Shrink Pedigree object to specified bit size with priority placed on trimming uninformative subjects. The algorithm is useful for getting a Pedigree condensed to a minimally informative size for algorithms or testing that are limited by size of the Pedigree.
If avail or affected are NULL, they are extracted
with their corresponding accessors from the Ped object.
Usage
# S4 method for class 'Pedigree'
shrink(obj, avail = NULL, affected = NULL, max_bits = 16)
# S4 method for class 'Ped'
shrink(obj, avail = NULL, affected = NULL, max_bits = 16)Arguments
- obj
A Pedigree or Ped object.
- avail
A logical vector with the availability status of the individuals (i.e.
FALSE= not available,TRUE= available,NA= unknown).- affected
A logical vector with the affection status of the individuals (i.e.
FALSE= unaffected,TRUE= affected,NA= unknown).- max_bits
Optional, the bit size for which to shrink the Pedigree
Value
A list containing the following elements:
pedObj: Pedigree object after trimming
id_trim: Vector of ids trimmed from Pedigree
id_lst: List of ids trimmed by category
bit_size: Vector of bit sizes after each trimming step
avail: Vector of availability status after trimming
pedSizeOriginal: Number of subjects in original Pedigree
pedSizeIntermed: Number of subjects after initial trimming
pedSizeFinal: Number of subjects after final trimming
Details
Iteratively remove subjects from the Pedigree. The random removal of members was previously controlled by a seed argument, but we remove this, forcing users to control randomness outside the function. First remove uninformative subjects, i.e., unavailable (not genotyped) with no available descendants. Next, available terminal subjects with unknown phenotype if both parents available. Last, iteratively shrinks Pedigrees by preferentially removing individuals (chosen at random if there are multiple of the same status):
Subjects with unknown affected status
Subjects with unaffected affected status
Affected subjects.
Examples
data(sampleped)
ped1 <- Pedigree(sampleped[sampleped$famid == '1',])
shrink(ped1, max_bits = 12)
#> $pedObj
#> Pedigree object with:
#> Ped object with 13 individuals and 5 metadata columns:
#> id dadid momid famid sex fertility
#> col_class <character> <character> <character> <character> <ordered> <factor>
#> 1_103 1_103 <NA> <NA> 1 male fertile
#> 1_104 1_104 <NA> <NA> 1 female fertile
#> 1_105 1_105 <NA> <NA> 1 male fertile
#> 1_106 1_106 <NA> <NA> 1 female fertile
#> 1_109 1_109 <NA> <NA> 1 female fertile
#> ... ... ... ... ... ... ...
#> 1_116 1_116 1_105 1_106 1 female fertile
#> 1_118 1_118 1_105 1_106 1 female fertile
#> 1_119 1_119 1_105 1_106 1 male fertile
#> 1_124 1_124 1_110 1_109 1 male fertile
#> 1_127 1_127 1_114 1_115 1 male fertile
#> miscarriage deceased avail evaluated consultand proband
#> col_class <factor> <logical> <logical> <logical> <logical> <logical>
#> 1_103 FALSE <NA> FALSE FALSE FALSE FALSE
#> 1_104 FALSE <NA> FALSE FALSE FALSE FALSE
#> 1_105 FALSE <NA> FALSE FALSE FALSE FALSE
#> 1_106 FALSE <NA> FALSE FALSE FALSE FALSE
#> 1_109 FALSE <NA> TRUE FALSE TRUE FALSE
#> ... ... ... ... ... ... ...
