Summarize prediction result into gene/sample-level

Summarize prediction result into gene/sample-level

gcap.runScoring(
  data,
  genome_build = "hg38",
  min_prob = 0.6,
  tightness = 1L,
  gap_cn = 3L,
  only_oncogenes = FALSE
)

Arguments

data

a data.table containing result from gcap.runPrediction.

genome_build

genome build version, should be one of 'hg38', 'hg19'.

min_prob

the minimal aggregated (in cytoband level) probability to determine a circular amplicon. The default value is for the balance of recall and precision. We highly recomment set it to 0.95 or larger if you want to detect solid positive cases (for experimental validation etc.) instead of subtyping cases.

tightness

a coefficient to times to TCGA somatic CN to set a more strict threshold as a circular amplicon. If the value is larger, it is more likely a fCNA assigned to noncircular instead of circular. When it is NA, we don't use TCGA somatic CN data as reference.

gap_cn

a gap copy number value. A gene with copy number above background (ploidy + gap_cn in general) would be treated as focal amplicon. Smaller, more amplicons.

only_oncogenes

if TRUE, only known oncogenes are kept for circular prediction.

Value

a list of data.table.

Examples

data("ec")
ec2 <- ec
ec2$prob <- gcap.runPrediction(ec)
score <- gcap.runScoring(ec2)
score