Summarise a tales_msa object

What an alignment looks like, in numbers: how gappy it is, and how much the arrays actually agree.

Usage

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

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

Arguments

object

A tales_msa object.

...

Unused.

x

A summary.tales_msa object.

Value

An object of class summary.tales_msa.

Details

Columns with no consensus is the measure worth having. A column has no consensus when no element is strictly more common than the rest (see tales_consensus), so the count says how much of the alignment the arrays agree on.

It is reported per layer, and the two usually differ in a way that means something. Repeats that are distinct proteins can carry the same RVD, so a column can lack a dom_code consensus while having a clear rvd one: the arrays disagree about the repeat and agree about the base it binds. The reverse is rarer and more interesting.

See also

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

Examples

aligned <- data.frame(
  array_id = c("A1", "A1", "A1", "A2", "A2"),
  position_in_array = c(1L, 2L, 3L, 1L, 2L),
  alignment_position = c(1L, 2L, 3L, 1L, 3L),
  rvd = c("NTERM", "HD", "CTERM", "NTERM", "CTERM")
)
msa <- tales_msa(aligned)
summary(msa)
#> <tales_msa> summary
#>   arrays / width            2 / 3
#>   gaps                      1 of 6 cells (17%)
#>   columns with no gap       2 of 3
#>   no consensus (rvd)        1 of 3 columns
#>   matching consensus (rvd)  4 of 4 scorable cells
#>   anomalies                 none