A small data.frame containing the information related to synthetic profiles. The ancestry of the profiles used to generate the synthetic profiles must be present.

Source: R/RAIDS.R

The object is a data.frame with 7 columns. The row names of the data.frame must be the profile unique identifiers.

data(pedSynthetic)

Format

The data.frame containing the information about the synthetic profiles. The row names of the data.frame correspond to the profile unique identifiers. The data.frame contains 7 columns:

data.id

a character string representing the unique synthetic profile identifier.

case.id

a character string representing the unique profile identifier that was used to generate the synthetic profile.

sample.type

a character string representing the type of profile.

diagnosis

a character string representing the diagnosis of profile that was used to generate the synthetic profile.

source

a character string representing the source of the synthetic profile.

study.id

a character string representing the name of the study to which the synthetic profile is associated.

superPop

a character string representing the super population of the profile that was used to generate the synthetic profile.

Value

The data.frame containing the information about the synthetic profiles. The row names of the data.frame correspond to the profile unique identifiers. The data.frame contains 7 columns:

data.id

a character string representing the unique synthetic profile identifier.

case.id

a character string representing the unique profile identifier that was used to generate the synthetic profile.

sample.type

a character string representing the type of profile.

diagnosis

a character string representing the diagnosis of profile that was used to generate the synthetic profile.

source

a character string representing the source of the synthetic profile.

study.id

a character string representing the name of the study to which the synthetic profile is associated.

superPop

a character string representing the super population of the profile that was used to generate the synthetic profile.

Details

This dataset can be used to test the computeSyntheticROC function.

See also

computeSyntheticROC

for calculating the AUROC of the inferences for specific values of D and K using the inferred ancestry results from the synthetic profiles

Examples


## Loading demo dataset containing pedigree information for synthetic
## profiles
data(pedSynthetic)

## Loading demo dataset containing the inferred ancestry results
## for the synthetic data
data(matKNNSynthetic)

## Retain one K and one D value
matKNN <- matKNNSynthetic[matKNNSynthetic$D == 5 & matKNNSynthetic$K == 4, ]

## Compile statistics from the
## synthetic profiles for fixed values of D and K
results <- RAIDS:::computeSyntheticROC(matKNN=matKNN,
    matKNNAncestryColumn="SuperPop",
    pedCall=pedSynthetic, pedCallAncestryColumn="superPop",
    listCall=c("EAS", "EUR", "AFR", "AMR", "SAS"))

results$matAUROC.All
#>   pcaD K   ROC.AUC ROC.CI  N NBNA
#> 1    5 4 0.6227679      0 52    0
results$matAUROC.Call
#>   pcaD K Call         L       AUC         H
#> 1    5 4  EAS 0.4807257 0.6547619 0.8287981
#> 2    5 4  EUR 0.4064737 0.5666667 0.7268596
#> 3    5 4  AFR 0.8168697 0.9154135 1.0000000
#> 4    5 4  AMR 0.3743226 0.5056818 0.6370411
#> 5    5 4  SAS 0.3609393 0.5047619 0.6485845
results$listROC.Call
#> $EAS
#> 
#> Call:
#> roc.formula(formula = fCur ~ predMat[, j], ci = TRUE, quiet = TRUE)
#> 
#> Data: predMat[, j] in 42 controls (fCur 0) < 10 cases (fCur 1).
#> Area under the curve: 0.6548
#> 95% CI: 0.4807-0.8288 (DeLong)
#> 
#> $EUR
#> 
#> Call:
#> roc.formula(formula = fCur ~ predMat[, j], ci = TRUE, quiet = TRUE)
#> 
#> Data: predMat[, j] in 42 controls (fCur 0) < 10 cases (fCur 1).
#> Area under the curve: 0.5667
#> 95% CI: 0.4065-0.7269 (DeLong)
#> 
#> $AFR
#> 
#> Call:
#> roc.formula(formula = fCur ~ predMat[, j], ci = TRUE, quiet = TRUE)
#> 
#> Data: predMat[, j] in 38 controls (fCur 0) < 14 cases (fCur 1).
#> Area under the curve: 0.9154
#> 95% CI: 0.8169-1 (DeLong)
#> 
#> $AMR
#> 
#> Call:
#> roc.formula(formula = fCur ~ predMat[, j], ci = TRUE, quiet = TRUE)
#> 
#> Data: predMat[, j] in 44 controls (fCur 0) < 8 cases (fCur 1).
#> Area under the curve: 0.5057
#> 95% CI: 0.3743-0.637 (DeLong)
#> 
#> $SAS
#> 
#> Call:
#> roc.formula(formula = fCur ~ predMat[, j], ci = TRUE, quiet = TRUE)
#> 
#> Data: predMat[, j] in 42 controls (fCur 0) < 10 cases (fCur 1).
#> Area under the curve: 0.5048
#> 95% CI: 0.3609-0.6486 (DeLong)
#>