Expands integration matrix with the cumulative IS union over time.
Source:R/analysis-functions.R
cumulative_is.Rd![[Experimental]](figures/lifecycle-experimental.svg)
Given an input integration matrix that can be grouped over time,
this function adds integrations in groups assuming that
if an integration is observed at time point "t" then it is also observed in
time point "t+1".
Usage
cumulative_is(
x,
key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
timepoint_col = "TimePoint",
include_tp_zero = FALSE,
counts = TRUE,
keep_og_is = FALSE,
expand = TRUE
)Arguments
- x
An integration matrix, ideally aggregated via
aggregate_values_by_key()- key
The aggregation key used
- timepoint_col
The name of the time point column
- include_tp_zero
Should time point 0 be included?
- counts
Add cumulative counts? Logical
- keep_og_is
Keep original set of integrations as a separate column?
- expand
If
FALSE, for each group, the set of integration sites is returned in a separate column as a nested table, otherwise the resulting column is unnested.
Required tags
The function will explicitly check for the presence of these tags:
All columns declared in
mandatory_IS_vars()Checks if the matrix is annotated by assessing presence of
annotation_IS_vars()
See also
Other Analysis functions:
CIS_grubbs(),
HSC_population_size_estimate(),
compute_abundance(),
gene_frequency_fisher(),
is_sharing(),
iss_source(),
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")
)
cumulated_is <- cumulative_is(aggreg)
cumulated_is
#> $coordinates
#> # A tibble: 2,375 × 9
#> SubjectID CellMarker Tissue TimePoint chr integration_locus strand GeneName
#> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr>
#> 1 PT001 MNC BM 30 1 8464757 - RERE
#> 2 PT001 MNC BM 30 1 16186297 - SPEN
#> 3 PT001 MNC BM 30 1 40689188 + RLF
#> 4 PT001 MNC BM 30 1 157759338 - FCRL1
#> 5 PT001 MNC BM 30 1 234596545 - TARBP1
#> 6 PT001 MNC BM 30 10 122533902 - WDR11-A…
#> 7 PT001 MNC BM 30 11 5306480 + HBE1
#> 8 PT001 MNC BM 30 11 64633964 + EHD1
#> 9 PT001 MNC BM 30 11 65949729 - PACS1
#> 10 PT001 MNC BM 30 11 72097513 + CLPB
#> # ℹ 2,365 more rows
#> # ℹ 1 more variable: GeneStrand <chr>
#>
#> $counts
#> # A tibble: 20 × 5
#> SubjectID CellMarker Tissue TimePoint is_n_cumulative
#> <chr> <chr> <chr> <dbl> <int>
#> 1 PT001 MNC BM 30 54
#> 2 PT001 MNC BM 60 147
#> 3 PT001 MNC BM 90 179
#> 4 PT001 MNC BM 180 240
#> 5 PT001 MNC BM 360 240
#> 6 PT001 MNC PB 30 28
#> 7 PT001 MNC PB 60 77
#> 8 PT001 MNC PB 90 104
#> 9 PT001 MNC PB 180 121
#> 10 PT001 MNC PB 360 121
#> 11 PT002 MNC BM 30 98
#> 12 PT002 MNC BM 60 126
#> 13 PT002 MNC BM 90 141
#> 14 PT002 MNC BM 180 184
#> 15 PT002 MNC BM 360 265
#> 16 PT002 MNC PB 30 15
#> 17 PT002 MNC PB 60 26
#> 18 PT002 MNC PB 90 38
#> 19 PT002 MNC PB 180 62
#> 20 PT002 MNC PB 360 109
#>