Run most steps leading to the ancestry inference call on a specific RNA profile

Source: R/processStudy.R

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.

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

Arguments

pedStudy

a data.frame with those mandatory columns: "Name.ID", "Case.ID", "Sample.Type", "Diagnosis", "Source". All columns must be in character strings (no factor). The data.frame must contain the information for all the samples passed in the listSamples parameter. Only filePedRDS or pedStudy can be defined.

studyDF

a data.frame containing the information about the study associated to the analysed sample(s). The data.frame must have those 3 columns: "study.id", "study.desc", "study.platform". All columns must be in character strings (no factor).

pathProfileGDS

a character string representing the path to the directory where the GDS Profile files will be created. Default: NULL.

pathGeno

a character string representing the path to the directory containing the VCF output of SNP-pileup for each sample. The SNP-pileup files must be compressed (gz files) and have the name identifiers of the samples. A sample with "Name.ID" identifier would have an associated file called if genoSource is "VCF", then "Name.ID.vcf.gz", if genoSource is "generic", then "Name.ID.generic.txt.gz" if genoSource is "snp-pileup", then "Name.ID.txt.gz".

pathOut

a character string representing the path to the directory where the output files are created.

fileReferenceGDS

a character string representing the file name of the Population Reference GDS file. The file must exist.

fileReferenceAnnotGDS

a character string representing the file name of the Population Reference GDS Annotation file. The file must exist.

chrInfo

a vector of positive integer values representing the length of the chromosomes. See 'details' section.

syntheticRefDF

a data.frame containing a subset of reference profiles for each sub-population present in the Reference GDS file. The data.frame must have 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.

genoSource

a character string with two possible values: 'snp-pileup', 'generic' or 'VCF'. It specifies if the genotype files are generated by snp-pileup (Facets) or are a generic format CSV file with at least those columns: 'Chromosome', 'Position', 'Ref', 'Alt', 'Count', 'File1R' and 'File1A'. The 'Count' is the depth at the specified position; 'FileR' is the depth of the reference allele and 'File1A' is the depth of the specific alternative allele. Finally the file can be a VCF file with at least those genotype fields: GT, AD, DP.

np

a single positive integer specifying the number of threads to be used. Default: 1L.

blockTypeID

a character string corresponding to the block type used to extract the block identifiers. The block type must be present in the GDS Reference Annotation file.

verbose

a logical indicating if messages should be printed to show how the different steps in the function. Default: FALSE.

Value

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

Details

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

Ancestry Inference

The ancestry inference CSV file (".Ancestry.csv" file)

Inference Informaton

The inference information RDS file (".infoCall.rds" file)

Synthetic Information

The 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.

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.

Author

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", package="RAIDS")

#################################################################
## Load the information about the profile
#################################################################
data(demoPedigreeEx1)
head(demoPedigreeEx1)
#>     Name.ID Case.ID   Sample.Type Diagnosis     Source
#> ex1     ex1     ex1 Primary Tumor    Cancer Databank B

#################################################################
## 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]

    # \donttest{

        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)

    # }
}