Package 'RAIDS'

Description

The RAIDS package implements specialized algorithms that enable ancestry inference from various cancer data sources (RNA, Exome and Whole-Genome sequencing).

Details

The RAIDS package also implements simulation algorithm that generates synthetic cancer-derived data.

This code and analysis pipeline was designed and developed for the following publication:

Pascal Belleau, Astrid Deschênes, Nyasha Chambwe, David A. Tuveson, Alexander Krasnitz; Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms. Cancer Res 1 January 2023; 83 (1): 49–58. https://doi.org/10.1158/0008-5472.CAN-22-0682

Value

RAIDS

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Maintainer: Pascal Belleau [email protected]

References

Pascal Belleau, Astrid Deschênes, Nyasha Chambwe, David A. Tuveson, Alexander Krasnitz; Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms. Cancer Res 1 January 2023; 83 (1): 49–58. https://doi.org/10.1158/0008-5472.CAN-22-0682

See Also

• runExomeAncestry This function runs most steps leading to the ancestry inference call on a specific exome profile.

• runExomeAncestry This function runs most steps leading to the ancestry inference call on a specific RNA profile.

• estimateAllelicFraction This function estimates the allelic fraction of the pruned SNVs for a specific sample and add the information to the associated GDS Sample file. The allelic fraction estimation method is adapted to the type of study (DNA or RNA).

• computeSyntheticROC This function calculate the AUROC of the inferences for specific values of D and K using the inferred ancestry results from the synthetic profiles.

• generateMapSnvSel The function applies a cut-off filter to the SNV information file to retain only the SNV that have a frequency superior or equal to the specified cut-off in at least one super population.

Description

The function extracts the information about the pruned SNVs from the 1KG GDS file and adds entries related to the pruned SNVs in the Profile GDS file. The nodes are added to the Profile GDS file: 'sample.id', 'snp.id', 'snp.chromosome', 'snp.position', 'snp.index', 'genotype' and 'lap'.

Usage

add1KG2SampleGDS(gdsReference, fileProfileGDS, currentProfile, studyID)

Arguments

Value

The function returns 0L when successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library for GDS
library(SNPRelate)

## Path to the demo 1KG GDS file is located in this package
dataDir <- system.file("extdata/tests", package="RAIDS")
fileGDS <- file.path(dataDir, "ex1_good_small_1KG.gds")

## The data.frame containing the information about the study
## The 3 mandatory columns: "studyID", "study.desc", "study.platform"
## The entries should be strings, not factors (stringsAsFactors=FALSE)
studyDF <- data.frame(study.id="MYDATA",
                        study.desc="Description",
                        study.platform="PLATFORM",
                        stringsAsFactors=FALSE)

## Temporary Profile file
fileProfile <- file.path(tempdir(), "ex2.gds")

## Copy required file
file.copy(file.path(dataDir, "ex1_demo_with_pruning.gds"),
        fileProfile)

## Open 1KG file
gds1KG <- snpgdsOpen(fileGDS)

## Compute the list of pruned SNVs for a specific profile 'ex1'
## and save it in the Profile GDS file 'ex2.gds'
add1KG2SampleGDS(gdsReference=gds1KG,
        fileProfileGDS=fileProfile,
        currentProfile=c("ex1"),
        studyID=studyDF$study.id)

## Close the 1KG GDS file (important)
closefn.gds(gds1KG)

## Check content of Profile GDS file
## The 'pruned.study' entry should be present
content <- openfn.gds(fileProfile)
content

## Close the Profile GDS file (important)
closefn.gds(content)

## Remove Profile GDS file (created for demo purpose)
unlink(fileProfile, force=TRUE)

Description

The function appends the information about the blocks into the Population Reference SNV Annotation GDS file. The information is extracted from the Population Reference GDS file.

Usage

addGeneBlockGDSRefAnnot(
  gdsReference,
  gdsRefAnnotFile,
  winSize = 10000,
  ensDb,
  suffixBlockName
)

Arguments

Value

The integer OL when the function is successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library
library(SNPRelate)

## Path to the demo pedigree file is located in this package
dataDir <- system.file("extdata", package="RAIDS")

fileAnnotGDS <- file.path(tempdir(), "ex1_good_small_1KG_Ann_GDS.gds")

## Required library
if (requireNamespace("EnsDb.Hsapiens.v86", quietly=TRUE)) {

    file.copy(file.path(dataDir, "tests",
        "ex1_NoBlockGene.1KG_Annot_GDS.gds"), fileAnnotGDS)

    ## Making a "short cut" on the ensDb object
    edb <- EnsDb.Hsapiens.v86::EnsDb.Hsapiens.v86

    ## GDS Reference file
    fileReferenceGDS  <- file.path(dataDir, "tests",
                "ex1_good_small_1KG.gds")

    
        ## Open the reference GDS file (demo version)
        gds1KG <- snpgdsOpen(fileReferenceGDS)

        ## Append information associated to blocks
        addGeneBlockGDSRefAnnot(gdsReference=gds1KG,
            gdsRefAnnotFile=fileAnnotGDS,
            ensDb=edb,
            suffixBlockName="EnsDb.Hsapiens.v86")

        gdsAnnot1KG <- openfn.gds(fileAnnotGDS)
        print(gdsAnnot1KG)
        print(read.gdsn(index.gdsn(gdsAnnot1KG, "block.annot")))

       ## Close GDS files
       closefn.gds(gds1KG)
       closefn.gds(gdsAnnot1KG)
    

    ## Remove temporary file
    unlink(fileAnnotGDS, force=TRUE)

}

Description

This function adds the information about the unrelated patients to the Reference GDS file. More specifically, it creates the field sample.ref which as the value 1 when the sample is unrelated and the value 0 otherwise. The sample.ref is filled based on the information present in the input RDS file.

Usage

addRef2GDS1KG(fileNameGDS, filePart)

Arguments

Value

The integer 0L when successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Locate RDS with unrelated/related status for 1KG samples
dataDir <- system.file("extdata", package="RAIDS")
rdsFilePath <- file.path(dataDir, "unrelatedPatientsInfo_Demo.rds")

## Create a temporary GDS file in an test directory
dataDir <- system.file("extdata/tests", package="RAIDS")
gdsFilePath <- file.path(dataDir, "GDS_TEMP_201.gds")

## Create and open the GDS file
tmpGDS  <- createfn.gds(filename=gdsFilePath)
## Create "sample.id" node (the node must be present)
sampleIDs <- c("HG00104", "HG00109", "HG00110")
add.gdsn(node=tmpGDS, name="sample.id", val=sampleIDs)

## Create "snp.id" node (the node must be present)
snpIDs <- c("s1", "s2", "s3", "s4", "s5", "s6")
add.gdsn(node=tmpGDS, name="snp.id", val=snpIDs)

## Create "snp.position" node (the node must be present)
snpPositions <- c(16102, 51478, 51897, 51927, 54489, 54707)
add.gdsn(node=tmpGDS, name="snp.position", val=snpPositions)

## Create "snp.chromosome" node (the node must be present)
snpPositions <- c(1, 1, 1, 1, 1, 1)
add.gdsn(node=tmpGDS, name="snp.chromosome", val=snpPositions)

## Create "genotype" node (the node must be present)
genotype <- matrix(rep(1, 18), ncol = 3)
add.gdsn(node=tmpGDS, name="genotype", val=genotype)

## Close GDS file
closefn.gds(tmpGDS)

## Create  "sample.ref" node in GDS file using RDS information
addRef2GDS1KG(fileNameGDS=gdsFilePath, filePart=rdsFilePath)

## Read sample reference data.frame
fileGDS <- openfn.gds(gdsFilePath, readonly=TRUE)
read.gdsn(index.gdsn(node=fileGDS, path="sample.ref"))
closefn.gds(gdsfile=fileGDS)

## Delete the temporary GDS file
unlink(x=gdsFilePath, force=TRUE)

Description

The information about the samples present in the 1KG GDS file is added into the GDS Sample file. Only the information about the unrelated samples from the 1OOO Genome Study are copied into the GDS Sample file. The information is only added to the GDS Sample file when the 1KG Study is not already present in the GDS Sample file. The sample information for all selected samples is appended to the GDS Sample file "study.annot" node. The study information is appended to the GDS Sample file "study.list" node.

Usage

addStudy1Kg(gdsReference, fileProfileGDS, verbose = FALSE)

Arguments

Value

The integer 0L when successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library for GDS
library(gdsfmt)

## Get the temp folder
tempDir <- tempdir()

## Create a temporary 1KG GDS file and add needed information
fileName1KG <- file.path(tempDir, "GDS_TEMP_addStudy1Kg_1KG.gds")
gds1KG  <- createfn.gds(filename=fileName1KG)
add.gdsn(gds1KG, "sample.id", c("HTT101", "HTT102", "HTT103"))

samples <- data.frame(sex=c(1, 1, 2), pop.group=c("GBR", "GIH", "GBR"),
    superPop=c("EUR", "SAS", "EUR"), batch=rep(0, 3),
    stringsAsFactors = FALSE)

add.gdsn(gds1KG, "sample.annot", samples)
add.gdsn(gds1KG, "sample.ref", c(1,0, 1))
sync.gds(gds1KG)

## Create a temporary Profile GDS file
fileNameProfile <- file.path(tempDir, "GDS_TEMP_addStudy1Kg_Sample.gds")
gdsProfile <- createfn.gds(fileNameProfile)

study.list <- data.frame(study.id=c("HTT Study"),
    study.desc=c("Important Study"),
    study.platform=c("Panel"), stringsAsFactors=FALSE)

add.gdsn(gdsProfile, "study.list", study.list)

study.annot <- data.frame(data.id=c("TOTO1"), case.id=c("TOTO1"),
                sample.type=c("Study"), diagnosis=c("Study"),
                source=rep("IGSR"), study.id=c("Study"),
                stringsAsFactors=FALSE)

add.gdsn(gdsProfile, "study.annot", study.annot)
sync.gds(gdsProfile)
closefn.gds(gdsProfile)

## Append information about the 1KG samples into the Profile GDS file
## The Profile GDS file will contain 'study.list' and 'study.annot' entries
addStudy1Kg(gdsReference=gds1KG, fileProfileGDS=fileNameProfile,
    verbose=TRUE)

closefn.gds(gds1KG)
unlink(fileNameProfile, recursive=TRUE, force=TRUE)
unlink(fileName1KG, recursive=TRUE, force=TRUE)
unlink(tempDir)

Description

The function select the optimal K and D parameters for a specific profile. The results on the synthetic data are used for the parameter selection. Once the optimal parameters are selected, the ancestry is inferred for the specific profile.

