Pedigree object

A pedigree is a ensemble of individuals linked to each other into a family tree. A Pedigree object store the informations of the individuals and the special relationships between them. It also permit to store the informations needed to plot the pedigree (i.e. scales and hints).

Constructor :

Main constructor of the package. This constructor help to create a Pedigree object from different data.frame or a set of vectors.

If any errors are found in the data, the function will return the data.frame with the errors of the Ped object and the Rel object.

Usage

Pedigree(obj, ...)

# S4 method for class 'character_OR_integer'
Pedigree(
  obj,
  dadid,
  momid,
  sex,
  famid = NA,
  fertility = NULL,
  miscarriage = NULL,
  deceased = NULL,
  avail = NULL,
  evaluated = NULL,
  consultand = NULL,
  proband = NULL,
  affections = NULL,
  carrier = NULL,
  asymptomatic = NULL,
  adopted = NULL,
  dateofbirth = NULL,
  dateofdeath = NULL,
  rel_df = NULL,
  missid = NA_character_,
  col_aff = "affection",
  date_pattern = "%Y-%m-%d",
  normalize = TRUE,
  ...
)

# S4 method for class 'data.frame'
Pedigree(
  obj = data.frame(id = character(), dadid = character(), momid = character(), famid =
    character(), sex = numeric(), fertility = numeric(), miscarriage = numeric(),
    deceased = numeric(), avail = numeric(), evaluated = logical(), consultand =
    logical(), proband = logical(), affection = logical(), carrier = logical(),
    asymptomatic = logical(), adopted = logical(), dateofbirth = character(), dateofdeath
    = character()),
  rel_df = data.frame(id1 = character(), id2 = character(), code = numeric(), famid =
    character()),
  cols_ren_ped = list(id = "indId", dadid = "fatherId", momid = "motherId", famid =
    "family", sex = "gender", fertility = c("sterilisation", "steril"), miscarriage =
    c("miscarriage", "aborted"), deceased = c("status", "dead", "vitalStatus"), avail =
    "available", evaluated = "evaluation", consultand = "consultant", proband =
    "proband", affection = "affected", carrier = "carrier", asymptomatic =
    "presymptomatic", adopted = "adoption", dateofbirth = c("dob", "birth"), dateofdeath
    = c("dod", "death")),
  cols_ren_rel = list(id1 = "indId1", id2 = "indId2", famid = "family"),
  hints = list(horder = NULL, spouse = NULL),
  normalize = TRUE,
  missid = NA_character_,
  col_aff = "affection",
  date_pattern = "%Y-%m-%d",
  na_strings = c("NA", "N/A", "None", "none", "null", "NULL"),
  ...
)

Arguments

obj

A vector of the individuals identifiers or a data.frame with the individuals informations. See Ped() for more informations.

...

Arguments passed on to generate_colors

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).

affections

A logical vector with the affections status of the individuals (i.e. FALSE = unaffected, TRUE = affected, NA = unknown). Can also be a data.frame with the same length as obj. If it is a matrix, it will be converted to a data.frame and the columns will be named after the col_aff argument.

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.

rel_df

A data.frame with the special relationships between individuals. See Rel() for more informations. The minimum columns required are id1, id2 and code. The famid column can also be used to specify the family of the individuals. If a matrix is given, the columns needs to be ordered as id1, id2, code and famid. The code values are:

1 = Monozygotic twin
2 = Dizygotic twin
3 = twin of unknown zygosity
4 = Spouse

The value relation code recognized by the function are the one defined by the rel_code_to_factor() function.

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_.

col_aff

A character vector with the name of the column to be used for the affection status.

date_pattern

The pattern of the date

normalize

A logical to know if the data should be normalised.

cols_ren_ped

A named list with the columns to rename for the pedigree dataframe. This is useful if you want to use a dataframe with different column names. The names of the list should be the new column names and the values should be the old column names. The default values are to be used with normalize = TRUE.

cols_ren_rel

A named list with the columns to rename for the relationship matrix. This is useful if you want to use a dataframe with different column names. The names of the list should be the new column names and the values should be the old column names.

hints

A Hints object or a named list containing horder and spouse.

na_strings

Vector of strings to be considered as NA values.

Value

A Pedigree object.

Details

If the normalization is set to TRUE, then the data will be standardized using the function norm_ped() and norm_rel().

If a data.frame is given, the columns names needed are as follow:

id: the individual identifier
dadid: the identifier of the biological father
momid: the identifier of the biological mother
famid: the family identifier of the individual
sex: the sex of the individual
fertility: the fertility status of the individual
miscarriage: the miscarriage status of the individual
deceased: the death status of the individual
avail: the availability status of the individual
evaluated: the evaluation status of the individual
consultand: the consultand status of the individual
proband: the proband status of the individual
affection: the affection status of the individual
carrier: the carrier status of the individual
asymptomatic: the asymptomatic status of the individual
adopted: the adopted status of the individual
dateofbirth: the date of birth of the individual
dateofdeath: the date of death of the individual
...: other columns that will be stored in the elementMetadata slot

The minimum columns required are :

id
dadid
momid
sex

The famid column can also be used to specify the family of the individuals and will be merge to the id field separated by an underscore.

