doppelgangR: doppelgangR

In waldronlab/doppelgangR: Identify likely duplicate samples from genomic or meta-data

doppelgangR

Description

Identify samples with suspiciously high correlations and phenotype similarities

Usage

doppelgangR(
  esets,
  separator = ":",
  corFinder.args = list(separator = separator, use.ComBat = TRUE, method = "pearson"),
  phenoFinder.args = list(separator = separator, vectorDistFun = vectorWeightedDist),
  outlierFinder.expr.args = list(bonf.prob = 0.5, transFun = atanh, tail = "upper"),
  outlierFinder.pheno.args = list(normal.upper.thresh = 0.99, bonf.prob = NULL, tail =
    "upper"),
  smokingGunFinder.args = list(transFun = I),
  impute.knn.args = list(k = 10, rowmax = 0.5, colmax = 0.8, maxp = 1500, rng.seed =
    362436069),
  manual.smokingguns = NULL,
  automatic.smokingguns = FALSE,
  within.datasets.only = FALSE,
  intermediate.pruning = FALSE,
  cache.dir = "cache",
  BPPARAM = bpparam(),
  verbose = TRUE
)

Arguments

esets	a list of ExpressionSets, containing the numeric and phenotypic data to be analyzed.
separator	a delimitor to use between dataset names and sample names
corFinder.args	a list of arguments to be passed to the corFinder function.
phenoFinder.args	a list of arguments to be passed to the phenoFinder function. If NULL, samples with similar phenotypes will not be searched for.
outlierFinder.expr.args	a list of arguments to be passed to outlierFinder when called for expression data
outlierFinder.pheno.args	a list of arguments to be passed to outlierFinder when called for phenotype data
smokingGunFinder.args	a list of arguments to be passed to smokingGunFinder
impute.knn.args	a list of arguments to be passed to impute::impute.knn. Set to NULL to do no knn imputation.
manual.smokingguns	a character vector of phenoData columns that, if identical, will be considered evidence of duplication
automatic.smokingguns	automatically look for "smoking guns." If TRUE, look for phenotype variables that are unique to each patient in dataset 1, also unique to each patient in dataset 2, but contain exact matches between datasets 1 and 2.
within.datasets.only	If TRUE, only search within each dataset for doppelgangers.
intermediate.pruning	The default setting FALSE will result in output with no missing values, but uses extra memory because all results from the expression, phenotype, and smoking gun doppelganger searches must be saved until the end. Setting this to TRUE will save memory for very large searches, but distance metrics will only be available if that value was identified as a doppelganger (for example, phenotype doppelgangers will have missing values for the expression and smoking gun similarity).
cache.dir	The name of a directory in which to cache or look up results to save re-calculating correlations. Set to NULL for no caching.
BPPARAM	Argument for BiocParallel::bplapply(), by default will use all cores of a multi-core machine
verbose	Print progress information

Value

Returns an object of S4-class "DoppelGang"

Author(s)

Levi Waldron, Markus Riester, Marcel Ramos

See Also

DoppelGang-class BiocParallelParam-class

Examples

example("phenoFinder")

results2 <- doppelgangR(esets2, cache.dir = NULL)
results2
plot(results2)
summary(results2)

## Set phenoFinder.args=NULL to ignore similar phenotypes, and
## turn off ComBat batch correction:

## Not run: 
results2 <- doppelgangR(testesets,
corFinder.args=list(use.ComBat=FALSE), phenoFinder.args=NULL,
    cache.dir=NULL)
summary(results2)

library(curatedOvarianData)
data(GSE32062.GPL6480_eset)
data(GSE32063_eset)
data(GSE12470_eset)
data(GSE17260_eset)

testesets <- list(JapaneseA = GSE32062.GPL6480_eset,
    JapaneseB = GSE32063_eset,
    Yoshihara2009 = GSE12470_eset,
    Yoshihara2010 = GSE17260_eset)

## standardize the sample ids to improve matching
## based on clinical annotation

testesets <- lapply(testesets, function(X) {
  X$alt_sample_name <-
    paste(X$sample_type, gsub("[^0-9]", "", X$alt_sample_name), sep = "_")
  pData(X) <-
    pData(X)[,!grepl("uncurated_author_metadata", colnames(pData(X)))]
  X[, 1:20]  ##speed computations
})

(results1 <- doppelgangR(testesets, cache.dir = NULL))
plot(results1)
summary(results1)


## End(Not run)

waldronlab/doppelgangR documentation built on Nov. 2, 2024, 11:52 p.m.