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Input formats

Source: vignettes/articles/Input-formats.Rmd
Input-formats.Rmd
library(lineaGT)
#> ✔ Loading ctree, 'Clone trees in cancer'. Support : <https://caravagn.github.io/ctree/>
#> ✔ Loading VIBER, 'Variational inference for multivariate Binomial mixtures'. Support : <https://caravagn.github.io/VIBER/>
#> ✔ Loading lineaGT, 'Lineage inference from gene therapy'. Support : <https://caravagnalab.github.io/lineaGT/>
#> ! The 'lineagt-env' environment is already loaded!
# library(magrittr)

The fit function of the library requires two input datasets: one dataset with the coverage observation and one with the mutations variant allele number of reads and per-locus depth.

Coverage Dataframe

The first dataframe requires the following columns:

  • IS: the integration site ID,

  • timepoints: the longitunal timepoint,

  • lineage: the cell lineage name,

  • coverage the number of reads assigned to the ISs.

If lineage and timepoints columns are not present, a single longitunal observation and single lineage will be assumed.

A dataset example is the following:

data(cov.df.example)
cov.df.example
#> # A tibble: 428 × 4
#>    IS    timepoints lineage coverage
#>    <chr> <chr>      <chr>      <int>
#>  1 IS1   t1         l1           124
#>  2 IS1   t2         l1           190
#>  3 IS1   t1         l2             2
#>  4 IS1   t2         l2             6
#>  5 IS10  t1         l1             4
#>  6 IS10  t2         l1            14
#>  7 IS10  t1         l2             0
#>  8 IS10  t2         l2            12
#>  9 IS100 t1         l1             0
#> 10 IS100 t2         l1           418
#> # ℹ 418 more rows

Mutations Dataframe

The second dataframe requires the following columns:

  • IS: the integration site ID,

  • mutation: the mutation ID,

  • timepoints: the longitunal timepoint,

  • lineage: the cell lineage name,

  • alt: the per-locus variant allele number of reads,

  • dp: the per-locus total number of reads, hence the per-locus depth.

A dataframe example is the following:

data(vaf.df.example)
vaf.df.example
#> # A tibble: 116 × 6
#>    IS    mutation timepoints lineage   alt    dp
#>    <chr> <chr>    <chr>      <chr>   <dbl> <dbl>
#>  1 IS1   mut1     t1         l1         58   134
#>  2 IS1   mut1     t2         l1         53   173
#>  3 IS1   mut1     t1         l2          0    26
#>  4 IS1   mut1     t2         l2         90   322
#>  5 IS10  mut12    t1         l1          0   372
#>  6 IS10  mut12    t2         l1          0   146
#>  7 IS10  mut12    t1         l2          0    57
#>  8 IS10  mut12    t2         l2          0   482
#>  9 IS11  mut13    t1         l1        160   372
#> 10 IS11  mut13    t2         l1          0   146
#> # ℹ 106 more rows