Usage

computeAncestryFromSyntheticFile(
  gdsReference,
  gdsProfile,
  listFiles,
  currentProfile,
  spRef,
  studyIDSyn,
  np = 1L,
  listCatPop = c("EAS", "EUR", "AFR", "AMR", "SAS"),
  fieldPopIn1KG = "superPop",
  fieldPopInfAnc = "SuperPop",
  kList = seq(2, 15, 1),
  pcaList = seq(2, 15, 1),
  algorithm = c("exact", "randomized"),
  eigenCount = 32L,
  missingRate = NaN,
  verbose = FALSE
)

Arguments

Value

a list containing 4 entries:

• pcaSample a list containing the information related to the eigenvectors. The list contains those 3 entries:

  • sample.id a character string representing the unique identifier of the current profile.

  • eigenvector.ref a matrix of numeric containing the eigenvectors for the reference profiles.

  • eigenvector a matrix of numeric containing the eigenvectors for the current profile projected on the PCA from the reference profiles.

• paraSample a list containing the results with different D and K values that lead to optimal parameter selection. The list contains those entries:

  • dfPCA a data.frame containing statistical results on all combined synthetic results done with a fixed value of D (the number of dimensions). The data.frame contains those columns:

    • D a numeric representing the value of D (the number of dimensions).

    • median a numeric representing the median of the minimum AUROC obtained (within super populations) for all combination of the fixed D value and all tested K values.

    • mad a numeric representing the MAD of the minimum AUROC obtained (within super populations) for all combination of the fixed D value and all tested K values.

    • upQuartile a numeric representing the upper quartile of the minimum AUROC obtained (within super populations) for all combination of the fixed D value and all tested K values.

    • k a numeric representing the optimal K value (the number of neighbors) for a fixed D value.

  • dfPop a data.frame containing statistical results on all combined synthetic results done with different values of D (the number of dimensions) and K (the number of neighbors). The data.frame contains those columns:

    • D a numeric representing the value of D (the number of dimensions).

    • K a numeric representing the value of K (the number of neighbors).

    • AUROC.min a numeric representing the minimum accuracy obtained by grouping all the synthetic results by super-populations, for the specified values of D and K.

    • AUROC a numeric representing the accuracy obtained by grouping all the synthetic results for the specified values of D and K.

    • Accu.CM a numeric representing the value of accuracy of the confusion matrix obtained by grouping all the synthetic results for the specified values of D and K.

  • dfAUROC a data.frame the summary of the results by super-population. The data.frame contains those columns:

    • pcaD a numeric representing the value of D (the number of dimensions).

    • K a numeric representing the value of K (the number of neighbors).

    • Call a character string representing the super-population.

    • L a numeric representing the lower value of the 95% confidence interval for the AUROC obtained for the fixed values of super-population, D and K.

    • AUR a numeric representing the AUROC obtained for the fixed values of super-population, D and K.

    • H a numeric representing the higher value of the 95% confidence interval for the AUROC obtained for the fixed values of super-population, D and K.

  • D a numeric representing the optimal D value (the number of dimensions) for the specific profile.

  • K a numeric representing the optimal K value (the number of neighbors) for the specific profile.

  • listD a numeric representing the optimal D values (the number of dimensions) for the specific profile. More than one D is possible.

• KNNSample a list containing the inferred ancestry using different D and K values. The list contains those entries:

  • sample.id a character string representing the unique identifier of the current profile.

  • matKNN a data.frame containing the inferred ancestry for different values of K and D. The data.frame contains those columns:

    • sample.id a character string representing the unique identifier of the current profile.

    • D a numeric representing the value of D (the number of dimensions) used to infer the ancestry.

    • K a numeric representing the value of K (the number of neighbors) used to infer the ancestry.

    • SuperPop a character string representing the inferred ancestry for the specified D and K values.

• Ancestry a data.frame containing the inferred ancestry for the current profile. The data.frame contains those columns:

  • sample.id a character string representing the unique identifier of the current profile.

  • D a numeric representing the value of D (the number of dimensions) used to infer the ancestry.

  • K a numeric representing the value of K (the number of neighbors) used to infer the ancestry.

  • SuperPop a character string representing the inferred ancestry.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

References

Galinsky KJ, Bhatia G, Loh PR, Georgiev S, Mukherjee S, Patterson NJ, Price AL. Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia. Am J Hum Genet. 2016 Mar 3;98(3):456-72. doi: 10.1016/j.ajhg.2015.12.022. Epub 2016 Feb 25.

Examples

## Required library
library(gdsfmt)

## Load the known ancestry for the demo 1KG reference profiles
data(demoKnownSuperPop1KG)

## The Reference GDS file
path1KG <- system.file("extdata/tests", package="RAIDS")

## Open the Reference GDS file
gdsRef <- snpgdsOpen(file.path(path1KG, "ex1_good_small_1KG.gds"))

## Path to the demo synthetic results files
## List of the KNN result files from PCA run on synthetic data
dataDirRes <- system.file("extdata/demoAncestryCall/ex1", package="RAIDS")
listFilesName <- dir(file.path(dataDirRes), ".rds")
listFiles <- file.path(file.path(dataDirRes) , listFilesName)

# The name of the synthetic study
studyID <- "MYDATA.Synthetic"

## Path to the demo Profile GDS file is located in this package
dataDir <- system.file("extdata/demoAncestryCall", package="RAIDS")

## Open the Profile GDS file
gdsProfile <- snpgdsOpen(file.path(dataDir, "ex1.gds"))

## Run the ancestry inference on one profile called 'ex1'
## The values of K and D used for the inference are selected using the
## synthetic results
resCall <- computeAncestryFromSyntheticFile(gdsReference=gdsRef,
                            gdsProfile=gdsProfile,
                            listFiles=listFiles,
                            currentProfile=c("ex1"),
                            spRef=demoKnownSuperPop1KG,
                            studyIDSyn=studyID, np=1L)

## The ancestry called with the optimal D and K values
resCall$Ancestry

## Close the GDS files (important)
closefn.gds(gdsProfile)
closefn.gds(gdsRef)

Description

The function runs k-nearest neighbors analysis on a one specific profile. The function uses the 'knn' package.

Usage

computeKNNRefSample(
  listEigenvector,
  listCatPop = c("EAS", "EUR", "AFR", "AMR", "SAS"),
  spRef,
  fieldPopInfAnc = "SuperPop",
  kList = seq(2, 15, 1),
  pcaList = seq(2, 15, 1)
)

Arguments

Value

a list containing 4 entries:

• sample.id a vector of character strings representing the identifier of the profile analysed.

• matKNN a data.frame containing the super population inference for the profile for different values of PCA dimensions D and k-neighbors values K. The fourth column title corresponds to the fieldPopInfAnc parameter. The data.frame contains 4 columns:

  • sample.id a character string representing the identifier of the profile analysed.

  • D a numeric strings representing the value of the PCA dimension used to infer the ancestry.

  • K a numeric strings representing the value of the k-neighbors used to infer the ancestry..

  • fieldPopInfAnc a character string representing the inferred ancestry.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Load the demo PCA on the synthetic profiles projected on the
## demo 1KG reference PCA
data(demoPCASyntheticProfiles)

## Load the known ancestry for the demo 1KG reference profiles
data(demoKnownSuperPop1KG)

## The PCA with 1 profile projected on the 1KG reference PCA
## Only one profile is retained
pca <- demoPCASyntheticProfiles
pca$sample.id <- pca$sample.id[1]
pca$eigenvector <- pca$eigenvector[1, , drop=FALSE]

## Projects profile on 1KG PCA
results <- computeKNNRefSample(listEigenvector=pca,
    listCatPop=c("EAS", "EUR", "AFR", "AMR", "SAS"),
    spRef=demoKnownSuperPop1KG, fieldPopInfAnc="SuperPop",
    kList=seq(10, 15, 1), pcaList=seq(10, 15, 1))

## The assigned ancestry to the profile for different values of K and D
head(results$matKNN)

Description

The function runs k-nearest neighbors analysis on a subset of the synthetic data set. The function uses the 'knn' package.

Usage

computeKNNRefSynthetic(
  gdsProfile,
  listEigenvector,
  listCatPop = c("EAS", "EUR", "AFR", "AMR", "SAS"),
  studyIDSyn,
  spRef,
  fieldPopInfAnc = "SuperPop",
  kList = seq(2, 15, 1),
  pcaList = seq(2, 15, 1)
)

Arguments

Value

a list containing 4 entries:

• sample.id a vector of character strings representing the identifiers of the synthetic profiles analysed.

• sample1Kg a vector of character strings representing the identifiers of the 1KG reference profiles used to generate the synthetic profiles.

• sp a vector of character strings representing the known super population ancestry of the 1KG reference profiles used to generate the synthetic profiles.

• matKNN a data.frame containing the super population inference for each synthetic profiles for different values of PCA dimensions D and k-neighbors values K. The fourth column title corresponds to the fieldPopInfAnc parameter. The data.frame contains 4 columns:

  • sample.id a character string representing the identifier of the synthetic profile analysed.

  • D a numeric strings representing the value of the PCA dimension used to infer the super population.

  • K a numeric strings representing the value of the k-neighbors used to infer the super population.

