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Find the source of IS by evaluating sharing.

Source: R/analysis-functions.R
iss_source.Rd

[Stable] The function computes the sharing between a reference group of interest for each time point and a selection of groups of interest. In this way it is possible to observe the percentage of shared integration sites between reference and each group and identify in which time point a certain IS was observed for the first time.

Usage

iss_source(
  reference,
  selection,
  ref_group_key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  selection_group_key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  timepoint_column = "TimePoint",
  by_subject = TRUE,
  subject_column = "SubjectID"
)

Arguments

reference

A data frame containing one or more groups of reference. Groups are identified by ref_group_key

selection

A data frame containing one or more groups of interest to compare. Groups are identified by selection_group_key

ref_group_key

Character vector of column names that identify a unique group in the reference data frame

selection_group_key

Character vector of column names that identify a unique group in the selection data frame

timepoint_column

Name of the column holding time point info?

by_subject

Should calculations be performed for each subject separately?

subject_column

Name of the column holding subjects information. Relevant only if by_subject = TRUE

Value

A list of data frames or a data frame

See also

Other Analysis functions: CIS_grubbs(), HSC_population_size_estimate(), compute_abundance(), cumulative_is(), gene_frequency_fisher(), is_sharing(), sample_statistics(), top_integrations(), top_targeted_genes()

Examples

data("integration_matrices", package = "ISAnalytics")
data("association_file", package = "ISAnalytics")
aggreg <- aggregate_values_by_key(
    x = integration_matrices,
    association_file = association_file,
    value_cols = c("seqCount", "fragmentEstimate")
)
df1 <- aggreg |>
    dplyr::filter(.data$Tissue == "BM")
df2 <- aggreg |>
    dplyr::filter(.data$Tissue == "PB")
source <- iss_source(df1, df2)
source
#> $PT001
#> # A tibble: 161 × 14
#>    g1       g1_SubjectID g1_CellMarker g1_Tissue g1_TimePoint g2    g2_SubjectID
#>    <chr>    <chr>        <chr>         <chr>            <int> <chr> <chr>       
#>  1 PT001_M… PT001        MNC           BM                 180 PT00… PT001       
#>  2 PT001_M… PT001        MNC           BM                 180 PT00… PT001       
#>  3 PT001_M… PT001        MNC           BM                 180 PT00… PT001       
#>  4 PT001_M… PT001        MNC           BM                 180 PT00… PT001       
#>  5 PT001_M… PT001        MNC           BM                 180 PT00… PT001       
#>  6 PT001_M… PT001        MNC           BM                 180 PT00… PT001       
#>  7 PT001_M… PT001        MNC           BM                 180 PT00… PT001       
#>  8 PT001_M… PT001        MNC           BM                 180 PT00… PT001       
#>  9 PT001_M… PT001        MNC           BM                 180 PT00… PT001       
#> 10 PT001_M… PT001        MNC           BM                 180 PT00… PT001       
#> # ℹ 151 more rows
#> # ℹ 7 more variables: g2_CellMarker <chr>, g2_Tissue <chr>, g2_TimePoint <int>,
#> #   chr <chr>, integration_locus <dbl>, strand <chr>, sharing_perc <dbl>
#> 
#> $PT002
#> # A tibble: 77 × 14
#>    g1       g1_SubjectID g1_CellMarker g1_Tissue g1_TimePoint g2    g2_SubjectID
#>    <chr>    <chr>        <chr>         <chr>            <int> <chr> <chr>       
#>  1 PT002_M… PT002        MNC           BM                 180 PT00… PT002       
#>  2 PT002_M… PT002        MNC           BM                 180 PT00… PT002       
#>  3 PT002_M… PT002        MNC           BM                 180 PT00… PT002       
#>  4 PT002_M… PT002        MNC           BM                 180 PT00… PT002       
#>  5 PT002_M… PT002        MNC           BM                 180 PT00… PT002       
#>  6 PT002_M… PT002        MNC           BM                 180 PT00… PT002       
#>  7 PT002_M… PT002        MNC           BM                 180 PT00… PT002       
#>  8 PT002_M… PT002        MNC           BM                 180 PT00… PT002       
#>  9 PT002_M… PT002        MNC           BM                 180 PT00… PT002       
#> 10 PT002_M… PT002        MNC           BM                 180 PT00… PT002       
#> # ℹ 67 more rows
#> # ℹ 7 more variables: g2_CellMarker <chr>, g2_Tissue <chr>, g2_TimePoint <int>,
#> #   chr <chr>, integration_locus <dbl>, strand <chr>, sharing_perc <dbl>
#> 
ggplot2::ggplot(source$PT001, ggplot2::aes(
    x = as.factor(g2_TimePoint),
    y = sharing_perc, fill = g1
)) +
    ggplot2::geom_col() +
    ggplot2::labs(
        x = "Time point", y = "Shared IS % with MNC BM",
        title = "Source of is MNC BM vs MNC PB"
    )