#> 1_116 FALSE <NA> TRUE FALSE FALSE FALSE
#> 1_118 FALSE <NA> TRUE FALSE FALSE FALSE
#> 1_119 FALSE <NA> TRUE FALSE FALSE FALSE
#> 1_124 FALSE <NA> TRUE FALSE FALSE FALSE
#> 1_127 FALSE <NA> TRUE FALSE FALSE FALSE
#> affected carrier asymptomatic adopted dateofbirth dateofdeath
#> col_class <logical> <logical> <logical> <logical> <character> <character>
#> 1_103 TRUE <NA> <NA> FALSE 1975-08-14 <NA>
#> 1_104 FALSE <NA> <NA> FALSE <NA> <NA>
#> 1_105 <NA> <NA> <NA> FALSE <NA> <NA>
#> 1_106 <NA> <NA> <NA> FALSE <NA> <NA>
#> 1_109 FALSE <NA> TRUE FALSE <NA> <NA>
#> ... ... ... ... ... ... ...
#> 1_116 TRUE <NA> <NA> FALSE <NA> <NA>
#> 1_118 TRUE <NA> <NA> FALSE <NA> <NA>
#> 1_119 TRUE <NA> <NA> FALSE <NA> <NA>
#> 1_124 TRUE <NA> <NA> FALSE 2006-07-28 <NA>
#> 1_127 TRUE <NA> <NA> FALSE <NA> <NA>
#> useful kin isinf num_child_tot num_child_dir
#> col_class <logical> <numeric> <logical> <numeric> <numeric>
#> 1_103 <NA> <NA> <NA> 2 2
#> 1_104 <NA> <NA> <NA> 2 2
#> 1_105 <NA> <NA> <NA> 4 4
#> 1_106 <NA> <NA> <NA> 4 4
#> 1_109 <NA> <NA> <NA> 1 1
#> ... ... ... ... ... ...
#> 1_116 <NA> <NA> <NA> 0 0
#> 1_118 <NA> <NA> <NA> 0 0
#> 1_119 <NA> <NA> <NA> 0 0
#> 1_124 <NA> <NA> <NA> 0 0
#> 1_127 <NA> <NA> <NA> 0 0
#> num_child_ind | affection num error affection_mods
#> col_class <numeric> <character> <character> <character> <character>
#> 1_103 0 1 2 <NA> 1
#> 1_104 0 0 4 <NA> 0
#> 1_105 0 <NA> 6 <NA> NA
#> 1_106 0 <NA> 1 <NA> NA
#> 1_109 0 0 3 <NA> 0
#> ... ... ... ... ... ...
#> 1_116 0 1 1 <NA> 1
#> 1_118 0 1 2 <NA> 1
#> 1_119 0 1 6 <NA> 1
#> 1_124 0 1 <NA> <NA> 1
#> 1_127 0 1 5 <NA> 1
#> avail_mods
#> col_class <character>
#> 1_103 0
#> 1_104 0
#> 1_105 0
#> 1_106 0
#> 1_109 1
#> ... ...
#> 1_116 1
#> 1_118 1
#> 1_119 1
#> 1_124 1
#> 1_127 1
#> Rel object with 0 relationshipswith 0 MZ twin, 0 DZ twin, 0 UZ twin, 0 Spouse:
#> id1 id2 code famid
#> <character> <character> <c("ordered", "factor")> <character>
#>
#> $id_trim
#> [1] "1_101" "1_102" "1_107" "1_108" "1_111" "1_113" "1_121" "1_122" "1_123"
#> [10] "1_131" "1_132" "1_134" "1_139" "1_126" "1_125" "1_140" "1_141" "1_133"
#> [19] "1_130" "1_129" "1_128"
#>
#> $id_lst
#> $id_lst$unavail
#> [1] "1_101" "1_102" "1_107" "1_108" "1_111" "1_113" "1_121" "1_122" "1_123"
#> [10] "1_131" "1_132" "1_134" "1_139"
#>
#> $id_lst$affect
#> [1] "1_126" "1_125" "1_140" "1_141" "1_133" "1_130" "1_129" "1_128"
#>
#>
#> $bit_size
#> [1] 46 29 27 23 21 17 15 14 13 11
#>
#> $avail
#> [1] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
#> [13] TRUE
#>
#> $pedSizeOriginal
#> [1] 41
#>
#> $pedSizeIntermed
#> [1] 28
#>
#> $pedSizeFinal
#> [1] 13
#>