  • fieldPopInfAnc value a character string representing the inferred ancestry.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library
library(gdsfmt)

## Load the demo PCA on the synthetic profiles projected on the
## demo 1KG reference PCA
data(demoPCASyntheticProfiles)

## Load the known ancestry for the demo 1KG reference profiles
data(demoKnownSuperPop1KG)

## Path to the demo Profile GDS file is located in this package
dataDir <- system.file("extdata/demoKNNSynthetic", package="RAIDS")

## Open the Profile GDS file
gdsProfile <- snpgdsOpen(file.path(dataDir, "ex1.gds"))

# The name of the synthetic study
studyID <- "MYDATA.Synthetic"

## Projects synthetic profiles on 1KG PCA
results <- computeKNNRefSynthetic(gdsProfile=gdsProfile,
    listEigenvector=demoPCASyntheticProfiles,
    listCatPop=c("EAS", "EUR", "AFR", "AMR", "SAS"), studyIDSyn=studyID,
    spRef=demoKnownSuperPop1KG)

## The inferred ancestry for the synthetic profiles for different values
## of D and K
head(results$matKNN)

## Close Profile GDS file (important)
closefn.gds(gdsProfile)

Description

The function projects the synthetic profiles onto existing principal component axes generated using the reference 1KG profiles. The reference profiles used to generate the synthetic profiles have previously been removed from the set of reference profiles.

Usage

computePCAMultiSynthetic(
  gdsProfile,
  listPCA,
  sampleRef,
  studyIDSyn,
  verbose = FALSE
)

Arguments

Value

a list containing 3 entries:

• sample.id a vector of character strings representing the identifiers of the synthetic profiles that have been projected onto the 1KG PCA.

• eigenvector.ref a matrix of numeric with the eigenvectors of the 1KG reference profiles used to generate the PCA.

• eigenvector a matrix of numeric with the eigenvectors of the synthetic profiles projected onto the 1KG PCA.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library
library(gdsfmt)

## Loading demo PCA on subset of 1KG reference dataset
data(demoPCA1KG)

## Path to the demo Profile GDS file is located in this package
dataDir <- system.file("extdata/demoKNNSynthetic", package="RAIDS")

# The name of the synthetic study
studyID <- "MYDATA.Synthetic"

samplesRM <- c("HG00246", "HG00325", "HG00611", "HG01173", "HG02165",
    "HG01112", "HG01615", "HG01968", "HG02658", "HG01850", "HG02013",
    "HG02465", "HG02974", "HG03814", "HG03445", "HG03689", "HG03789",
    "NA12751", "NA19107", "NA18548", "NA19075", "NA19475", "NA19712",
    "NA19731", "NA20528", "NA20908")
names(samplesRM) <- c("GBR", "FIN", "CHS","PUR", "CDX", "CLM", "IBS",
    "PEL", "PJL", "KHV", "ACB", "GWD", "ESN", "BEB", "MSL", "STU", "ITU",
    "CEU", "YRI", "CHB", "JPT", "LWK", "ASW", "MXL", "TSI", "GIH")

## Open the Profile GDS file
gdsProfile <- snpgdsOpen(file.path(dataDir, "ex1.gds"))

## Projects synthetic profiles on 1KG PCA
results <- computePCAMultiSynthetic(gdsProfile=gdsProfile,
    listPCA=demoPCA1KG,
    sampleRef=samplesRM, studyIDSyn=studyID, verbose=FALSE)

## The eigenvectors for the synthetic profiles
head(results$eigenvector)

## Close Profile GDS file (important)
closefn.gds(gdsProfile)

Description

This function generates a PCA using the know reference profiles. Them, it projects the specified profile onto the PCA axes.

Usage

computePCARefSample(
  gdsProfile,
  currentProfile,
  studyIDRef = "Ref.1KG",
  np = 1L,
  algorithm = c("exact", "randomized"),
  eigenCount = 32L,
  missingRate = NaN,
  verbose = FALSE
)

Arguments

Value

a list containing 3 entries:

• sample.id a character string representing the unique identifier of the analyzed profile.

• eigenvector.ref a matrix of numeric representing the eigenvectors of the reference profiles.

• eigenvector a matrix of numeric representing the eigenvectors of the analyzed profile.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

References

Galinsky KJ, Bhatia G, Loh PR, Georgiev S, Mukherjee S, Patterson NJ, Price AL. Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia. Am J Hum Genet. 2016 Mar 3;98(3):456-72. doi: 10.1016/j.ajhg.2015.12.022. Epub 2016 Feb 25.

Examples

## Required library
library(gdsfmt)

## Path to the demo Profile GDS file is located in this package
dataDir <- system.file("extdata/demoAncestryCall", package="RAIDS")

## Open the Profile GDS file
gdsProfile <- snpgdsOpen(file.path(dataDir, "ex1.gds"))

## Project a profile onto a PCA generated using reference profiles
## The reference profiles come from 1KG
resPCA <- computePCARefSample(gdsProfile=gdsProfile,
    currentProfile=c("ex1"), studyIDRef="Ref.1KG", np=1L, verbose=FALSE)
resPCA$sample.id
resPCA$eigenvector

## Close the GDS files (important)
closefn.gds(gdsProfile)

Description

The function runs a PCA analysis using 1 synthetic profile from each sub-continental population. The reference profiles used to create those synthetic profiles are first removed from the list of 1KG reference profiles that generates the reference PCA. Then, the retained synthetic profiles are projected on the 1KG PCA space. Finally, a K-nearest neighbors analysis using a range of K and D values is done.

Usage

computePoolSyntheticAncestryGr(
  gdsProfile,
  sampleRM,
  spRef,
  studyIDSyn,
  np = 1L,
  listCatPop = c("EAS", "EUR", "AFR", "AMR", "SAS"),
  fieldPopInfAnc = "SuperPop",
  kList = seq(2, 15, 1),
  pcaList = seq(2, 15, 1),
  algorithm = c("exact", "randomized"),
  eigenCount = 32L,
  missingRate = 0.025,
  verbose = FALSE
)

Arguments

Value

a list containing the following entries:

• sample.ida vector of character strings representing the identifiers of the synthetic profiles.

• sample1Kga vector of character strings representing the identifiers of the reference 1KG profiles used to generate the synthetic profiles.

• spa vector of character strings representing the known ancestry for the reference 1KG profiles used to generate the synthetic profiles.

• matKNNa data.frame containing 4 columns. The first column 'sample.id' contains the name of the synthetic profile. The second column 'D' represents the dimension D used to infer the ancestry. The third column 'K' represents the number of neighbors K used to infer the ancestry. The fourth column 'SuperPop' contains the inferred ancestry.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

References

Galinsky KJ, Bhatia G, Loh PR, Georgiev S, Mukherjee S, Patterson NJ, Price AL. Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia. Am J Hum Genet. 2016 Mar 3;98(3):456-72. doi: 10.1016/j.ajhg.2015.12.022. Epub 2016 Feb 25.

Examples

## Required library
library(gdsfmt)

## Load the known ancestry for the demo 1KG reference profiles
data(demoKnownSuperPop1KG)


# The name of the synthetic study
studyID <- "MYDATA.Synthetic"

samplesRM <- c("HG00246", "HG00325", "HG00611", "HG01173", "HG02165",
    "HG01112", "HG01615", "HG01968", "HG02658", "HG01850", "HG02013",
    "HG02465", "HG02974", "HG03814", "HG03445", "HG03689", "HG03789",
    "NA12751", "NA19107", "NA18548", "NA19075", "NA19475", "NA19712",
    "NA19731", "NA20528", "NA20908")
names(samplesRM) <- c("GBR", "FIN", "CHS","PUR", "CDX", "CLM", "IBS",
    "PEL", "PJL", "KHV", "ACB", "GWD", "ESN", "BEB", "MSL", "STU", "ITU",
    "CEU", "YRI", "CHB", "JPT", "LWK", "ASW", "MXL", "TSI", "GIH")

## Path to the demo Profile GDS file is located in this package
dataDir <- system.file("extdata/demoKNNSynthetic", package="RAIDS")

## Open the Profile GDS file
gdsProfile <- snpgdsOpen(file.path(dataDir, "ex1.gds"))

## Run a PCA analysis and a K-nearest neighbors analysis on a small set
## of synthetic data
results <- computePoolSyntheticAncestryGr(gdsProfile=gdsProfile,
    sampleRM=samplesRM, studyIDSyn=studyID, np=1L,
    spRef=demoKnownSuperPop1KG,
    kList=seq(10,15,1), pcaList=seq(10,15,1), eigenCount=15L)

## The ancestry inference for the synthetic data using
## different K and D values
head(results$matKNN)

## Close Profile GDS file (important)
closefn.gds(gdsProfile)

Description

The function calculates the AUROC of the inferences for specific values of D and K using the inferred ancestry results from the synthetic profiles. The calculations are done on each super-population separately as well as on all the results together.

Usage

computeSyntheticROC(
  matKNN,
  matKNNAncestryColumn,
  pedCall,
  pedCallAncestryColumn,
  listCall = c("EAS", "EUR", "AFR", "AMR", "SAS")
)

Arguments

Value

list containing 3 entries:

• matAUROC.All a data.frame containing the AUROC for all the ancestry results.

• matAUROC.Call a data.frame containing the AUROC information for each super-population.

• listROC.Call a list containing the output from the roc function for each super-population.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Loading demo dataset containing pedigree information for synthetic
## profiles and known ancestry of the profiles used to generate the
## synthetic profiles
data(pedSynthetic)

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

## The inferred ancestry results for the synthetic data using
## values of D=6 and K=5
matKNN <- matKNNSynthetic[matKNNSynthetic$K == 6 & matKNNSynthetic$D == 5, ]

## 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$matAUROC.All
results$matAUROC.Call
results$listROC.Call

Description

The function uses the information for the Reference GDS file and the RDS Sample Description file to create the Profile GDS file. One Profile GDS file is created per profile. One Profile GDS file will be created for each entry present in the listProfiles parameter.