The columns deceased, avail, evaluated, consultand, proband, carrier, asymptomatic, adopted will be transformed with the vect_to_binary() function when the normalisation is selected.

The fertility column will be transformed with the fertility_to_factor() function.

The miscarriage column will be transformed with the miscarriage_to_factor() function.

The dateofbirth and dateofdeath columns will be transformed with the char_to_date() function.

If affections is a data.frame, col_aff will be overwritten by the column names of the data.frame.

Slots

ped: A Ped object for the identity informations. See Ped() for more informations.
rel: A Rel object for the special relationships. See Rel() for more informations.
scales: A Scales object for the filling and bordering colors used in the plot. See Scales() for more informations.
hints: A Hints object for the ordering of the individuals in the plot. See Hints() for more informations.

Accessors

ped(x, slot) : Get the value of a specific slot of the Ped object

ped(x) : Get the Ped object

ped(x, slot) <- value : Set the value of a specific slot of the Ped object Wrapper of slot(ped(x)) <- value

ped(x) <- value : Set the Ped object

mcols(x) : Get the metadata of a Pedigree object. This function is a wrapper around mcols(ped(x)).

mcols(x) <- value : Set the metadata of a Pedigree object. This function is a wrapper around mcols(ped(x)) <- value.

rel(x, slot) : Get the value of a specific slot of the Rel object

rel(x) : Get the Rel object

rel(x, slot) <- value : Set the value of a specific slot of the Rel object Wrapper of slot(rel(x)) <- value

rel(x) <- value : Set the Rel object

scales(x) : Get the Scales object

scales(x) <- value : Set the Scales object

fill(x) : Get the fill data.frame from the Scales object. Wrapper of fill(scales(x))

fill(x) <- value : Set the fill data.frame from the Scales object. Wrapper of fill(scales(x)) <- value

border(x) : Get the border data.frame from the Scales object. Wrapper of border(scales(x))

border(x) <- value : Set the border data.frame from the Scales object. Wrapper of border(scales(x)) <- value

hints(x) : Get the Hints object

hints(x) <- value : Set the Hints object

horder(x) : Get the horder vector from the Hints object. Wrapper of horder(hints(x))

horder(x) <- value : Set the horder vector from the Hints object. Wrapper of horder(hints(x)) <- value

spouse(x) : Get the spouse data.frame from the Hints object. Wrapper of spouse(hints(x)).

spouse(x) <- value : Set the spouse data.frame from the Hints object. Wrapper of spouse(hints(x)) <- value.

Generics

length(x): Get the length of a Pedigree object. Wrapper of length(ped(x)).

show(x): Print the information of the Ped and Rel object inside the Pedigree object.

summary(x): Compute the summary of the Ped and Rel object inside the Pedigree object.

as.list(x): Convert a Pedigree object to a list

subset(x, i, keep = TRUE): Subset a Pedigree object based on the individuals identifiers given.
- i : A vector of individuals identifiers to keep.
- del_parents : A logical value indicating if the parents of the individuals should be deleted.
- keep : A logical value indicating if the individuals should be kept or deleted.

x[i, del_parents, keep]: Subset a Pedigree object based on the individuals identifiers given.

Examples


Pedigree(
    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),
    avail = c(0, 1, 0, 1, 0, 1),
    affections = matrix(c(
        0, 1, 0, 1, 0, 1,
        1, 1, 1, 1, 1, 1
    ), ncol = 2),
    col_aff = c("aff1", "aff2"),
    missid = "0",
    rel_df = matrix(c(
        "1", "2", 2
    ), ncol = 3, byrow = TRUE),
)
#> Pedigree object with: 
#> Ped object with 6 individuals and 6 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>     FALSE     FALSE      FALSE     FALSE
#> 2               FALSE      <NA>      TRUE     FALSE      FALSE     FALSE
#> 3               FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 4               FALSE      <NA>      TRUE     FALSE      FALSE     FALSE
#> 5               FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 6               FALSE      <NA>      TRUE     FALSE      FALSE     FALSE
#>            affected   carrier asymptomatic   adopted dateofbirth dateofdeath
#> col_class <logical> <logical>    <logical> <logical> <character> <character>
#> 1              TRUE      <NA>         <NA>     FALSE        <NA>        <NA>
#> 2              TRUE      <NA>         <NA>     FALSE        <NA>        <NA>
#> 3              TRUE      <NA>         <NA>     FALSE        <NA>        <NA>
#> 4              TRUE      <NA>         <NA>     FALSE        <NA>        <NA>
#> 5              TRUE      <NA>         <NA>     FALSE        <NA>        <NA>
#> 6              TRUE      <NA>         <NA>     FALSE        <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 |          V1          V2       error     V1_mods
#> col_class     <numeric>   <character> <character> <character> <character>
#> 1                     0             0           1        <NA>           0
#> 2                     0             1           1        <NA>           1
#> 3                     0             0           1        <NA>           0
#> 4                     1             1           1        <NA>           1
#> 5                     0             0           1        <NA>           0
#> 6                     2             1           1        <NA>           1
#>            avail_mods     V2_mods
#> col_class <character> <character>
#> 1                   0           1
#> 2                   1           1
#> 3                   0           1
#> 4                   1           1
#> 5                   0           1
#> 6                   1           1
#> Rel object with 1 relationshipwith 0 MZ twin, 1 DZ twin, 0 UZ twin, 0 Spouse:
#>           id1         id2                     code       famid
#>   <character> <character> <c("ordered", "factor")> <character>
#> 1           1           2                  DZ twin        <NA>

