Pedigree object
Source:R/AllClass.R, R/AllConstructor.R, R/AllAccessors.R, and 1 more
Pedigree-class.RdA 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).
Usage
Pedigree(obj, ...)
# S4 method for class 'character_OR_integer'
Pedigree(
obj,
dadid,
momid,
sex,
famid = NA,
avail = NULL,
affected = NULL,
status = NULL,
steril = NULL,
rel_df = NULL,
missid = NA_character_,
col_aff = "affection",
normalize = TRUE,
...
)
# S4 method for class 'data.frame'
Pedigree(
obj = data.frame(indId = character(), fatherId = character(), motherId = character(),
gender = numeric(), family = character(), available = numeric(), vitalStatus =
numeric(), affection = numeric(), sterilisation = numeric()),
rel_df = data.frame(id1 = character(), id2 = character(), code = numeric(), famid =
character()),
cols_ren_ped = list(indId = "id", fatherId = "dadid", motherId = "momid", family =
"famid", gender = "sex", sterilisation = "steril", affection = "affected", available
= "avail", vitalStatus = "status"),
cols_ren_rel = list(id1 = "indId1", id2 = "indId2", famid = "family"),
hints = list(horder = NULL, spouse = NULL),
normalize = TRUE,
missid = NA_character_,
col_aff = "affection",
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<terminatedThe following values are recognized:character() or factor() : "f", "m", "woman", "man", "male", "female", "unknown", "terminated"
numeric() : 1 = "male", 2 = "female", 3 = "unknown", 4 = "terminated"
- famid
A character vector with the family identifiers of the individuals. If provide, will be aggregated to the individuals identifiers separated by an underscore.
- 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). Can also be a data.frame with the same length asobj. If it is a matrix, it will be converted to a data.frame and the columns will be named after thecol_affargument.- status
A logical vector with the affection status of the individuals (i.e.
FALSE= alive,TRUE= dead,NA= unknown).- steril
A logical vector with the sterilisation status of the individuals (i.e.
FALSE= not sterilised,TRUE= sterilised,NA= unknown).- rel_df
A data.frame with the special relationships between individuals. See
Rel()for more informations. The minimum columns required areid1,id2andcode. Thefamidcolumn can also be used to specify the family of the individuals. If a matrix is given, the columns needs to be ordered asid1,id2,codeandfamid. The code values are:1= Monozygotic twin2= Dizygotic twin3= twin of unknown zygosity4= 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.
- 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
horderandspouse.- na_strings
Vector of strings to be considered as NA values.
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 will depend if the normalization is selected or not. If the normalization is selected, the columns names needed are as follow and if not the columns names needed are in parenthesis:
indID: the individual identifier (id)fatherId: the identifier of the biological father (dadid)motherId: the identifier of the biological mother (momid)gender: the sex of the individual (sex)family: the family identifier of the individual (famid)sterilisation: the sterilisation status of the individual (steril)available: the availability status of the individual (avail)vitalStatus: the death status of the individual (status)affection: the affection status of the individual (affected)...: other columns that will be stored in theelementMetadataslot
The minimum columns required are :
indID/idfatherId/dadidmotherId/momidgender/sex
The family / famid column can also be used to specify the
family of the individuals and will be merge to the
indId / id field separated by an underscore.
The columns sterilisation, available,
vitalStatus, affection
will be transformed with the vect_to_binary()
function when the normalisation is selected.
If you do not use the normalisation, the columns will be checked to
be 0 or 1.
If affected is a data.frame, col_aff will be
overwritten by the column names of the data.frame.
Slots
pedA Ped object for the identity informations. See
Ped()for more informations.relA Rel object for the special relationships. See
Rel()for more informations.scalesA Scales object for the filling and bordering colors used in the plot. See
Scales()for more informations.hintsA 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 ofslot(ped(x)) <- value
ped(x) <- value: Set the Ped object
mcols(x): Get the metadata of a Pedigree object. This function is a wrapper aroundmcols(ped(x)).
mcols(x) <- value: Set the metadata of a Pedigree object. This function is a wrapper aroundmcols(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 ofslot(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 offill(scales(x))
fill(x) <- value: Set the fill data.frame from the Scales object. Wrapper offill(scales(x)) <- value
border(x): Get the border data.frame from the Scales object. Wrapper ofborder(scales(x))
border(x) <- value: Set the border data.frame from the Scales object. Wrapper ofborder(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 ofhorder(hints(x))
horder(x) <- value: Set the horder vector from the Hints object. Wrapper ofhorder(hints(x)) <- value
spouse(x): Get the spouse data.frame from the Hints object. Wrapper ofspouse(hints(x)).
spouse(x) <- value: Set the spouse data.frame from the Hints object. Wrapper ofspouse(hints(x)) <- value.