Usage

createStudy2GDS1KG(
  pathGeno = file.path("data", "sampleGeno"),
  filePedRDS = NULL,
  pedStudy = NULL,
  fileNameGDS,
  batch = 1,
  studyDF,
  listProfiles = NULL,
  pathProfileGDS = NULL,
  genoSource = c("snp-pileup", "generic", "VCF"),
  verbose = FALSE
)

Arguments

Value

The function returns 0L when successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Path to the demo 1KG GDS file is located in this package
dataDir <- system.file("extdata/tests", package="RAIDS")
fileGDS <- file.path(dataDir, "ex1_good_small_1KG.gds")

## The data.frame containing the information about the study
## The 3 mandatory columns: "study.id", "study.desc", "study.platform"
## The entries should be strings, not factors (stringsAsFactors=FALSE)
studyDF <- data.frame(study.id = "MYDATA",
                        study.desc = "Description",
                        study.platform = "PLATFORM",
                        stringsAsFactors = FALSE)

## The data.frame containing the information about the samples
## The entries should be strings, not factors (stringsAsFactors=FALSE)
samplePED <- data.frame(Name.ID=c("ex1", "ex2"),
                    Case.ID=c("Patient_h11", "Patient_h12"),
                    Diagnosis=rep("Cancer", 2),
                    Sample.Type=rep("Primary Tumor", 2),
                    Source=rep("Databank B", 2), stringsAsFactors=FALSE)
rownames(samplePED) <- samplePED$Name.ID

## Create the Profile GDS File for samples in 'listSamples' vector
## (in this case, samples "ex1")
## The Profile GDS file is created in the pathProfileGDS directory
result <- createStudy2GDS1KG(pathGeno=dataDir,
            pedStudy=samplePED, fileNameGDS=fileGDS,
            studyDF=studyDF, listProfiles=c("ex1"),
            pathProfileGDS=tempdir(),
            genoSource="snp-pileup",
            verbose=FALSE)

## The function returns OL when successful
result

## The Profile GDS file 'ex1.gds' has been created in the
## specified directory
list.files(tempdir())

## Remove Profile GDS file (created for demo purpose)
unlink(file.path(tempdir(), "ex1.gds"), force=TRUE)

Description

The object is a vector.

Usage

data(demoKnownSuperPop1KG)

Format

The vector containing the know super population ancestry for the demo 1KG reference profiles.

Details

This object can be used to test the computeKNNRefSynthetic and computePoolSyntheticAncestryGr functions.

Value

The vector containing the know super population ancestry for the demo 1KG reference profiles.

See Also

• computeKNNRefSynthetic for running a k-nearest neighbors analysis on a subset of the synthetic data set.

• computePoolSyntheticAncestryGr for running a PCA analysis using 1 synthetic profile from each sub-continental population.

Examples

## Required library
library(gdsfmt)

## Load the demo PCA on the synthetic profiles projected on the
## demo 1KG reference PCA
data(demoPCASyntheticProfiles)

## Load the known ancestry for the demo 1KG reference profiles
data(demoKnownSuperPop1KG)

## Path to the demo Profile GDS file is located in this package
dataDir <- system.file("extdata/demoKNNSynthetic", package="RAIDS")

## Open the Profile GDS file
gdsProfile <- snpgdsOpen(file.path(dataDir, "ex1.gds"))

# The name of the synthetic study
studyID <- "MYDATA.Synthetic"

## Projects synthetic profiles on 1KG PCA
results <- computeKNNRefSynthetic(gdsProfile=gdsProfile,
    listEigenvector=demoPCASyntheticProfiles,
    listCatPop=c("EAS", "EUR", "AFR", "AMR", "SAS"), studyIDSyn=studyID,
    spRef=demoKnownSuperPop1KG)

## The inferred ancestry for the synthetic profiles for different values
## of D and K
head(results$matKNN)

## Close Profile GDS file (important)
closefn.gds(gdsProfile)

Description

The object is a list.

Usage

data(demoPCA1KG)

Format

The list containing the PCA results for a small subset of the reference 1KG dataset. The list contains 2 entries:

• pruned a vector of SNV identifiers specifying selected SNVs for the PCA analysis.

• pca.unrel a snpgdsPCAClass object containing the eigenvalues as generated by snpgdsPCA function.

Details

This object can be used to test the computePCAMultiSynthetic function.

Value

The list containing the PCA results for a small subset of the reference 1KG dataset. The list contains 2 entries:

• pruned a vector of SNV identifiers specifying selected SNVs for the PCA analysis.

• pca.unrel a snpgdsPCAClass object containing the eigenvalues as generated by snpgdsPCA function.

Examples

## Required library
library(gdsfmt)

## Loading demo PCA on subset of 1KG reference dataset
data(demoPCA1KG)

## Path to the demo Profile GDS file is located in this package
dataDir <- system.file("extdata/demoKNNSynthetic", package="RAIDS")

# The name of the synthetic study
studyID <- "MYDATA.Synthetic"

samplesRM <- c("HG00246", "HG00325", "HG00611", "HG01173", "HG02165",
    "HG01112", "HG01615", "HG01968", "HG02658", "HG01850", "HG02013",
    "HG02465", "HG02974", "HG03814", "HG03445", "HG03689", "HG03789",
    "NA12751", "NA19107", "NA18548", "NA19075", "NA19475", "NA19712",
    "NA19731", "NA20528", "NA20908")
names(samplesRM) <- c("GBR", "FIN", "CHS","PUR", "CDX", "CLM", "IBS",
    "PEL", "PJL", "KHV", "ACB", "GWD", "ESN", "BEB", "MSL", "STU", "ITU",
    "CEU", "YRI", "CHB", "JPT", "LWK", "ASW", "MXL", "TSI", "GIH")

## Open the Profile GDS file
gdsProfile <- snpgdsOpen(file.path(dataDir, "ex1.gds"))

## Projects synthetic profiles on demo 1KG PCA
results <- computePCAMultiSynthetic(gdsProfile=gdsProfile,
    listPCA=demoPCA1KG, sampleRef=samplesRM, studyIDSyn=studyID,
    verbose=FALSE)

## The eigenvectors for the synthetic profiles
head(results$eigenvector)

## Close Profile GDS file (important)
closefn.gds(gdsProfile)

Description

The object is a list.

Usage

data(demoPCASyntheticProfiles)

Format

The list containing the PCA result of demo synthetic profiles projected on the demo subset 1KG reference PCA. The list contains 3 entries:

• sample.id a character string representing the unique identifier of the synthetic profiles.

• eigenvector.ref a matrix of numeric containing the eigenvectors for the reference profiles.

• eigenvector a matrix of numeric containing the eigenvectors for the current synthetic profiles projected on the demo PCA 1KG reference profiles.

Details

This object can be used to test the computeKNNRefSynthetic function.

Value

The list containing the PCA result of demo synthetic profiles projected on the demo subset 1KG reference PCA. The list contains 3 entries:

• sample.id a character string representing the unique identifier of the synthetic profiles.

• eigenvector.ref a matrix of numeric containing the eigenvectors for the reference profiles.

• eigenvector a matrix of numeric containing the eigenvectors for the current synthetic profiles projected on the demo PCA 1KG reference profiles.

See Also

• computeKNNRefSynthetic for running a k-nearest neighbors analysis on a subset of the synthetic data set.

Examples

## Required library
library(gdsfmt)

## Load the demo PCA on the synthetic profiles projected on the
## demo 1KG reference PCA
data(demoPCASyntheticProfiles)

## Load the known ancestry for the demo 1KG reference profiles
data(demoKnownSuperPop1KG)

## Path to the demo Profile GDS file is located in this package
dataDir <- system.file("extdata/demoKNNSynthetic", package="RAIDS")

## Open the Profile GDS file
gdsProfile <- snpgdsOpen(file.path(dataDir, "ex1.gds"))

# The name of the synthetic study
studyID <- "MYDATA.Synthetic"

## Projects synthetic profiles on 1KG PCA
results <- computeKNNRefSynthetic(gdsProfile=gdsProfile,
    listEigenvector=demoPCASyntheticProfiles,
    listCatPop=c("EAS", "EUR", "AFR", "AMR", "SAS"), studyIDSyn=studyID,
    spRef=demoKnownSuperPop1KG)

## The inferred ancestry for the synthetic profiles for different values
## of D and K
head(results$matKNN)

## Close Profile GDS file (important)
closefn.gds(gdsProfile)

Description

The object is a data.frame.

Usage

data(demoPedigreeEx1)

Format

The data.frame containing the information about a demo profile called 'ex1'. the data.frame has 5 columns:

• Name.ID a character string representing the unique identifier of the profile.

• Case.ID a character string representing the unique identifier of the case associated to the profile.

• Sample.Type a character string describing the type of profile.

• Diagnosis a character string describing the diagnosis of the profile.

• Source a character string describing the source of the profile.

Details

This object can be used to test the runExomeAncestry function.

Value

The data.frame containing the information about a demo profile called 'ex1'. the data.frame has 5 columns:

• Name.ID a character string representing the unique identifier of the profile.

• Case.ID a character string representing the unique identifier of the case associated to the profile.

• Sample.Type a character string describing the type of profile.

• Diagnosis a character string describing the diagnosis of the profile.

• Source a character string describing the source of the profile.

See Also

• runExomeAncestry for running runs most steps leading to the ancestry inference call on a specific exome profile.

