run | R Documentation |
Runs SigMA: (1) calculates likelihood, cosine similarity, NNLS exposures, and likelihood of the decomposition. (2) These features are later used in multivariate analysis. (3) Based on scores a final decision on existence of the signature.
run(genome_file, output_file = NULL, do_assign = T, data = "msk",
tumor_type = "breast", do_mva = T, check_msi = F, weight_cf = F,
lite_format = F, add_sig3 = F)
genome_file |
a csv file with snv spectra info can be created from vcf file using @make_genome_matrix() function see ?make_genome_matrix |
output_file |
the output file name, can be NULL in which case input file name is used and appended with "_output" |
do_assign |
boolean for whether a cutoff should be applied to determine the final decision or just the features should be returned |
data |
the options are "msk" (for a panel that is similar size to MSK-Impact panel with 410 genes), "seqcap" (for whole exome sequencing), "seqcap_probe" (64 Mb SeqCap EZ Probe v3), or "wgs" (for whole genome sequencing) |
tumor_type |
the options are "bladder", "bone_other" (Ewing's sarcoma or Chordoma), "breast", "crc", "eso", "gbm", "lung", "lymph", "medullo", "osteo", "ovary", "panc_ad", "panc_en", "prost", "stomach", "thy", or "uterus". The exact correspondance of these names can be found in https://github.com/parklab/SigMA |
do_mva |
a boolean for whether multivariate analysis should be run |
check_msi |
is a boolean which determines whether the user wants to identify micro-sattelite instable tumors |
weight_cf |
determines whether the likelihood calculation will take into account the number of tumors in each cluster when it is F the clusters get equal weights and when it's T they are weighted according to the fraction of tumors in each cluster |
lite_format |
saves the output in a lite format when set to true |
add_sig3 |
should be set to T when the likelihood of Signature 3 is calculated for tumor types for which Signature 3 was not discovered by NMF in their WGS data |
run(genome_file = "input_genomes.csv",
data = "msk",
tumor_type = "ovary")
run(genome_file = "input_genomes.csv",
data = "seqcap",
tumor_type = "bone_other")
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