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Ped object

Source: R/AllClass.R, R/AllConstructor.R, R/AllAccessors.R, and 1 more
Ped-class.Rd

S4 class to represent the identity informations of the individuals in a pedigree.

Constructor :

You either need to provide a vector of the same size for each slot or a data.frame with the corresponding columns.

The metadata will correspond to the columns that do not correspond to the Ped slots.

Usage

# S4 method for class 'data.frame'
Ped(
  obj,
  cols_used_init = FALSE,
  cols_used_del = FALSE,
  date_pattern = "%Y-%m-%d"
)

# S4 method for class 'character_OR_integer'
Ped(
  obj,
  dadid,
  momid,
  sex,
  famid = NA,
  fertility = NA,
  miscarriage = NA,
  deceased = NA,
  avail = NA,
  evaluated = NA,
  consultand = NA,
  proband = NA,
  affected = NA,
  carrier = NA,
  asymptomatic = NA,
  adopted = NA,
  dateofbirth = NA,
  dateofdeath = NA,
  missid = NA_character_,
  useful = NA,
  isinf = NA,
  kin = NA_real_
)

Arguments

obj

A character vector with the id of the individuals or a data.frame with all the informations in corresponding columns.

cols_used_init

Boolean defining if the columns that will be used should be initialised to NA.

cols_used_del

Boolean defining if the columns that will be used should be deleted.

date_pattern

The pattern of the date

dadid

A vector containing for each subject, the identifiers of the biologicals fathers.

momid

A vector containing for each subject, the identifiers of the biologicals mothers.

sex

A character, factor or numeric vector corresponding to the gender of the individuals. This will be transformed to an ordered factor with the following levels: male < female < unknown

The following values are recognized:

  • "male": "m", "male", "man", 1

  • "female": "f", "female", "woman", 2

  • "unknown": "unknown", 3

famid

A character vector with the family identifiers of the individuals. If provide, will be aggregated to the individuals identifiers separated by an underscore.

fertility

A character, factor or numeric vector corresponding to the fertility status of the individuals. This will be transformed to a factor with the following levels: infertile_choice_na, infertile, fertile

The following values are recognized:

  • "inferile_choice_na" : "infertile_choice", "infertile_na"

  • "infertile" : "infertile", "steril", FALSE, 0

  • "fertile" : "fertile", TRUE, 1, NA

miscarriage

A character, factor or numeric vector corresponding to the miscarriage status of the individuals. This will be transformed to a factor with the following levels: TOP, SAB, ECT, FALSE The following values are recognized:

  • "SAB" : "spontaneous", "spontaenous abortion"

  • "TOP" : "termination", "terminated", "termination of pregnancy"

  • "ECT" : "ectopic", "ectopic pregnancy"

  • FALSE : 0, FALSE, "no", NA

deceased

A logical vector with the death status of the individuals (i.e. FALSE = alive, TRUE = dead, NA = unknown).

avail

A logical vector with the availability status of the individuals (i.e. FALSE = not available, TRUE = available, NA = unknown).

evaluated

A logical vector with the evaluation status of the individuals. (i.e. FALSE = documented evaluation not available, TRUE = documented evaluation available).

consultand

A logical vector with the consultand status of the individuals. A consultand being an individual seeking genetic counseling/testing (i.e. FALSE = not a consultand, TRUE = consultand).

proband

A logical vector with the proband status of the individuals. A proband being an affected family member coming to medical attention independent of other family members. (i.e. FALSE = not a proband, TRUE = proband).

affected

A logical vector with the affection status of the individuals (i.e. FALSE = unaffected, TRUE = affected, NA = unknown).

carrier

A logical vector with the carrier status of the individuals. A carrier being an individual who has the genetic trait but who is not likely to manifest the disease regardless of inheritance pattern (i.e. FALSE = not carrier, TRUE = carrier, NA = unknown).

asymptomatic

A logical vector with the asymptomatic status of the individuals. An asymptomatic individual being an individual clinically unaffected at this time but could later exhibit symptoms. (i.e. FALSE = not asymptomatic, TRUE = asymptomatic, NA = unknown).