Examples

## Required library for GDS
library(SNPRelate)

## Path to the demo 1KG GDS file is located in this package
dataDir <- system.file("extdata", package="RAIDS")

#################################################################
## Load the information about the profile
#################################################################
data(demoPedigreeEx1)
head(demoPedigreeEx1)

#################################################################
## The 1KG GDS file and the 1KG SNV Annotation GDS file
## need to be located in the same directory
## Note that the 1KG GDS file used for this example is a
## simplified version and CANNOT be used for any real analysis
#################################################################
path1KG <- file.path(dataDir, "tests")

fileReferenceGDS  <- file.path(path1KG, "ex1_good_small_1KG.gds")
fileAnnotGDS <- file.path(path1KG, "ex1_good_small_1KG_Annot.gds")

#################################################################
## The Sample SNP pileup files (one per sample) need
## to be located in the same directory.
#################################################################
pathGeno <- file.path(dataDir, "example", "snpPileup")

#################################################################
## The path where the Profile GDS Files (one per sample)
## will be created need to be specified.
#################################################################
pathProfileGDS <- file.path(tempdir(), "out.tmp")

pathOut <- file.path(tempdir(), "res.out")

#################################################################
## A data frame containing general information about the study
## is also required. The data frame must have
## those 3 columns: "studyID", "study.desc", "study.platform"
#################################################################
studyDF <- data.frame(study.id="MYDATA",
                        study.desc="Description",
                        study.platform="PLATFORM",
                        stringsAsFactors=FALSE)

####################################################################
## Fix seed to ensure reproducible results
####################################################################
set.seed(2043)

gds1KG <- snpgdsOpen(fileReferenceGDS)
dataRef <- select1KGPop(gds1KG, nbProfiles=2L)
closefn.gds(gds1KG)

## Required library for this example to run correctly
if (requireNamespace("GenomeInfoDb", quietly=TRUE) &&
     requireNamespace("BSgenome.Hsapiens.UCSC.hg38", quietly=TRUE)) {

    ## Chromosome length information
    ## chr23 is chrX, chr24 is chrY and chrM is 25
    chrInfo <- GenomeInfoDb::seqlengths(BSgenome.Hsapiens.UCSC.hg38::Hsapiens)[1:25]

    
        runExomeAncestry(pedStudy=demoPedigreeEx1, studyDF=studyDF,
            pathProfileGDS=pathProfileGDS,
            pathGeno=pathGeno, pathOut=pathOut,
            fileReferenceGDS=fileReferenceGDS,
            fileReferenceAnnotGDS=fileAnnotGDS,
            chrInfo=chrInfo, syntheticRefDF=dataRef,
            genoSource="snp-pileup")

        unlink(pathProfileGDS, recursive=TRUE, force=TRUE)
        unlink(pathOut, recursive=TRUE, force=TRUE)
    
}

Description

The function estimates the allelic fraction of the SNVs for a specific profile and add the information to the associated Profile GDS file. The allelic fraction estimation method is adapted to the type of study (DNA or RNA).

Usage

estimateAllelicFraction(
  gdsReference,
  gdsProfile,
  currentProfile,
  studyID,
  chrInfo,
  studyType = c("DNA", "RNA"),
  minCov = 10L,
  minProb = 0.999,
  eProb = 0.001,
  cutOffLOH = -5,
  cutOffHomoScore = -3,
  wAR = 9,
  cutOffAR = 3,
  gdsRefAnnot = NULL,
  blockID = NULL,
  verbose = FALSE
)

Arguments

Details

The chrInfo parameter contains the length of the chromosomes. The length of the chromosomes can be obtain through the seqlengths library.

As example, for hg38 genome:

if (requireNamespace("GenomeInfoDb", quietly=TRUE) &&
     requireNamespace("BSgenome.Hsapiens.UCSC.hg38", quietly=TRUE)) {
     chrInfo <- GenomeInfoDb::seqlengths(BSgenome.Hsapiens.UCSC.hg38::Hsapiens)[1:25]
}

Value

The integer 0L when successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library for GDS
library(gdsfmt)

## Path to the demo 1KG GDS file located in this package
dataDir <- system.file("extdata/tests", package="RAIDS")
fileGDS <- file.path(dataDir, "ex1_good_small_1KG.gds")

## Profile GDS file for one profile
fileProfile <- file.path(tempdir(), "ex1.gds")

## Copy the Profile GDS file demo that has been pruned and annotated
## into current directory
file.copy(file.path(dataDir, "ex1_demo_with_pruning_and_1KG_annot.gds"),
                            fileProfile)

## Open the reference GDS file (demo version)
gds1KG <- snpgdsOpen(fileGDS)

## Profile GDS file for one profile
profileGDS <- openfn.gds(fileProfile, readonly=FALSE)

## Required library for this example to run correctly
if (requireNamespace("GenomeInfoDb", quietly=TRUE) &&
     requireNamespace("BSgenome.Hsapiens.UCSC.hg38", quietly=TRUE)) {

    ## Chromosome length information
    ## chr23 is chrX, chr24 is chrY and chrM is 25
    chrInfo <- GenomeInfoDb::seqlengths(BSgenome.Hsapiens.UCSC.hg38::Hsapiens)[1:25]

    ## Estimate the allelic fraction of the pruned SNVs
    estimateAllelicFraction(gdsReference=gds1KG, gdsProfile=profileGDS,
        currentProfile="ex1", studyID="MYDATA", chrInfo=chrInfo,
        studyType="DNA", minCov=10L, minProb=0.999, eProb=0.001,
        cutOffLOH=-5, cutOffHomoScore=-3, wAR=9, cutOffAR=3,
        gdsRefAnnot=NULL, blockID=NULL)

    ## The allelic fraction is saved in the 'lap' node of Profile GDS file
    ## The 'lap' entry should be present
    profileGDS

    ## Close both GDS files (important)
    closefn.gds(profileGDS)
    closefn.gds(gds1KG)

    ## Remove Profile GDS file (created for demo purpose)
    unlink(fileProfile, force=TRUE)

}

Description

This function generates the GDS file that will contain the information from Reference. The function also add the samples information, the SNP information and the genotyping information into the GDS file.

Usage

generateGDS1KG(
  pathGeno = file.path("data", "sampleGeno"),
  filePedRDS,
  fileSNVIndex,
  fileSNVSelected,
  fileNameGDS,
  listSamples = NULL,
  verbose = FALSE
)

Arguments

Details

More information about GDS file format can be found at the Bioconductor gdsfmt website: https://bioconductor.org/packages/gdsfmt/

Value

The integer 0L when successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Path to the demo pedigree file is located in this package
dataDir <- system.file("extdata", package="RAIDS")

## Path to the CSV genoytype files
pathGeno <- file.path(dataDir, "demoProfileGenotypes")

## The RDS file containing the pedigree information
pedigreeFile <- file.path(dataDir, "PedigreeReferenceDemo.rds")

## The RDS file containing the indexes of the retained SNPs
snpIndexFile <- file.path(dataDir, "listSNPIndexes_Demo.rds")

## The RDS file containing the filtered SNP information
filterSNVFile <- file.path(dataDir, "mapSNVSelected_Demo.rds")

## Temporary Reference GDS file
tempRefGDS <- file.path(tempdir(), "1KG_TEMP.gds")

## Create a temporary Reference GDS file
generateGDS1KG(pathGeno=pathGeno, filePedRDS=pedigreeFile,
            fileSNVIndex=snpIndexFile, fileSNVSelected=filterSNVFile,
            fileNameGDS=tempRefGDS, listSamples=NULL)

## Remove temporary files
unlink(tempRefGDS, force=TRUE)

Description

The function applies a cut-off filter to the SNP information file to retain only the SNP that have a frequency superior or equal to the specified cut-off in at least one super population. The information about the retained SNPs is saved in a RDS format file. A RDS file containing the indexes of the retained SNP is also created.

Usage

generateMapSnvSel(cutOff = 0.01, fileSNV, fileSNPsRDS, fileFREQ)

Arguments

Details

The filtered SNP information RDS file (parameter fileFREQ), contains a data.frame with those columns:

• CHROM a character string representing the chromosome where the SNV is located.

• POS a character string representing the SNV position on the chromosome.

• REF a character string representing the reference DNA base for the SNV.

• ALT a character string representing the alternative DNA base for the SNV.\

• EAS_AF a character string representing the allele frequency of the EAS super population.

• AFR_AF a character string representing the allele frequency of the AFR super population.

• AMR_AF a character string representing the allele frequency of the AMR super population.

• SAS_AF a character string representing the allele frequency of the SAS super population.

Value

The integer 0 when successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Path to the demo pedigree file is located in this package
dataDir <- system.file("extdata", package="RAIDS")

## Demo SNV information file used as input
snvFile <- file.path(dataDir, "matFreqSNV_Demo.txt.bz2")

## Temporary output files
## The first file contains the indexes of the retained SNPs
## The second file contains the filtered SNP information
snpIndexFile <- file.path(tempdir(), "listSNP_TEMP.rds")
filterSNVFile <- file.path(tempdir(), "mapSNVSel_TEMP.rds")

## Create a data.frame containing the information of the retained
## samples (samples with existing genotyping files)
generateMapSnvSel(cutOff=0.01, fileSNV=snvFile,
        fileSNPsRDS=snpIndexFile, fileFREQ=filterSNVFile)

## Remove temporary files
unlink(snpIndexFile, force=TRUE)
unlink(filterSNVFile, force=TRUE)

Description

The function is adding the phase information into the Reference Phase GDS file. The phase information is extracted from a Reference GDS file and is added into a Reference Phase GDS file. An entry called 'phase' is added to the Reference Phase GDS file.

Usage

generatePhase1KG2GDS(
  gdsReference,
  gdsReferencePhase,
  pathGeno,
  fileSNPsRDS,
  verbose = FALSE
)

Arguments

Value

The function returns 0L when successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required package
library(gdsfmt)

## Path to the demo pedigree file is located in this package
dataDir <- system.file("extdata", package="RAIDS")

## Path where the demo genotype CSV files are located
pathGeno <- file.path(dataDir, "demoProfileGenotypes")

## The RDS file containing the pedigree information
pedigreeFile <- file.path(dataDir, "PedigreeReferenceDemo.rds")

## The RDS file containing the indexes of the retained SNPs
snpIndexFile <- file.path(dataDir, "listSNPIndexes_Demo.rds")

## The RDS file containing the filtered SNP information
filterSNVFile <- file.path(dataDir, "mapSNVSelected_Demo.rds")

## Temporary Reference GDS file containing reference information
fileReferenceGDS <- file.path(tempdir(), "1KG_TEMP_02.gds")

## Create a temporary Reference GDS file containing information from 1KG
generateGDS1KG(pathGeno=pathGeno, filePedRDS=pedigreeFile,
        fileSNVIndex=snpIndexFile, fileSNVSelected=filterSNVFile,
        fileNameGDS=fileReferenceGDS, listSamples=NULL)

## Temporary Phase GDS file that will contain the 1KG Phase information
fileRefPhaseGDS <- file.path(tempdir(), "1KG_TEMP_Phase_02.gds")

## Create Reference Phase GDS file
gdsPhase <- createfn.gds(fileRefPhaseGDS)

## Open Reference GDS file
gdsRef <- openfn.gds(fileReferenceGDS)

## Fill temporary Reference Phase GDS file
if (FALSE) {
    generatePhase1KG2GDS(gdsReference=gdsRef,
            gdsReferencePhase=gdsPhase,
            pathGeno=pathGeno, fileSNPsRDS=filterSNVFile,
            verbose=FALSE)
}

## Close Reference Phase information file
closefn.gds(gdsPhase)

## Close Reference information file
closefn.gds(gdsRef)

## Remove temporary files
unlink(fileReferenceGDS, force=TRUE)
unlink(fileRefPhaseGDS, force=TRUE)

Description

The function extract the specified column for the 'sample.ref' node present in the Reference GDS file. The column must be present in the data.frame saved in the 'sample.ref' node. Only the information for the reference profiles is returned. The values represent the known ancestry assignation.

