The dependency data can be downloaded from these links and should be placed in the <reference_path> folder.

• Vep:
• Annovar:

Please prepare the input files in advance. Follow the instructions below based on your data type.

If you are providing raw sequencing data, ensure the FASTQ files are named according to the following convention and move the files to the <data_path> directory, like : ./Demo/Raw/*.fastq.gz

[SampleName]S1_L00[LaneNumber][ReadType]_001.fastq.gz

If you are skipping upstream analysis, prepare the following processed files:

• BAM files: Ensure the BAM files are named according to the following convention and move the files to the <data_path> directory, like : ./Demo/Processed/*.bam
• h5ad files: Ensure the h5ad files are named according to the following convention and move the files to the <data_path> directory, like : ./Demo/Processed/*.h5ad
• rds files: Ensure the Seurat files are named according to the following convention and move the files to the <data_path> directory, like : ./Demo/Processed/*.rds

Only one of either the h5ad file or the rds file is needed.

Create a configuration file ./scVar/config.yaml in the <results_path> directory. This file should contain the following information:

# Configuration file for scVar analysis
# Please fill in the required information below
project:
    <ProjectName> # Project name
samples:
    <SampleName>: SamplePath # Sample name and path to the sample data
result_path:
    <results_path> # Path to save the results
SNV_filter_vaf:
    <VAF> # VAF filter for SNV
threads:
    <number> # Number of threads to use
genotype_filter:
    <reads> #Minimum number of reads required per barcode
genotype_mapq:
    <mapq> # Minimum mapping quality for genotype filtering
genotype_baseq:
    <baseq> # Minimum base quality for genotype filtering

Copy a snakefile file in the <results_path> directory.

• For Raw Data: Copy ./scVar/Snakefile_Raw and rename it to Snakefile.
• For Processed Data: Copy ./scVar/Snakefile and rename it to Snakefile.

docker run -it -v <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && cd <results_path> && snakemake --cores <cores_number>'

• <reference_path>: Path to the reference genome directory.
• <results_path>: Path to the results directory.
• <data_path>: Path to the data directory.
• <cores_number>: Number of cores to use for the analysis.

docker run -it -v <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/Analysis_SigTMBEntroy.sh <project_path> <sample_name>'

• <project_path>: Path to the project directory.
• <sample_name>: Name of the sample to analyze.

docker run -it -v <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/Calculate_Specific_Mutations.sh <project_path> <sample_name>'

• <project_path>: Path to the project directory.
• <sample_name>: Name of the sample to analyze.

docker run -it -v <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/MutationCluster.sh --path <project_path> --sample <sample_name> --method <method> --flag <flag> --number <number> --clustermethod <clustermethod>'

• <project_path>: Path to the project directory.
• <sample_name>: Name of the sample to analyze.
• --method: Method for selecting features (options: var1, var2, TF_IDF, default: var1).
• --flag: Flag for clustering (options: 0, 1, default: 0).
• --number: Number of features for clustering (integer, default: 100).
• --clustermethod: Method for clustering (options: ward.D, ward.D2, single, complete, average, mcquitty, median, centroid, default: average).

docker run -it -v <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/GOonco.sh --path <project_path> --sample <sample_name> --pCutoff <pCutoff> --qCutoff <qCutoff>'

• <project_path>: Path to the project directory.
• <sample_name>: Name of the sample to analyze.
• --pCutoff: P-value cutoff for GO analysis (default: 0.05).
• --qCutoff: Q-value cutoff for GO analysis (default: 0.2).

docker run -it -v <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/Pseudotime.sh --path <project_path> --sample <sample_name> --mutation <mutations>'

• <project_path>: Path to the project directory.
• <sample_name>: Name of the sample to analyze.
• --mutation: Mutation IDs connected by commas, e.g.,12_25245350_C_G,17_47592542_A_G.

docker run -it -v <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/Statistic.sh --path <project_path> --sample <sample_name>'

• <project_path>: Path to the project directory.
• <sample_name>: Name of the sample to analyze.

For detailed information on inputs, outputs, scripts, and parameters of each module, refer to Guided.

• Copy Input Files: Copy files from either ./Demo/Raw/ or ./Demo/Processed/ to the <data_path> directory. Add that if copying files from ./Demo/Processed/, only one of either the h5ad or rds files needs to be copied.
• Copy Configuration File: If you copy data from ./Demo/Raw/, copy ./Demo/Raw_config/config.yaml and ./Demo/Raw_config/Snakefile to the <results_path> directory. If you copy data from ./Demo/Processed/, copy ./Demo/Processed_config/config.yaml and ./Demo/Processed_config/Snakefile to the <results_path> directory.

docker run -it -v <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && cd /results && snakemake --cores 1'

A h5mu file containing transcriptome and mutation information in the <results_path> folder.

docker run -it -v <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/Analysis_SigTMBEntroy.sh /results/Example Demo'

• Cosimc Files: The mutation spectrum results of all cells and different cell types, output similar to:

• TMB File: The TMB results of all cells and different cell types, output similar to:

Total length of Endothelium qualifying regions: 672240
Mutations count of Endothelium: 16
TMB of Endothelium qualifying regions: 23.80102344400809234797
Total length of Alveolar_cell qualifying regions: 2910688
Mutations count of Alveolar_cell: 40
TMB of Alveolar_cell qualifying regions: 13.74245539198979760111
Total length of All qualifying regions: 3051450
Mutations count of All: 41
TMB of All qualifying regions: 13.43623523243048386832

• Entropy and Simpson's Index File: The results of the entropy and Simpson's index for each mutation, output similar to:

docker run -it -v <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/Calculate_Specific_Mutations.sh /results/Example Demo'

The cluster-specific and cell type-specific mutations, output similar to:

docker run -it <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/MutationCluster.sh --path /results/Example --sample Demo --method TF_IDF --flag 0 --number 80 --clustermethod average'

The results of cell clustering based on mutations, similar to:

docker run -it <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/GOonco.sh --path /results/Example --sample Demo  --pCutoff 0.05 --qCutoff 0.2'

GO and oncogenic pathway enrichment results for all cells and different cell types, output similar to:

docker run -it <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/Pseudotime.sh --path /results/Example --sample Demo --mutation 7_6004027_A_G,12_25245350_C_G,17_47592542_A_G,22_20708085_C_T,2_28942361_C_A,3_49684128_A_C,3_49684173_G_A,3_49684565_A_G,7_56116040_A_C,9_137717161_G_T,9_137717162_C_A,X_100662268_C_G'

Pseudotime analysis results with highlighted mutated cells, similar to:

docker run -it <reference_path>:/reference -v <results_path>:/results  -v <data_path>:/data scvar /bin/bash -c 'source /opt/miniconda/bin/activate scVar && bash /codes/Statistic.sh /results/Example Demo'

A report file will be generated.