Description Usage Arguments Value References Examples
Evaluate whether a tumor follows neutral evolution or under strong selection during the growth based on variant frequency distribution (VAF) of subclonal mutations. The subclonal mutant allele frequencies of a follow a simple power-law distribution predicted by neutral growth.
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maf |
Maf or MafList object generated by |
patient.id |
Select the specific patients. Default NULL, all patients are included. |
withinTumor |
Test neutral within tumros in each patients. Default FALSE. |
min.total.depth |
The minimun total depth of coverage. Defalut 2 |
min.vaf |
The minimum value of adjusted VAF value. Default 0.1 |
max.vaf |
The maximum value of adjusted VAF value. Default 0.3 |
R2.threshold |
The threshod of R2 to decide whether a tumor follows neutral evolution. Default 0.98 |
min.mut.count |
The minimun number of subclonal mutations used to fit model. Default 20 |
plot |
Logical, whether to print model fitting plot of each sample. Default TRUE. |
use.tumorSampleLabel |
Let Tumor_Sample_Barcode replace Tumor_Sample_Label if Tumor Label is provided in clinical data. Default FALSE. |
... |
Other options passed to |
the neutrality metrics and model fitting plots
Williams, M., Werner, B. et al. Identification of neutral tumor evolution across cancer types. Nat Genet 48, 238-244 (2016)
1 2 3 4 5 | maf.File <- system.file("extdata/", "CRC_HZ.maf", package = "MesKit")
clin.File <- system.file("extdata/", "CRC_HZ.clin.txt", package = "MesKit")
ccf.File <- system.file("extdata/", "CRC_HZ.ccf.tsv", package = "MesKit")
maf <- readMaf(mafFile=maf.File, clinicalFile = clin.File, ccfFile=ccf.File, refBuild="hg19")
testNeutral(maf)
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