Usage

getRef1KGPop(gdsReference, popName = "superPop")

Arguments

Value

vector of character strings representing the content of the extracted column for the 1KG GDS 'sample.ref' node. The values represent the known ancestry assignation. The profile identifiers are used as names for the vector.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library
library(gdsfmt)

## Path to the demo pedigree file is located in this package
dataDir <- system.file("extdata", package="RAIDS")

## Open existing demo 1K GDS file with "sample.ref" node
nameFileGDS <- file.path(dataDir, "PopulationReferenceDemo.gds")
fileGDS <- snpgdsOpen(nameFileGDS)

## Extract super population information for the 1KG profiles
getRef1KGPop(gdsReference=fileGDS, popName="superPop")

## Close 1K GDS file
closefn.gds(fileGDS)

Description

This function merge all the genotyping files associated to one specific sample into one file. That merged VCF file will be saved in a specified directory and will have the name of the sample. It will also be compressed (bzip). The function will merge the files for all samples present in the input directory.

Usage

groupChr1KGSNV(pathGenoChr, pathOut)

Arguments

Value

The integer 0L when successful or FALSE if not.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Path to the demo vcf files in this package
dataDir <- system.file("extdata", package="RAIDS")
pathGenoTar <- file.path(dataDir, "demoGenoChr", "demoGenoChr.tar")

## Path where the chromosomes files will be located
pathGeno <- file.path(tempdir(), "tempGeno")
dir.create(pathGeno, showWarnings=FALSE)

## Untar the file that contains the VCF files for 3 samples split by
## chromosome (one directory per chromosome)
untar(tarfile=pathGenoTar, exdir=pathGeno)

## Path where the output VCF file will be created is
## the same where the split VCF are (pathGeno)

## The files must not exist
if (!file.exists(file.path(pathGeno, "NA12003.csv.bz2")) &&
        !file.exists(file.path(pathGeno, "NA12004.csv.bz2")) &&
        !file.exists(file.path(pathGeno, "NA12005.csv.bz2"))) {

        ## Return 0 when successful
        ## The files "NA12003.csv.bz2", "NA12004.csv.bz2" and
        ## "NA12005.csv.bz2" should not be present in the current directory
        groupChr1KGSNV(pathGenoChr=pathGeno, pathOut=pathGeno)

        ## Validate that files have been created
        file.exists(file.path(pathGeno, "NA12003.csv.bz2"))
        file.exists(file.path(pathGeno, "NA12004.csv.bz2"))
        file.exists(file.path(pathGeno, "NA12005.csv.bz2"))

}

## Remove temporary directory
unlink(pathGeno, recursive=TRUE, force=TRUE)

Description

The function identify patients that are genetically related in the Reference file. It generates a first RDS file with the list of unrelated patient. It also generates a second RDS file with the kinship coefficient between the patients.

Usage

identifyRelative(gds, maf = 0.05, thresh = 2^(-11/2), fileIBD, filePart)

Arguments

Value

NULL invisibly.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required package
library(gdsfmt)

## Path to the demo pedigree file is located in this package
dataDir <- system.file("extdata", package="RAIDS")

## Open existing demo Reference GDS file
fileGDS <- file.path(dataDir, "PopulationReferenceDemo.gds")
tmpGDS <- snpgdsOpen(fileGDS)

## Temporary output files
## The first RDS file will contain the list of unrelated patients
## The second RDS file will contain the kinship information between patients
patientTmpFile <-  "unrelatedPatients_TEMP.rds"
ibdTmpFile <- "ibd_TEMP.rds"

## Different code depending of the withr package availability
if (requireNamespace("withr", quietly=TRUE)) {

    ## Temporary output files
    ## The first RDS file will contain the list of unrelated patients
    ## The second RDS file will contain the kinship information
    ## between patients
    patientTmpFileLocal <- withr::local_file(patientTmpFile)
    ibdTmpFileLocal <- withr::local_file(ibdTmpFile)

    ## Identify unrelated patients in demo Reference GDS file
    identifyRelative(gds=tmpGDS, maf=0.05, thresh=2^(-11/2),
        fileIBD=ibdTmpFileLocal, filePart=patientTmpFileLocal)

    ## Close demo Reference GDS file
    closefn.gds(tmpGDS)

    ## Remove temporary files
    withr::deferred_run()

} else {

    ## Identify unrelated patients in demo Reference GDS file
    identifyRelative(gds=tmpGDS, maf=0.05, thresh=2^(-11/2),
        fileIBD=ibdTmpFile, filePart=patientTmpFile)

    ## Close demo Reference GDS file
    closefn.gds(tmpGDS)

    ## Remove temporary files
    unlink(patientTmpFile, force=TRUE)
    unlink(ibdTmpFile, force=TRUE)
}

Description

The object is a data.frame with 4 columns.

Usage

data(matKNNSynthetic)

Format

The data.frame containing the information about the synthetic profiles. The data.frame contains 4 columns:

• sample.id a character string representing the unique synthetic profile identifier.

• D a numeric representing the number of dimensions used to infer the ancestry of the synthetic profile.

• K a numeric representing the number of neighbors used to infer the ancestry of the synthetic profile.

• SuperPop a character string representing the inferred ancestry of the synthetic profile for the specific D and K values.

Details

This dataset can be used to test the computeSyntheticROC function.

Value

The data.frame containing the information about the synthetic profiles. The data.frame contains 4 columns:

• sample.id a character string representing the unique synthetic profile identifier.

• D a numeric representing the number of dimensions used to infer the ancestry of the synthetic profile.

• K a numeric representing the number of neighbors used to infer the ancestry of the synthetic profile.

• SuperPop a character string representing the inferred ancestry of the synthetic profile for the specific D and K values.

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$matAUROC.All
results$matAUROC.Call
results$listROC.Call

Description

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

Usage

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.

Details

This dataset can be used to test the computeSyntheticROC function.

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.

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$matAUROC.All
results$matAUROC.Call
results$listROC.Call

Description

Using the pedigree file from Reference, this function extracts needed information and formats it into a data.frame so in can be used in following steps of the ancestry inference process. The function also requires that the genotyping files associated to each sample be available in a specified directory.

Usage

prepPed1KG(filePed, pathGeno = file.path("data", "sampleGeno"), batch = 0L)

Arguments

Value

a data.frame containing the needed pedigree information from Reference. The data.frame contains those columns:

• sample.ida character string representing the profile unique ID.

• Name.IDa character string representing the profile name.

• sexa character string representing the sex of the profile.

• pop.groupa character string representing the sub-continental ancestry of the profile.

• superPop a character string representing the continental ancestry of the profile.

• superPop a integer representing the batch of the profile.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Path to the demo pedigree file is located in this package
dataDir <- system.file("extdata", package="RAIDS")

## Path where the demo genotype CSV files are located
pathGeno <- file.path(dataDir, "demoProfileGenotypes")

## Demo pedigree file
pedDemoFile <- file.path(dataDir, "PedigreeDemo.ped")

## Create a data.frame containing the information of the retained
## samples (samples with existing genotyping files)
prepPed1KG(filePed=pedDemoFile, pathGeno=pathGeno, batch=0L)

Description

This function add entries related to synthetic profiles into a Profile GDS file. The entries are related to two types of information: the synthetic study and the synthetic profiles.

The study information is appended to the Profile GDS file "study.list" node. The "study.platform" entry is always set to 'Synthetic'.

The profile information, for all selected synthetic profiles, is appended to the Profile GDS file "study.annot" node. Both the "Source" and the "Sample.Type" entries are always set to 'Synthetic'.

The synthetic profiles are assigned unique names by combining: prefix.data.id.profile.listSampleRef.simulation number(1 to nbSim)

Usage

prepSynthetic(
  fileProfileGDS,
  listSampleRef,
  profileID,
  studyDF,
  nbSim = 1L,
  prefix = "",
  verbose = FALSE
)

Arguments

Value

0L when successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library
library(gdsfmt)

## Path to the demo 1KG GDS file is located in this package
dataDir <- system.file("extdata/tests", package="RAIDS")

## Temporary Profile GDS file
fileNameGDS <- file.path(tempdir(), "ex1.gds")

## Copy the Profile GDS file demo that has been pruned and annotated
file.copy(file.path(dataDir, "ex1_demo_with_pruning_and_1KG_annot.gds"),
                 fileNameGDS)

## Information about the synthetic data set
syntheticStudyDF <- data.frame(study.id="MYDATA.Synthetic",
        study.desc="MYDATA synthetic data", study.platform="PLATFORM",
        stringsAsFactors=FALSE)

## Add information related to the synthetic profiles into the Profile GDS
prepSynthetic(fileProfileGDS=fileNameGDS,
        listSampleRef=c("HG00243", "HG00150"), profileID="ex1",
        studyDF=syntheticStudyDF, nbSim=1L, prefix="synthetic",
        verbose=FALSE)

## Open Profile GDS file
profileGDS <- openfn.gds(fileNameGDS)

## The synthetic profiles should be added in the 'study.annot' entry
tail(read.gdsn(index.gdsn(profileGDS, "study.annot")))

## The synthetic study information should be added to
## the 'study.list' entry
tail(read.gdsn(index.gdsn(profileGDS, "study.list")))

## Close GDS file (important)
closefn.gds(profileGDS)

## Remove Profile GDS file (created for demo purpose)
unlink(fileNameGDS, force=TRUE)

Description

This function computes the list of pruned SNVs for a specific profile. When a group of SNVs are in linkage disequilibrium, only one SNV from that group is retained. The linkage disequilibrium is calculated with the snpgdsLDpruning() function. The initial list of SNVs that are passed to the snpgdsLDpruning() function can be specified by the user.

