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Summarise a tales object

Source: R/tales_summary.R
summary.tales.Rd

The measures worth knowing about a set of TALE arrays before doing anything with them, and the ones too expensive for print.tales.

Usage

# S3 method for class 'tales'
summary(object, ...)

# S3 method for class 'summary.tales'
print(x, ...)

Arguments

object

A tales object.

...

Unused.

x

A summary.tales object.

Value

An object of class summary.tales: a list of the measures, so they can be used as well as read.

Details

Distinct domains counts different dom_codes against the number of parts, broken down by domain type. These are domains, repeats and termini alike: a dom_code identifies any distinct part sequence, and the two termini are parts like the repeats are. In the example output shipped with the package (see tales_from_telltales), 47 distinct codes cover 39 repeats and 8 termini, so reading the total as a count of distinct repeats would overstate repeat diversity.

The ratio to parts is a property of the biology: TALEs reuse repeats heavily, within an array and between arrays. It is also what decides the cost of tales_compare_distal, whose pairwise comparison runs over distinct domains.

Repeats per array counts repeats only, excluding the termini, so it is the number that determines how long a target box each TALE recognises. Note that tell_tales()'s log reports array length as the number of domain hits, which includes the two termini and is therefore larger by up to two.

Complete arrays are those where both an N- and a C-terminus were identified. An incomplete one is not necessarily wrong (the array may sit at the end of a contig, or a terminus may simply not have been detected), but completeness is a precondition several downstream functions depend on.

Anomalies come from tales_anomalies. They are worth computing but too slow to compute every time an object is printed, which is why they appear here.

See also

Other tales objects: [.tales(), as_tales(), format.tales(), format.tales_msa(), is_tales(), new_tales(), print.tales(), print.tales_msa(), summary.tales_msa(), tales(), tales_anchor_codes(), tales_anomalies(), tales_assert_complete(), tales_bind(), tales_names(), tales_namespace(), tales_requirements(), validate_tales()

Examples

parts <- data.frame(
  array_id = c("A1", "A1", "A1", "A2", "A2", "A2"),
  position_in_array = c(1L, 2L, 3L, 1L, 2L, 3L),
  domain_type = c("N-terminus", "repeat", "C-terminus",
                  "N-terminus", "repeat", "C-terminus"),
  rvd = c("NTERM", "HD", "CTERM", "NTERM", "NI", "CTERM")
)
x <- tales(parts)
summary(x)
#> <tales> summary
#>   arrays / parts            2 / 6
#>   distinct RVDs             2
#>   repeats per array         min 1   median 1   max 1
#>   arrays with both termini  2 of 2
#>   anomalies                 none