adopted

A logical vector with the adopted status of the individuals. (i.e. FALSE = not adopted, TRUE = adopted, NA = unknown).

dateofbirth

A character vector with the date of birth of the individuals.

dateofdeath

A character vector with the date of death of the individuals.

missid

A character vector with the missing values identifiers. All the id, dadid and momid corresponding to those values will be set to NA_character_.

useful

A logical vector with the usefulness status of the individuals (i.e. FALSE = not useful, TRUE = useful).

isinf

A logical vector indicating if the individual is informative or not (i.e. FALSE = not informative, TRUE = informative).

kin

A numeric vector with minimal kinship value between the individuals and the informative individuals.

Value

A Ped object.

Details

The minimal needed informations are id, dadid, momid and sex. The other slots are used to store recognized informations. Additional columns can be added to the Ped object and will be stored in the elementMetadata slot of the Ped object.

Slots

id

A character vector with the id of the individuals.

dadid

A character vector with the id of the father of the individuals.

momid

A character vector with the id of the mother of the individuals.

famid

A character vector with the family identifiers of the individuals (optional).

sex

An ordered factor vector for the sex of the individuals (i.e. male < female < unknown).

fertility

A factor vector with the fertility status of the individuals (optional). (i.e. infertile_choice_na = no children by choice or unknown reason, infertile = individual is inferile, fertile = individual is fertile).

miscarriage

A factor vector with the miscarriage status of the individuals (optional). (i.e. TOP = Termination of Pregnancy, SAB = Spontaneous Abortion, ECT = Ectopic Pregnancy, FALSE = no miscarriage).

deceased

A logical vector with the death status of the individuals (optional). (i.e. FALSE = alive, TRUE = dead, NA = unknown).

avail

A logical vector with the availability status of the individuals (optional). (i.e. FALSE = not available, TRUE = available, NA = unknown).

evaluated

A logical vector with the evaluation status of the individuals (optional). (i.e. FALSE = documented evaluation not available, TRUE = documented evaluation available).

consultand

A logical vector with the consultand status of the individuals (optional). A consultand being an individual seeking genetic counseling/testing (i.e. FALSE = not a consultand, TRUE = consultand).

proband

A logical vector with the proband status of the individuals (optional). A proband being an affected family member coming to medical attention independent of other family members. (i.e. FALSE = not a proband, TRUE = proband).

affected

A logical vector with the affection status of the individuals (optional). (i.e. FALSE = not affected, TRUE = affected, NA = unknown).

carrier

A logical vector with the carrier status of the individuals (optional). A carrier being an individual who has the genetic trait but who is not likely to manifest the disease regardless of inheritance pattern (i.e. FALSE = not carrier, TRUE = carrier, NA = unknown).

asymptomatic

A logical vector with the asymptomatic status of the individuals (optional). An asymptomatic individual being an individual clinically unaffected at this time but could later exhibit symptoms. (i.e. FALSE = not asymptomatic, TRUE = asymptomatic, NA = unknown).

adopted

A logical vector with the adopted status of the individuals (optional). (i.e. FALSE = not adopted, TRUE = adopted, NA = unknown).

dateofbirth

A date vector with the birth date of the individuals (optional).

dateofdeath

A date vector with the death date of the individuals (optional).

useful

A logical vector with the usefulness status of the individuals (computed). (i.e. FALSE = not useful, TRUE = useful).

isinf

A logical vector indicating if the individual is informative or not (computed). (i.e. FALSE = not informative, TRUE = informative).

kin

A numeric vector with minimal kinship value between the individuals and the useful individuals (computed).

num_child_tot

A numeric vector with the total number of children of the individuals (computed).

num_child_dir

A numeric vector with the number of children of the individuals (computed).

num_child_ind

A numeric vector with the number of children of the individuals (computed).

elementMetadata

A DataFrame with the additional metadata columns of the Ped object.

metadata

Meta informations about the pedigree.

