Table of arguments for analysis and visualization

Arguments for analysis and visualization

Argument name(s)	Default value	Descriptions	Example
Required arguments
--output, -o	NULL	The path for storing output files. This path must end with a folder.	-o examples/output
--picture, -p	NULL	The path for storing output images. This path must end with a folder.	-p examples/pic
Optional arguments
--file, -f	NULL	The relative path of the input MAF file	-f examples/test_data/maf/TCGA_test.maf
--significantly_mutated_gene, -smg	False	Implement significantly mutated gene detection for input cohort	-smg
--known_cancer_gene_annotaiton, -kcga	False	Annotate known cancer gene in MAFs	-kcga
--tumor_mutation_burden, -tmb	[]	Calculate tumor mutation burden for each sample and generate a summary table	-tmb 60456963 ; the value indicates the sequencing taget region
--comut_analysis, -cm	False	Summarize mutation counts and mutation types of genes for each sample	-cm
--comut_plot, -cmp	[]	Generate CoMut plot for input cohort	-cmp examples/tsv/comut.tsv examples/tsv/comut_info.tsv 0 comut.pdf : input of CoMut plot and ouput of the figure with selected format
--mutational_signature, -ms	[]	Estimate optimal number of sinatures, extract mutational signatures for input cohort, and perform visualization	Two types of methods to implement estimation. Signature refitting -ms 0 "[SBS1, SBS5, SBS40, SBS87]" : The parameter enter a list of COSMIC signatures. NMF -ms 1 "[2,9,10]" : (step 1) Estimate optimal number of mutational signatures (2-9) with 10 epoch; -ms 2 "[3]" : (step 2) Generate plots according to extracted 3 mutational signatures
--hrd_score, -hrd	[]	Calculate HRD score for each sample, generate a summary table, and perform visualization	-hrd examples/tsv/hrd.tsv grch37 : enter the summarized CNAs of input cohort with information of genome reference that users employed
--wgd_cin, -wgdcin	NULL	Calculate CIN level for each sample, identify WGD cohort, generate a summary table, and perform visualization	-wgdcin examples/tsv/hrd.tsv : enter the summarized CNAs of input cohort
--hcw_comparison, -hcwc	[]	Comparison of HRD score, CIN level and WGD for each sample with different timing. The example is the data for a cohort of patients before and after treatment.	-hcwc examples/tsv/hcw_comparison.tsv grch37 : enter the summarized CNAs of input cohort with information of genome reference that users employed
--oncokb_annotator, -oncokb	[]	Annotate actionable mutation(drug) and perform visualization	-oncokb ../oncokb-annotator/ [your_oncokb_token] 4 examples/test_data/oncokb/clinical_input.txt : enter the path of oncokb-annotator, your personal API token of OncoKB, choose evidence levels (4 for Level 1-4, 3 for Level 1-3, etc.) for visualization, and the path of clinical data of input cohort