Usage

pruningSample(
  gdsReference,
  method = c("corr", "r", "dprime", "composite"),
  currentProfile,
  studyID,
  listSNP = NULL,
  slideWindowMaxBP = 500000L,
  thresholdLD = sqrt(0.1),
  np = 1L,
  verbose = FALSE,
  chr = NULL,
  superPopMinAF = NULL,
  keepPrunedGDS = TRUE,
  pathProfileGDS = NULL,
  keepFile = FALSE,
  pathPrunedGDS = ".",
  outPrefix = "pruned"
)

Arguments

Value

The function returns 0L when successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library for GDS
library(gdsfmt)

## Path to the demo Reference GDS file is located in this package
dataDir <- system.file("extdata/tests", package="RAIDS")
fileGDS <- file.path(dataDir, "ex1_good_small_1KG.gds")

## The data.frame containing the information about the study
## The 3 mandatory columns: "study.id", "study.desc", "study.platform"
## The entries should be strings, not factors (stringsAsFactors=FALSE)
studyDF <- data.frame(study.id = "MYDATA",
                        study.desc = "Description",
                        study.platform = "PLATFORM",
                        stringsAsFactors = FALSE)

## The data.frame containing the information about the samples
## The entries should be strings, not factors (stringsAsFactors=FALSE)
samplePED <- data.frame(Name.ID = c("ex1", "ex2"),
                    Case.ID = c("Patient_h11", "Patient_h12"),
                    Diagnosis = rep("Cancer", 2),
                    Sample.Type = rep("Primary Tumor", 2),
                    Source = rep("Databank B", 2), stringsAsFactors = FALSE)
rownames(samplePED) <- samplePED$Name.ID

## Temporary Profile GDS file
profileFile <- file.path(tempdir(), "ex1.gds")

## Copy the Profile GDS file demo that has not been pruned yet
file.copy(file.path(dataDir, "ex1_demo.gds"), profileFile)

## Open 1KG file
gds1KG <- snpgdsOpen(fileGDS)

## Compute the list of pruned SNVs for a specific profile 'ex1'
## and save it in the Profile GDS file 'ex1.gds'
pruningSample(gdsReference=gds1KG, currentProfile=c("ex1"),
              studyID = studyDF$study.id, pathProfileGDS=tempdir())

## Close the Reference GDS file (important)
closefn.gds(gds1KG)

## Check content of Profile GDS file
## The 'pruned.study' entry should be present
content <- openfn.gds(profileFile)
content

## Close the Profile GDS file (important)
closefn.gds(content)

## Remove Profile GDS file (created for demo purpose)
unlink(profileFile, force=TRUE)

Description

This function runs most steps leading to the ancestry inference call on a specific exome profile. First, the function creates the Profile GDS file for the specific profile using the information from a RDS Sample description file and the Population reference GDS file.

Usage

runExomeAncestry(
  pedStudy,
  studyDF,
  pathProfileGDS,
  pathGeno,
  pathOut,
  fileReferenceGDS,
  fileReferenceAnnotGDS,
  chrInfo,
  syntheticRefDF,
  genoSource = c("snp-pileup", "generic", "VCF"),
  np = 1L,
  verbose = FALSE
)

Arguments

Details

The runExomeAncestry() function generates 3 types of files in the OUTPUT directory.

• Ancestry InferenceThe ancestry inference CSV file (".Ancestry.csv" file)

• Inference InformatonThe inference information RDS file (".infoCall.rds" file)

• Synthetic InformationThe parameter information RDS files from the synthetic inference ("KNN.synt.*.rds" files in a sub-directory)

In addition, a sub-directory (named using the profile ID) is also created.

Value

The integer 0L when successful. See details section for more information about the generated output files.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

References

Galinsky KJ, Bhatia G, Loh PR, Georgiev S, Mukherjee S, Patterson NJ, Price AL. Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia. Am J Hum Genet. 2016 Mar 3;98(3):456-72. doi: 10.1016/j.ajhg.2015.12.022. Epub 2016 Feb 25.

Examples

## Required library for GDS
library(SNPRelate)

## Path to the demo 1KG GDS file is located in this package
dataDir <- system.file("extdata", package="RAIDS")

#################################################################
## Load the information about the profile
#################################################################
data(demoPedigreeEx1)
head(demoPedigreeEx1)

#################################################################
## The 1KG GDS file and the 1KG SNV Annotation GDS file
## need to be located in the same directory
## Note that the 1KG GDS file used for this example is a
## simplified version and CANNOT be used for any real analysis
#################################################################
path1KG <- file.path(dataDir, "tests")

fileReferenceGDS  <- file.path(path1KG, "ex1_good_small_1KG.gds")
fileAnnotGDS <- file.path(path1KG, "ex1_good_small_1KG_Annot.gds")

#################################################################
## The Sample SNP pileup files (one per sample) need
## to be located in the same directory.
#################################################################
pathGeno <- file.path(dataDir, "example", "snpPileup")

#################################################################
## The path where the Profile GDS Files (one per sample)
## will be created need to be specified.
#################################################################
pathProfileGDS <- file.path(tempdir(), "out.tmp")

pathOut <- file.path(tempdir(), "res.out")

#################################################################
## A data frame containing general information about the study
## is also required. The data frame must have
## those 3 columns: "studyID", "study.desc", "study.platform"
#################################################################
studyDF <- data.frame(study.id="MYDATA",
                        study.desc="Description",
                        study.platform="PLATFORM",
                        stringsAsFactors=FALSE)

####################################################################
## Fix seed to ensure reproducible results
####################################################################
set.seed(3043)

gds1KG <- snpgdsOpen(fileReferenceGDS)
dataRef <- select1KGPop(gds1KG, nbProfiles=2L)
closefn.gds(gds1KG)

## Required library for this example to run correctly
if (requireNamespace("GenomeInfoDb", quietly=TRUE) &&
     requireNamespace("BSgenome.Hsapiens.UCSC.hg38", quietly=TRUE)) {

    ## Chromosome length information
    ## chr23 is chrX, chr24 is chrY and chrM is 25
    chrInfo <- GenomeInfoDb::seqlengths(BSgenome.Hsapiens.UCSC.hg38::Hsapiens)[1:25]

    

        runExomeAncestry(pedStudy=demoPedigreeEx1, studyDF=studyDF,
            pathProfileGDS=pathProfileGDS,
            pathGeno=pathGeno,
            pathOut=pathOut,
            fileReferenceGDS=fileReferenceGDS,
            fileReferenceAnnotGDS=fileAnnotGDS,
            chrInfo=chrInfo,
            syntheticRefDF=dataRef,
            genoSource="snp-pileup")

        unlink(pathProfileGDS, recursive=TRUE, force=TRUE)
        unlink(pathOut, recursive=TRUE, force=TRUE)
    
}

Description

This function runs most steps leading to the ancestry inference call on a specific RNA profile. First, the function creates the Profile GDS file for the specific profile using the information from a RDS Sample description file and the Population Reference GDS file.

Usage

runRNAAncestry(
  pedStudy,
  studyDF,
  pathProfileGDS,
  pathGeno,
  pathOut,
  fileReferenceGDS,
  fileReferenceAnnotGDS,
  chrInfo,
  syntheticRefDF,
  genoSource = c("snp-pileup", "generic", "VCF"),
  np = 1L,
  blockTypeID,
  verbose = FALSE
)

Arguments

Details

The runExomeAncestry() function generates 3 types of files in the OUTPUT directory.

• Ancestry InferenceThe ancestry inference CSV file (".Ancestry.csv" file)

• Inference InformatonThe inference information RDS file (".infoCall.rds" file)

• Synthetic InformationThe parameter information RDS files from the synthetic inference ("KNN.synt.*.rds" files in a sub-directory)

In addition, a sub-directory (named using the profile ID) is also created.

Value

The integer 0L when successful. See details section for more information about the generated output files.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

References

Galinsky KJ, Bhatia G, Loh PR, Georgiev S, Mukherjee S, Patterson NJ, Price AL. Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia. Am J Hum Genet. 2016 Mar 3;98(3):456-72. doi: 10.1016/j.ajhg.2015.12.022. Epub 2016 Feb 25.

Examples

## Required library for GDS
library(SNPRelate)

## Path to the demo 1KG GDS file is located in this package
dataDir <- system.file("extdata", package="RAIDS")

#################################################################
## Load the information about the profile
#################################################################
data(demoPedigreeEx1)
head(demoPedigreeEx1)

#################################################################
## The 1KG GDS file and the 1KG SNV Annotation GDS file
## need to be located in the same directory
## Note that the 1KG GDS file used for this example is a
## simplified version and CANNOT be used for any real analysis
#################################################################
path1KG <- file.path(dataDir, "tests")

fileReferenceGDS <- file.path(path1KG, "ex1_good_small_1KG.gds")
fileAnnotGDS <- file.path(path1KG, "ex1_good_small_1KG_Annot.gds")

#################################################################
## The Sample SNP pileup files (one per sample) need
## to be located in the same directory.
#################################################################
pathGeno <- file.path(dataDir, "example", "snpPileup")

#################################################################
## The path where the Profile GDS Files (one per sample)
## will be created need to be specified.
#################################################################
pathProfileGDS <- file.path(tempdir(), "out.tmp")

pathOut <- file.path(tempdir(), "res.out")

#################################################################
## A data frame containing general information about the study
## is also required. The data frame must have
## those 3 columns: "studyID", "study.desc", "study.platform"
#################################################################
studyDF <- data.frame(study.id="MYDATA",
                        study.desc="Description",
                        study.platform="PLATFORM",
                        stringsAsFactors=FALSE)

####################################################################
## Fix seed to ensure reproducible results
####################################################################
set.seed(3043)

gds1KG <- snpgdsOpen(fileReferenceGDS)
dataRef <- select1KGPop(gds1KG, nbProfiles=2L)
closefn.gds(gds1KG)

## Required library for this example to run correctly
if (requireNamespace("GenomeInfoDb", quietly=TRUE) &&
     requireNamespace("BSgenome.Hsapiens.UCSC.hg38", quietly=TRUE)) {

    ## Chromosome length information
    ## chr23 is chrX, chr24 is chrY and chrM is 25
    chrInfo <- GenomeInfoDb::seqlengths(BSgenome.Hsapiens.UCSC.hg38::Hsapiens)[1:25]

    

        runRNAAncestry(pedStudy=demoPedigreeEx1, studyDF=studyDF,
            pathProfileGDS=pathProfileGDS,
            pathGeno=pathGeno,
            pathOut=pathOut,
            fileReferenceGDS=fileReferenceGDS,
            fileReferenceAnnotGDS=fileAnnotGDS,
            chrInfo=chrInfo,
            syntheticRefDF=dataRef,
            blockTypeID="GeneS.Ensembl.Hsapiens.v86",
            genoSource="snp-pileup")

        unlink(pathProfileGDS, recursive=TRUE, force=TRUE)
        unlink(pathOut, recursive=TRUE, force=TRUE)

    
}

Description

The function randomly selects a fixed number of reference for each subcontinental population present in the 1KG GDS file. When a subcontinental population has less samples than the fixed number, all samples from the subcontinental population are selected.

