is_sharing {ISAnalytics} | R Documentation |
Computes the amount of integration sites shared between the groups identified
in the input data.
is_sharing( ..., group_key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"), group_keys = NULL, n_comp = 2, is_count = TRUE, relative_is_sharing = TRUE, minimal = TRUE, include_self_comp = FALSE, keep_genomic_coord = FALSE, table_for_venn = FALSE )
... |
One or more integration matrices |
group_key |
Character vector of column names which identify a single group. An associated group id will be derived by concatenating the values of these fields, separated by "_" |
group_keys |
A list of keys for asymmetric grouping.
If not NULL the argument |
n_comp |
Number of comparisons to compute. This argument is relevant only if provided a single data frame and a single key. |
is_count |
Logical, if |
relative_is_sharing |
Logical, if |
minimal |
Compute only combinations instead of all possible
permutations? If |
include_self_comp |
Include comparisons with the same group? |
keep_genomic_coord |
If |
table_for_venn |
Add column with truth tables for venn plots? |
An integration site is always identified by the triple
(chr, integration_locus, strand)
, thus these columns must be present
in the input(s).
The function accepts multiple inputs for different scenarios, please refer
to the vignette
vignette("sharing_analyses", package = "ISAnalytics")
for a more in-depth explanation.
The function outputs a single data frame containing all requested comparisons and optionally individual group counts, genomic coordinates of the shared integration sites and truth tables for plotting venn diagrams.
The sharing data obtained can be easily plotted in a heatmap via the
function sharing_heatmap
or via the function
sharing_venn
A data frame
Other Analysis functions:
CIS_grubbs()
,
comparison_matrix()
,
compute_abundance()
,
cumulative_count_union()
,
cumulative_is()
,
purity_filter()
,
sample_statistics()
,
separate_quant_matrices()
,
threshold_filter()
,
top_integrations()
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") ) sharing <- is_sharing(aggreg) sharing