SComaticExample.md

Example of how to run SComatic

This document shows an example of how to run SComatic in a scRNA-seq sample (10x). The provided bam file expands a small fraction of human chromosome 10 (Hg38) and harbours 5 somatic mutations (5 SNVs). The scripts used here are all available at the SComatic/scripts folder, and the example files are located in SComatic/example_data.

Prepare files and environment

Activate conda environment if needed

conda activate SComatic

Create an output folder and go to the main SComatic folder

SCOMATIC=$PWD
output_dir=$SCOMATIC/example_data/results
mkdir -p $output_dir

If reference genome is not unpacked and indexed (.fai), you have to do it before running SComatic

gunzip $SCOMATIC/example_data/chr10.fa.gz
samtools faidx $SCOMATIC/example_data/chr10.fa

Step 1: Splitting alignment file in cell type specific bams

sample=Example
output_dir1=$output_dir/Step1_BamCellTypes
mkdir -p $output_dir1

python $SCOMATIC/scripts/SplitBam/SplitBamCellTypes.py --bam $SCOMATIC/example_data/Example.scrnaseq.bam \
        --meta $SCOMATIC/example_data/Example.cell_barcode_annotations.tsv \
        --id ${sample} \
        --n_trim 5 \
        --max_nM 5 \
        --max_NH 1 \
        --outdir $output_dir1

Step 2: Collecting base count information

REF=$SCOMATIC/example_data/chr10.fa

output_dir2=$output_dir/Step2_BaseCellCounts
mkdir -p $output_dir2

for bam in $(ls -d $output_dir1/*bam);do
  
  # Cell type
  cell_type=$(basename $bam | awk -F'.' '{print $(NF-1)}')

  # Temp folder
  temp=$output_dir2/temp_${cell_type}
  mkdir -p $temp

  # Command line to submit to cluster
  python $SCOMATIC/scripts/BaseCellCounter/BaseCellCounter.py --bam $bam \
    --ref $REF \
    --chrom all \
    --out_folder $output_dir2 \
    --min_bq 30 \
    --tmp_dir $temp \
    --nprocs 1

  rm -rf $temp
done

In our experience, when running SComatic in an HPC cluster it is most efficient to compute the base count information for all cell types at once, specially when submitting each python line independently and at the same time to the cluster, and using multiple processors (p.e. --nprocs 16)

Step 3: Merging base count matrices

sample=Example
output_dir3=$output_dir/Step3_BaseCellCountsMerged
mkdir -p $output_dir3

python $SCOMATIC/scripts/MergeCounts/MergeBaseCellCounts.py --tsv_folder ${output_dir2} \
  --outfile ${output_dir3}/${sample}.BaseCellCounts.AllCellTypes.tsv

Step 4: Detection of somatic mutations

Step 4.1 & Step 4.2

# Step 4.1
output_dir4=$output_dir/Step4_VariantCalling
mkdir -p $output_dir4

sample=Example
REF=$SCOMATIC/example_data/chr10.fa

python $SCOMATIC/scripts/BaseCellCalling/BaseCellCalling.step1.py \
          --infile ${output_dir3}/${sample}.BaseCellCounts.AllCellTypes.tsv \
          --outfile ${output_dir4}/${sample} \
          --ref $REF

# Step 4.2
editing=$SCOMATIC/RNAediting/AllEditingSites.hg38.txt
PON=$SCOMATIC/PoNs/PoN.scRNAseq.hg38.tsv

python $SCOMATIC/scripts/BaseCellCalling/BaseCellCalling.step2.py \
          --infile ${output_dir4}/${sample}.calling.step1.tsv \
          --outfile ${output_dir4}/${sample} \
          --editing $editing \
          --pon $PON

As done in the datasets analysed in our study, we suggest intersecting the variants with the high quality regions of the human genome (provided in SComatic/bed_files_of_interest/UCSC.k100_umap.without.repeatmasker.bed). Hence, if you want to do this intersection and keep the somatic mutations that passed all filters, you can do it with a simple command:

bedtools intersect -header -a ${output_dir4}/${sample}.calling.step2.tsv -b $SCOMATIC/bed_files_of_interest/UCSC.k100_umap.without.repeatmasker.bed | awk '$1 ~ /^#/ || $6 == "PASS"' > ${output_dir4}/${sample}.calling.step2.pass.tsv

Other SComatic functionalities

Computing the number of callable sites per cell type

sample=Example
output_dir5=$output_dir/CellTypeCallableSites
mkdir -p $output_dir5

python $SCOMATIC/scripts/GetCallableSites/GetAllCallableSites.py --infile $output_dir4/${sample}.calling.step1.tsv  \
   --outfile $output_dir5/${sample} \
   --max_cov 150 --min_cell_types 2

Computing the number of callable sites per cell

sample=Example
REF=$SCOMATIC/example_data/chr10.fa
STEP4_1=$output_dir4/${sample}.calling.step1.tsv

output_dir6=$output_dir/UniqueCellCallableSites
mkdir -p $output_dir6

for bam in $(ls -d $output_dir1/*bam);do  
    cell_type=$(basename $bam | awk -F'.' '{print $(NF-1)}')
    echo $cell_type
    
    temp=$output_dir6/temp_${cell_type}
    mkdir -p $temp

    python $SCOMATIC/scripts/SitesPerCell/SitesPerCell.py --bam $bam    \
       --infile $output_dir4/${sample}.calling.step1.tsv   \
       --ref $REF \
       --out_folder $output_dir6 --tmp_dir $temp --nprocs 1
    echo
done

Computing the genotype for each cell at the variant sites

META=$SCOMATIC/example_data/Example.cell_barcode_annotations.tsv
sample=Example
REF=$SCOMATIC/example_data/chr10.fa
STEP4_2_pass=${output_dir4}/${sample}.calling.step2.pass.tsv

output_dir7=$output_dir/SingleCellAlleles
mkdir -p $output_dir7

for bam in $(ls -d $output_dir1/*bam);do  
    cell_type=$(basename $bam | awk -F'.' '{print $(NF-1)}')
    
    temp=$output_dir7/temp_${cell_type}
    mkdir -p $temp

    python $SCOMATIC/scripts/SingleCellGenotype/SingleCellGenotype.py --bam $bam  \
        --infile ${STEP4_2_pass}   \
        --nprocs 1   \
        --meta $META   \
        --outfile ${output_dir7}/${cell_type}.single_cell_genotype.tsv  \
        --tmp_dir $temp  \
        --ref $REF

    rm -rf $temp
done

Computing the trinucleotide context background

sample=Example
output_dir8=$output_dir/TrinucleotideContext
output_dir4=$output_dir/Step4_VariantCalling # Already defined in previous steps
mkdir -p $output_dir8

echo ${output_dir4}/${sample}.calling.step1.tsv > ${output_dir8}/step1_files.txt

python $SCOMATIC/scripts/TrinucleotideBackground/TrinucleotideContextBackground.py \
        --in_tsv ${output_dir8}/step1_files.txt \
        --out_file ${output_dir8}/TrinucleotideBackground.txt