $DONOR_GT_FILE -o $OUT_DIR --forceLearnGT

Formats of cell data

Viroe supports the cell data in three formats:

  1. a cellSNP output folder containing VCF for variants info and sparse matrices AD and DP
  2. Vartrix outputs with three or four files: alt.mtx,ref.mtx,barcodes.tsv[,SNP.vcf.gz]
  3. standard VCF file with variants by cells

Vireo full arguments

Type vireo -h for details of all arguments:

Usage: vireo [options]

-h, --help            show this help message and exit
-c CELL_DATA, --cellData=CELL_DATA
                        The cell genotype file in VCF format or cellSNP folder
                        with sparse matrices.
-N N_DONOR, --nDonor=N_DONOR
                        Number of donors to demultiplex; can be larger than
                        provided in donor_file
-o OUT_DIR, --outDir=OUT_DIR
                        Dirtectory for output files [default:

Optional input files:
                        The cell genotype files in vartrix outputs (three/four
                        files, comma separated):
                        alt.mtx,ref.mtx,barcodes.tsv,SNPs.vcf.gz. This will
                        suppress cellData argument.
   -d DONOR_FILE, --donorFile=DONOR_FILE
                        The donor genotype file in VCF format. Please filter
                        the sample and region with bcftools -s and -R first!
   -t GENO_TAG, --genoTag=GENO_TAG
                        The tag for donor genotype: GT, GP, PL [default: PL]

Optional arguments:
   --noDoublet         If use, not checking doublets.
   -M N_INIT, --nInit=N_INIT
                        Number of random initializations, when GT needs to
                        learn [default: 50]
                        Number of extra donor in pre-cluster, when GT needs to
                        learn [default: 0]
                        Method for searching from extra donors. size: n_cell
                        per donor; distance: GT distance between donors
                        [default: distance]
   --forceLearnGT      If use, treat donor GT as prior only.
   --ASEmode           If use, turn on SNP specific allelic ratio.
   --noPlot            If use, turn off plotting GT distance.
                        Seed for random initialization [default: none]
                        Range of cells to process, eg. 0-10000 [default: all]
   --callAmbientRNAs   If use, detect ambient RNAs in each cell (under
   -p NPROC, --nproc=NPROC
                        Number of subprocesses for computing - this sacrifices
                        memory for speedups [default: 1]

Discriminatory variants

Given a set of variants for which estimated genotypes are available, the Vireo software implements a heuristic to define a minimal and informative set of discriminatory variants. This set of variants can be used to perform qPCR-based genotyping or for other targeted genoytping methods. Briefly, this algorithm prioritises variants with largest information gain in splitting samples.

For any donor genotype file in VCF format, especially the output from Vireo, GT_donors.vireo.vcf.gz, the GTbarcode function can be used to generate the minimal set of discriminatory variants by the following command line:

GTbarcode -i $dir/GT_donors.vireo.vcf.gz -o $dir/GT_barcodes.tsv --randSeed 1

By default, this function filters out variants with <20 UMIs or >0.05 reads aligned other alleles except the annotated reference and alternative alleles. In case the variants with homozygous alternative alleles are not wanted, the arguments --noHomoAlt can be used. By default, this GTbarcode function will also generate a figure for the identified genotype barcode, as following (based on example data in the repo),

identified discriminatory variants

Example data

In order to test vireo and illustrate the usage, we provide a test data set, also some demo scripts.

This example data set contains 952 cells from 4 samples. The genotypes for these four samples are also provided.