data(sampleped)
Pedigree(sampleped)
#> Pedigree object with: 
#> Ped object with 55 individuals and 5 metadata columns:
#>                    id       dadid       momid       famid       sex fertility
#> col_class <character> <character> <character> <character> <ordered>  <factor>
#> 1_101           1_101        <NA>        <NA>           1      male   fertile
#> 1_102           1_102        <NA>        <NA>           1    female   fertile
#> 1_103           1_103       1_135       1_136           1      male   fertile
#> 1_104           1_104        <NA>        <NA>           1    female   fertile
#> 1_105           1_105        <NA>        <NA>           1      male   fertile
#> ...               ...         ...         ...         ...       ...       ...
#> 2_210           2_210       2_203       2_204           2      male   fertile
#> 2_211           2_211       2_203       2_204           2      male   fertile
#> 2_212           2_212       2_209       2_208           2    female   fertile
#> 2_213           2_213       2_209       2_208           2      male   fertile
#> 2_214           2_214       2_209       2_208           2      male   fertile
#>           miscarriage  deceased     avail evaluated consultand   proband
#> col_class    <factor> <logical> <logical> <logical>  <logical> <logical>
#> 1_101           FALSE      <NA>     FALSE      TRUE      FALSE     FALSE
#> 1_102           FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 1_103           FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 1_104           FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 1_105           FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> ...               ...       ...       ...       ...        ...       ...
#> 2_210           FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 2_211           FALSE      <NA>      TRUE     FALSE      FALSE     FALSE
#> 2_212           FALSE      <NA>      TRUE     FALSE      FALSE     FALSE
#> 2_213           FALSE      <NA>     FALSE     FALSE      FALSE     FALSE
#> 2_214           FALSE      <NA>      TRUE     FALSE      FALSE     FALSE
#>            affected   carrier asymptomatic   adopted dateofbirth dateofdeath
#> col_class <logical> <logical>    <logical> <logical> <character> <character>
#> 1_101         FALSE      <NA>         <NA>     FALSE  1968-01-22        <NA>
#> 1_102          TRUE      <NA>         <NA>     FALSE  1975-06-27        <NA>
#> 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>
#> ...             ...       ...          ...       ...         ...         ...
#> 2_210         FALSE      <NA>         <NA>     FALSE        <NA>        <NA>
#> 2_211         FALSE      <NA>         <NA>     FALSE        <NA>        <NA>
#> 2_212         FALSE      <NA>         <NA>     FALSE        <NA>        <NA>
#> 2_213         FALSE      <NA>         <NA>     FALSE        <NA>        <NA>
#> 2_214          TRUE      <NA>         <NA>     FALSE        <NA>        <NA>
#>              useful       kin     isinf num_child_tot num_child_dir
#> col_class <logical> <numeric> <logical>     <numeric>     <numeric>
#> 1_101          <NA>      <NA>      <NA>             1             1
#> 1_102          <NA>      <NA>      <NA>             1             1
#> 1_103          <NA>      <NA>      <NA>             4             4
#> 1_104          <NA>      <NA>      <NA>             4             4
#> 1_105          <NA>      <NA>      <NA>             4             4
#> ...             ...       ...       ...           ...           ...
#> 2_210          <NA>      <NA>      <NA>             0             0
#> 2_211          <NA>      <NA>      <NA>             0             0
#> 2_212          <NA>      <NA>      <NA>             0             0
#> 2_213          <NA>      <NA>      <NA>             0             0
#> 2_214          <NA>      <NA>      <NA>             0             0
#>           num_child_ind |   affection         num       error affection_mods
#> col_class     <numeric>   <character> <character> <character>    <character>
#> 1_101                 0             0           2        <NA>              0
#> 1_102                 0             1           3        <NA>              1
#> 1_103                 0             1           2        <NA>              1
#> 1_104                 0             0           4        <NA>              0
#> 1_105                 0          <NA>           6        <NA>             NA
#> ...                 ...           ...         ...         ...            ...
#> 2_210                 0             0           2        <NA>              0
#> 2_211                 0             0           1        <NA>              0
#> 2_212                 0             0           3        <NA>              0
#> 2_213                 0             0           2        <NA>              0
#> 2_214                 0             1           0        <NA>              1
#>            avail_mods
#> col_class <character>
#> 1_101               0
#> 1_102               0
#> 1_103               0
#> 1_104               0
#> 1_105               0
#> ...               ...
#> 2_210               0
#> 2_211               1
#> 2_212               1
#> 2_213               0
#> 2_214               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>