![]() Processed common SNVs for hg19 and hg38 can be found here: ![]() To process the genotype files of common SNPs, either download per-chromosome files and concatenate them using bcftools or download the whole genome file, take the first two columns of the vcf file and replace the tab with colon sign so that each line is one SNV, e.g., "1:10177". Human common SNPs from 1000 Genome project) ![]() scSplit count -v mixed_genotype.vcf -i filtered.bam -b barcodes.tsv -r ref_filtered.csv -a alt_filtered.csvī) It is strongly recommended to use below SNV list to filter the matrices to improve prediction accuracy: Common SNPs (e.g. Input parameters: -v, -vcf, VCF from mixed BAMĮ.g. csv files ("ref_filtered.csv" and "alt_filtered.csv") as output. In order to fasten the calling process, user can split the BAM by chromosome and call SNVs separately and merge the vcf files afterwards.ī) The output VCF file should be futher filtered so that only the SNVs with quality score larger than 30 would be kept.Ĭ) Typical number of filtered SNVs is roughly between 20,000 and 60,000.Ī) Run "scSplit count" and get two. This step could take very long (up to 30 hours if not using parallel processing), GATK or other SNV calling tools should work as well. freebayes -f -iXu -C 2 -q 1 filtered.bam > snv.vcf Calling for single-nucleotide variantsĪ) Use freebayes v1.2 to call SNVs from the mixed sample BAM file after being processed in the first step, set the parameters for freebayes so that no insertion and deletions (indels), nor Multi-nucleotide polymorphysim (MNP) or complex events would be captured, set minimum allele count to 2 and set minimum base quality to 1.Į.g. samtools view -S -b -q 10 -F 3844 processed.bam > filtered.bamĬ) Mark BAM file for duplication, and get it sorted and indexed, using rmdup, sort, index commands in samtoolsĢ. Run with "/scSplit " or "python /scSplit "Ī) Filter original BAM file (barcodes marked with CB:Z: tag) with white listed barcodes to minimize technical noises.ī) Filter processed BAM in a way that reads with any of following patterns be removed: read quality lower than 10, being unmapped segment, being secondary alignment, not passing filters, being PCR or optical duplicate, or being supplementary alignment.Į.g. Math, numpy, pandas pickle, pysam, PyVCF, scikit-learn, scipy, statistics Make sure below python packages can be imported: ![]() It has been tested on 3 to 8 real mixed samples using 10X pipeline, and on up to 32-mixed simulated datasets Genotype-free demultiplexing of pooled single-cell RNA-seq, using a hidden state model for identifying genetically distinct samples within a mixed population. ![]()
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