In caravagnalab/lineaGT: lineaGT

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(lineaGT)
library(magrittr)

The coverage dataset can be filtered calling the filter_dataset() function.

data(cov.df.example)
data(vaf.df.example)

cov.example.filt = cov.df.example %>%
  filter_dataset(min_cov=5, min_frac=0.05)

cov.example.filt

Fitting the model

x = fit(
  cov.df = cov.example.filt,
  vaf.df = vaf.df.example,
  steps = 500,
  # n_runs = 1,
  k_interval = c(5, 15),
  timepoints_to_int = unlist(list("t1"=60, "t2"=150))
  )

Printing the fitted object information regarding the data:

• lineages and timpoints present in the data,

• number of integration sites,

• number of inferred clones of ISs, estimated via model selection on the input range of number of clusters,

• for each clone, the number of assigned ISs and the mean coverage, per timepoint and lineage.

data(x.example)
x.example

caravagnalab/lineaGT documentation built on June 13, 2025, 6:01 p.m.