Accessors

For all the following accessors, the x parameters is a Ped object. Each getters return a vector of the same length as x with the values of the corresponding slot. For each getter, you have a setter with the same name, to be use as slot(x) <- value. The value parameter is a vector of the same length as x, except for the mcols() accessors where value is a list or a data.frame with each elements with the same length as x.

  • id(x) : Individuals identifiers

  • dadid(x) : Individuals' father identifiers

  • momid(x) : Individuals' mother identifiers

  • famid(x) : Individuals' family identifiers

  • sex(x) : Individuals' gender

  • fertility(x) : Individuals' fertility status

  • miscarriage(x) : Individuals' miscarriage status

  • deceased(x) : Individuals' death status

  • avail(x) : Individuals' availability status

  • evaluated(x) : Individuals' evaluated status

  • consultand(x) : Individuals' consultand status

  • proband(x) : Individuals' proband status

  • carrier(x) : Individuals' carrier status

  • asymptomatic(x) : Individuals' asymptomatic status

  • adopted(x) : Individuals' adopted status

  • affected(x) : Individuals' affection status

  • dateofbirth(x) : Individuals' birth dates

  • dateofdeath(x) : Individuals' death dates

  • isinf(x) : Individuals' informativeness status

  • kin(x) : Individuals' kinship distance to the informative individuals

  • useful(x) : Individuals' usefullness status

  • mcols(x) : Individuals' metadata

Generics

  • summary(x): Compute the summary of a Ped object

  • show(x): Convert the Ped object to a data.frame and print it with its summary.

  • as.list(x): Convert a Ped object to a list with the metadata columns at the end.

  • as.data.frame(x): Convert a Ped object to a data.frame with the metadata columns at the end.

  • subset(x, i, del_parents = FALSE, keep = TRUE): Subset a Ped object based on the individuals identifiers given.

    • i : A vector of individuals identifiers to keep.

    • del_parents : A value indicating if the parents of the individuals should be deleted.

    • keep : A logical value indicating if the individuals should be kept or deleted.

See also

Pedigree()

Examples


data(sampleped)
Ped(sampleped)
#> Ped object with 55 individuals and 2 metadata columns:
#>                    id       dadid       momid       famid       sex fertility
#> col_class <character> <character> <character> <character> <ordered>  <factor>
#> 101               101        <NA>        <NA>           1      male   fertile
#> 102               102        <NA>        <NA>           1    female   fertile
#> 103               103         135         136           1      male   fertile
#> 104               104        <NA>        <NA>           1    female   fertile
#> 105               105        <NA>        <NA>           1      male   fertile
#> ...               ...         ...         ...         ...       ...       ...
#> 210               210         203         204           2      male   fertile
#> 211               211         203         204           2      male   fertile
#> 212               212         209         208           2    female   fertile
#> 213               213         209         208           2      male   fertile
#> 214               214         209         208           2      male   fertile
#>           miscarriage  deceased     avail evaluated consultand   proband
#> col_class    <factor> <logical> <logical> <logical>  <logical> <logical>
#> 101             FALSE      <NA>     FALSE      TRUE      FALSE     FALSE
#> 102             FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 103             FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 104             FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 105             FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> ...               ...       ...       ...       ...        ...       ...
#> 210             FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 211             FALSE      <NA>      TRUE     FALSE      FALSE     FALSE
#> 212             FALSE      <NA>      TRUE     FALSE      FALSE     FALSE
#> 213             FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 214             FALSE      <NA>      TRUE     FALSE      FALSE     FALSE
#>            affected   carrier asymptomatic   adopted dateofbirth dateofdeath
#> col_class <logical> <logical>    <logical> <logical> <character> <character>
#> 101            <NA>      <NA>         <NA>      <NA>  1968-01-22        <NA>
#> 102            <NA>      <NA>         <NA>      <NA>  1975-06-27        <NA>
#> 103            <NA>      <NA>         <NA>      <NA>  1975-08-14        <NA>
#> 104            <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#> 105            <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#> ...             ...       ...          ...       ...         ...         ...
#> 210            <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#> 211            <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#> 212            <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#> 213            <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#> 214            <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#>              useful       kin     isinf num_child_tot num_child_dir
#> col_class <logical> <numeric> <logical>     <numeric>     <numeric>
#> 101            <NA>      <NA>      <NA>             1             1
#> 102            <NA>      <NA>      <NA>             1             1
#> 103            <NA>      <NA>      <NA>             4             4
#> 104            <NA>      <NA>      <NA>             4             4
#> 105            <NA>      <NA>      <NA>             4             4
#> ...             ...       ...       ...           ...           ...
#> 210            <NA>      <NA>      <NA>             0             0
#> 211            <NA>      <NA>      <NA>             0             0
#> 212            <NA>      <NA>      <NA>             0             0
#> 213            <NA>      <NA>      <NA>             0             0
#> 214            <NA>      <NA>      <NA>             0             0
#>           num_child_ind |   affection         num
#> col_class     <numeric>   <character> <character>
#> 101                   0             0           2
#> 102                   0             1           3
#> 103                   0             1           2
#> 104                   0             0           4
#> 105                   0          <NA>           6
#> ...                 ...           ...         ...
#> 210                   0             0           2
#> 211                   0             0           1
#> 212                   0             0           3
#> 213                   0             0           2
#> 214                   0             1           0