Usage

select1KGPop(gdsReference, nbProfiles)

Arguments

Value

a data.frame containing those columns:

• sample.id a character string representing the sample identifier.

• pop.group a character string representing the subcontinental population assigned to the sample.

• superPop a character string representing the super-population assigned to the sample.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library
library(gdsfmt)

## The number of samples needed by subcontinental population
## The number is small for demonstration purpose
nbProfiles <- 5L

## Open 1KG GDS Demo file
## This file only one superpopulation (for demonstration purpose)
dataDir <- system.file("extdata", package="RAIDS")
fileGDS <- file.path(dataDir, "PopulationReferenceDemo.gds")
gdsFileOpen <- openfn.gds(fileGDS, readonly=TRUE)

## Extract a selected number of random samples
## for each subcontinental population
## In the 1KG GDS Demo file, there is one subcontinental population
dataR <- select1KGPop(gdsReference=gdsFileOpen, nbProfiles=nbProfiles)

## Close the 1KG GDS Demo file (important)
closefn.gds(gdsFileOpen)

Description

The object is a data.frame with 17 columns.

Usage

data(snpPositionDemo)

Format

The data.frame containing the information about the synthetic profiles. The data.frame contains 4 columns:

• cnt.tot a integer representing the number of reads at the SNV position.

• cnt.ref a integer representing the number of reads corresponding to the reference at the SNV position.

• cnt.alt a integer representing the number of reads different than the reference at the SNV position.

• snp.pos a integer representing the position of the SNV on the chromosome.

• snp.chr a integer representing the chromosome on which the SNV is located.

• normal.geno a integer representing the genotype (0=wild-type reference; 1=heterozygote; 2=homozygote alternative; 3=unkown).

• pruned a logical indicated if the SNV is pruned.

• snp.index a integer representing the index of the SNV in the reference SNV GDS file.

• keep a logical indicated if the genotype exists for the SNV.

• hetero a logical indicated if the SNV is heterozygote.

• homo a logical indicated if the SNV is homozygote.

• block.id a integer representing the block identifier associated to the current SNV.

• phase a integer representing the block identifier associated to the current SNV.

• lap a numeric representing the lower allelic fraction.

• LOH a integer indicating if the SNV is in an LOH region (0=not LOH, 1=in LOH).

• imbAR a integer indicating if the SNV is in an imbalanced region (-1=not classified as imbalanced or LOH, 0=in LOH; 1=tested positive for imbalance in at least 1 window).

• freq a numeric representing the frequency of the variant in the the reference.

Details

This dataset can be used to test the calcAFMLRNA and tableBlockAF internal functions.

Value

The data.frame containing the information about the synthetic profiles. The data.frame contains 4 columns:

• cnt.tot a integer representing the number of reads at the SNV position.

• cnt.ref a integer representing the number of reads corresponding to the reference at the SNV position.

• cnt.alt a integer representing the number of reads different than the reference at the SNV position.

• snp.pos a integer representing the position of the SNV on the chromosome.

• snp.chr a integer representing the chromosome on which the SNV is located.

• normal.geno a integer representing the genotype (0=wild-type reference; 1=heterozygote; 2=homozygote alternative; 3=unkown).

• pruned a logical indicated if the SNV is pruned.

• snp.index a integer representing the index of the SNV in the reference SNV GDS file.

• keep a logical indicated if the genotype exists for the SNV.

• hetero a logical indicated if the SNV is heterozygote.

• homo a logical indicated if the SNV is homozygote.

• block.id a integer representing the block identifier associated to the current SNV.

• phase a integer representing the block identifier associated to the current SNV.

• lap a numeric representing the lower allelic fraction.

• LOH a integer indicating if the SNV is in an LOH region (0=not LOH, 1=in LOH).

• imbAR a integer indicating if the SNV is in an imbalanced region (-1=not classified as imbalanced or LOH, 0=in LOH; 1=tested positive for imbalance in at least 1 window).

• freq a numeric representing the frequency of the variant in the the reference.

Examples

## Loading demo dataset containing SNV information
data(snpPositionDemo)

## Only use a subset of heterozygote SNVs related to one block
subset <- snpPositionDemo[which(snpPositionDemo$block.id == 2750 &
                  snpPositionDemo$hetero), c("cnt.ref", "cnt.alt", "phase")]

## Compute the log likelihood ratio based on the coverage of
## each allele in a specific block
result <- RAIDS:::calcAFMLRNA(subset)
head(result)

Description

This function extract the SNPs that pass a frequency cut-off in at least one super population from a GDS SNP information file and save the retained SNP information into a VCF file.

Usage

snvListVCF(gdsReference, fileOut, offset = 0L, freqCutoff = NULL)

Arguments

Value

The integer 0L when successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library
library(gdsfmt)

## Path to the demo pedigree file is located in this package
dataDir <- system.file("extdata", package="RAIDS")

## Demo 1KG Reference GDS file
fileGDS <- openfn.gds(file.path(dataDir,
                    "PopulationReferenceDemo.gds"))

## Output VCF file that will be created (temporary)
vcfFile <- file.path(tempdir(), "Demo_TMP_01.vcf")

## Create a VCF file with the SNV dataset present in the GDS file
## No cutoff on frequency, so all SNVs are saved
snvListVCF(gdsReference=fileGDS, fileOut=vcfFile, offset=0L,
                    freqCutoff=NULL)

## Close GDS file (IMPORTANT)
closefn.gds(fileGDS)

## Remove temporary VCF file
unlink(vcfFile, force=TRUE)

Description

The function groups the samples per subcontinental population and generates a matrix containing the sample identifiers and where each column is a subcontinental population.

Usage

splitSelectByPop(dataRef)

Arguments

Value

a matrix containing the sample identifiers and where each column is the name of a subcontinental population. The number of row corresponds to the number of samples for each subcontinental population.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## A data.frame containing samples from 2 subcontinental populations
demo <- data.frame(sample.id=c("SampleA", "SampleB", "SampleC", "SampleD"),
    pop.group=c("TSI", "TSI", "YRI", "YRI"),
    superPop=c("EUR", "EUR", "AFR", "AFR"))

## Generate a matrix populated with the sample identifiers and where
## each row is a subcontinental population
splitSelectByPop(dataRef=demo)

Description

The functions uses one cancer profile in combination with one 1KG reference profile to generate an synthetic profile that is saved in the Profile GDS file.

When more than one 1KG reference profiles are specified, the function recursively generates synthetic profiles for each cancer profile + 1KG reference profile combination.

The number of synthetic profiles generated by combination is specified by the number of simulation requested.

Usage

syntheticGeno(
  gdsReference,
  gdsRefAnnot,
  fileProfileGDS,
  profileID,
  listSampleRef,
  nbSim = 1L,
  prefix = "",
  pRecomb = 0.01,
  minProb = 0.999,
  seqError = 0.001
)

Arguments

Value

The integer OL when the function is successful.

Author(s)

Pascal Belleau, Astrid Deschênes and Alexander Krasnitz

Examples

## Required library
library(gdsfmt)

## Path to the demo 1KG GDS file is located in this package
dataDir <- system.file("extdata/tests", package="RAIDS")

## Profile GDS file (temporary)
fileNameGDS <- file.path(tempdir(), "ex1.gds")

## Copy the Profile GDS file demo that has been pruned and annotated
file.copy(file.path(dataDir, "ex1_demo_with_pruning_and_1KG_annot.gds"),
                 fileNameGDS)

## Information about the synthetic data set
syntheticStudyDF <- data.frame(study.id="MYDATA.Synthetic",
        study.desc="MYDATA synthetic data", study.platform="PLATFORM",
        stringsAsFactors=FALSE)

## Add information related to the synthetic profiles into the Profile GDS
prepSynthetic(fileProfileGDS=fileNameGDS,
        listSampleRef=c("HG00243", "HG00150"), profileID="ex1",
        studyDF=syntheticStudyDF, nbSim=1L, prefix="synthTest",
        verbose=FALSE)

## The 1KG files
gds1KG <- snpgdsOpen(file.path(dataDir,
                            "ex1_good_small_1KG.gds"))
gds1KGAnnot <- openfn.gds(file.path(dataDir,
                            "ex1_good_small_1KG_Annot.gds"))

## Generate the synthetic profiles and add them into the Profile GDS
syntheticGeno(gdsReference=gds1KG, gdsRefAnnot=gds1KGAnnot,
        fileProfileGDS=fileNameGDS, profileID="ex1",
        listSampleRef=c("HG00243", "HG00150"), nbSim=1,
        prefix="synthTest",
        pRecomb=0.01, minProb=0.999, seqError=0.001)

## Open Profile GDS file
profileGDS <- openfn.gds(fileNameGDS)

tail(read.gdsn(index.gdsn(profileGDS, "sample.id")))

## Close GDS files (important)
closefn.gds(profileGDS)
closefn.gds(gds1KG)
closefn.gds(gds1KGAnnot)

## Remove Profile GDS file (created for demo purpose)
unlink(fileNameGDS, force=TRUE)