Generics
length(x): Get the length of a Pedigree object. Wrapper oflength(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),
affected = 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 15 metadata columns:
#> id dadid momid sex famid steril
#> col_class <character> <character> <character> <ordered> <character> <logical>
#> 1 1 4 5 male <NA> <NA>
#> 2 2 4 5 female <NA> <NA>
#> 3 3 6 5 unknown <NA> <NA>
#> 4 4 <NA> <NA> male <NA> <NA>
#> 5 5 <NA> <NA> female <NA> <NA>
#> 6 6 <NA> <NA> male <NA> <NA>
#> status avail affected useful kin isinf
#> col_class <logical> <logical> <logical> <logical> <numeric> <logical>
#> 1 <NA> FALSE TRUE <NA> <NA> <NA>
#> 2 <NA> TRUE TRUE <NA> <NA> <NA>
#> 3 <NA> FALSE TRUE <NA> <NA> <NA>
#> 4 <NA> TRUE TRUE <NA> <NA> <NA>
#> 5 <NA> FALSE TRUE <NA> <NA> <NA>
#> 6 <NA> TRUE TRUE <NA> <NA> <NA>
#> num_child_tot num_child_dir num_child_ind | family indId
#> col_class <numeric> <numeric> <numeric> <character> <character>
#> 1 0 0 0 0 1
#> 2 0 0 0 0 2
#> 3 0 0 0 0 3
#> 4 3 2 1 0 4
#> 5 3 3 0 0 5
#> 6 3 1 2 0 6
#> fatherId motherId gender V1 V2
#> col_class <character> <character> <character> <character> <character>
#> 1 4 5 1 0 1
#> 2 4 5 2 1 1
#> 3 6 5 3 0 1
#> 4 0 0 1 1 1
#> 5 0 0 2 0 1
#> 6 0 0 1 1 1
#> available error sterilisation vitalStatus affection
#> col_class <character> <character> <character> <character> <character>
#> 1 0 <NA> <NA> <NA> <NA>
#> 2 1 <NA> <NA> <NA> <NA>
#> 3 0 <NA> <NA> <NA> <NA>
#> 4 1 <NA> <NA> <NA> <NA>
#> 5 0 <NA> <NA> <NA> <NA>
#> 6 1 <NA> <NA> <NA> <NA>
#> V1_mods avail_mods V2_mods
#> col_class <character> <character> <character>
#> 1 0 0 1
#> 2 1 1 1
#> 3 0 0 1
#> 4 1 1 1
#> 5 0 0 1
#> 6 1 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 13 metadata columns:
#> id dadid momid sex famid steril
#> col_class <character> <character> <character> <ordered> <character> <logical>
#> 1_101 1_101 <NA> <NA> male 1 <NA>
#> 1_102 1_102 <NA> <NA> female 1 <NA>
#> 1_103 1_103 1_135 1_136 male 1 <NA>
#> 1_104 1_104 <NA> <NA> female 1 <NA>
#> 1_105 1_105 <NA> <NA> male 1 <NA>
#> ... ... ... ... ... ... ...
#> 2_210 2_210 2_203 2_204 male 2 <NA>
#> 2_211 2_211 2_203 2_204 male 2 <NA>
#> 2_212 2_212 2_209 2_208 female 2 <NA>
#> 2_213 2_213 2_209 2_208 male 2 <NA>
#> 2_214 2_214 2_209 2_208 male 2 <NA>
#> status avail affected useful kin isinf
#> col_class <logical> <logical> <logical> <logical> <numeric> <logical>
#> 1_101 <NA> FALSE FALSE <NA> <NA> <NA>
#> 1_102 <NA> FALSE TRUE <NA> <NA> <NA>
#> 1_103 <NA> FALSE TRUE <NA> <NA> <NA>
#> 1_104 <NA> FALSE FALSE <NA> <NA> <NA>
#> 1_105 <NA> FALSE <NA> <NA> <NA> <NA>
#> ... ... ... ... ... ... ...
#> 2_210 <NA> FALSE FALSE <NA> <NA> <NA>
#> 2_211 <NA> TRUE FALSE <NA> <NA> <NA>
#> 2_212 <NA> TRUE FALSE <NA> <NA> <NA>
#> 2_213 <NA> FALSE FALSE <NA> <NA> <NA>
#> 2_214 <NA> TRUE TRUE <NA> <NA> <NA>
#> num_child_tot num_child_dir num_child_ind | family indId
#> col_class <numeric> <numeric> <numeric> <character> <character>
#> 1_101 1 1 0 1 101
#> 1_102 1 1 0 1 102
#> 1_103 4 4 0 1 103
#> 1_104 4 4 0 1 104
#> 1_105 4 4 0 1 105
#> ... ... ... ... ... ...
#> 2_210 0 0 0 2 210
#> 2_211 0 0 0 2 211
#> 2_212 0 0 0 2 212
#> 2_213 0 0 0 2 213
#> 2_214 0 0 0 2 214
#> fatherId motherId gender affection available
#> col_class <character> <character> <character> <character> <character>
#> 1_101 <NA> <NA> 1 0 0
#> 1_102 <NA> <NA> 2 1 0
#> 1_103 135 136 1 1 0
#> 1_104 <NA> <NA> 2 0 0
#> 1_105 <NA> <NA> 1 <NA> 0
#> ... ... ... ... ... ...
#> 2_210 203 204 1 0 0
#> 2_211 203 204 1 0 1
#> 2_212 209 208 2 0 1
#> 2_213 209 208 1 0 0
#> 2_214 209 208 1 1 1
#> num error sterilisation vitalStatus affection_mods
#> col_class <character> <character> <character> <character> <character>
#> 1_101 2 <NA> <NA> <NA> 0
#> 1_102 3 <NA> <NA> <NA> 1
#> 1_103 2 <NA> <NA> <NA> 1
#> 1_104 4 <NA> <NA> <NA> 0
#> 1_105 6 <NA> <NA> <NA> NA
#> ... ... ... ... ... ...
#> 2_210 2 <NA> <NA> <NA> 0
#> 2_211 1 <NA> <NA> <NA> 0
#> 2_212 3 <NA> <NA> <NA> 0
#> 2_213 2 <NA> <NA> <NA> 0
#> 2_214 0 <NA> <NA> <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>