Ped(
    obj = c("1", "2", "3", "4", "5", "6"),
    dadid = c("4", "4", "6", "0", "0", "0"),
    momid = c("5", "5", "5", "0", "0", "0"),
    sex = c(1, 2, 3, 1, 2, 1),
    missid = "0"
)
#> Ped object with 6 individuals and 0 metadata columns:
#>                    id       dadid       momid       famid       sex fertility
#> col_class <character> <character> <character> <character> <ordered>  <factor>
#> 1                   1           4           5        <NA>      male   fertile
#> 2                   2           4           5        <NA>    female   fertile
#> 3                   3           6           5        <NA>   unknown   fertile
#> 4                   4        <NA>        <NA>        <NA>      male   fertile
#> 5                   5        <NA>        <NA>        <NA>    female   fertile
#> 6                   6        <NA>        <NA>        <NA>      male   fertile
#>           miscarriage  deceased     avail evaluated consultand   proband
#> col_class    <factor> <logical> <logical> <logical>  <logical> <logical>
#> 1               FALSE      <NA>      <NA>     FALSE      FALSE     FALSE
#> 2               FALSE      <NA>      <NA>     FALSE      FALSE     FALSE
#> 3               FALSE      <NA>      <NA>     FALSE      FALSE     FALSE
#> 4               FALSE      <NA>      <NA>     FALSE      FALSE     FALSE
#> 5               FALSE      <NA>      <NA>     FALSE      FALSE     FALSE
#> 6               FALSE      <NA>      <NA>     FALSE      FALSE     FALSE
#>            affected   carrier asymptomatic   adopted dateofbirth dateofdeath
#> col_class <logical> <logical>    <logical> <logical> <character> <character>
#> 1              <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#> 2              <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#> 3              <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#> 4              <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#> 5              <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#> 6              <NA>      <NA>         <NA>      <NA>        <NA>        <NA>
#>              useful       kin     isinf num_child_tot num_child_dir
#> col_class <logical> <numeric> <logical>     <numeric>     <numeric>
#> 1              <NA>      <NA>      <NA>             0             0
#> 2              <NA>      <NA>      <NA>             0             0
#> 3              <NA>      <NA>      <NA>             0             0
#> 4              <NA>      <NA>      <NA>             3             2
#> 5              <NA>      <NA>      <NA>             3             3
#> 6              <NA>      <NA>      <NA>             3             1
#>           num_child_ind
#> col_class     <numeric>
#> 1                     0
#> 2                     0
#> 3                     0
#> 4                     1
#> 5                     0
#> 6                     2