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Last data update: 2014.03.03

R: doppelgangR

doppelgangR

R Documentation

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". See ?DoppelGang-class.

Author(s)

Levi Waldron, Markus Riester, Marcel Ramos

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:
##    results2 <- doppelgangR(testesets, corFinder.args=list(use.ComBat=FALSE), phenoFinder.args=NULL, cache.dir=NULL)
##    summary(results2)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(doppelgangR)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: BiocParallel
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/doppelgangR/doppelgangR.Rd_%03d_medium.png", width=480, height=480)
> ### Name: doppelgangR
> ### Title: doppelgangR
> ### Aliases: doppelgangR
> 
> ### ** Examples
> 
> example("phenoFinder")

phnFnd> library(curatedOvarianData)
Loading required package: affy

phnFnd> data(GSE32063_eset)

phnFnd> data(GSE17260_eset)

phnFnd> esets2 <- list(JapaneseB=GSE32063_eset,
phnFnd+                 Yoshihara2010=GSE17260_eset)

phnFnd> ## standardize the sample ids to improve matching based on clinical annotation
phnFnd> esets2 <- lapply(esets2, function(X){
phnFnd+     X$alt_sample_name <- paste(X$sample_type, gsub("[^0-9]", "", X$alt_sample_name), sep="_")
phnFnd+ 
phnFnd+ ## Removal of columns that cannot possibly match also helps duplicated patients to stand out
phnFnd+     pData(X) <- pData(X)[, !grepl("uncurated_author_metadata", colnames(pData(X)))]
phnFnd+     X <- X[, 1:20]  ##speed computations
phnFnd+     return(X) })

phnFnd> ## See first six samples in both rows and columns
phnFnd> phenoFinder(esets2)[1:6, 1:6]
          GSM432220 GSM432221 GSM432222 GSM432223 GSM432224  GSM432225
GSM795125 0.2351904 0.1014047 0.3525417 0.7274151 0.2189890 0.27397077
GSM795126 0.5404524 0.2588727 0.4083015 0.4079720 0.2927870 0.74123368
GSM795127 0.3791279 0.5008562 0.4983502 0.4981226 0.6385506 0.04416984
GSM795128 0.2351904 0.1014047 0.3525417 0.3523760 0.2189890 0.27397077
GSM795129 0.1076309 0.2395470 0.2190910 0.2189890 0.3643260 0.16030839
GSM795130 0.2603947 0.1344290 0.1077761 0.1076793 0.2489234 0.29544860
> 
> results2 <- doppelgangR(esets2, cache.dir = NULL)
Working on datasets JapaneseB and JapaneseB
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets Yoshihara2010 and Yoshihara2010
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets JapaneseB and Yoshihara2010
Calculating correlations...
Found 2 batches
Adjusting for 0 covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> results2
S4 object of class: DoppelGang 
Number of potential doppelgangers: 6 :  6 expression,  2 phenotype,  0 smoking gun. 
 
 Use summary(object) to obtain a data.frame of potential doppelgangrs. 
 
 > plot(results2)
> summary(results2)
                  sample1                 sample2 expr.similarity expr.doppel
1 Yoshihara2010:GSM432230 Yoshihara2010:GSM432231       0.7468418        TRUE
2     JapaneseB:GSM795125 Yoshihara2010:GSM432223       0.9694795        TRUE
3     JapaneseB:GSM795126 Yoshihara2010:GSM432225       0.9626765        TRUE
4     JapaneseB:GSM795127 Yoshihara2010:GSM432226       0.9695461        TRUE
5     JapaneseB:GSM795128 Yoshihara2010:GSM432228       0.9640154        TRUE
6     JapaneseB:GSM795129 Yoshihara2010:GSM432229       0.9695206        TRUE
  pheno.similarity pheno.doppel smokinggun.similarity smokinggun.doppel
1        0.2029299        FALSE                    NA             FALSE
2        0.7274151        FALSE                    NA             FALSE
3        0.7412337        FALSE                    NA             FALSE
4        1.0000000         TRUE                    NA             FALSE
5        0.5398956        FALSE                    NA             FALSE
6        1.0000000         TRUE                    NA             FALSE
      alt_sample_name unique_patient_ID sample_type histological_type
1 tumor_112:tumor_113             NA:NA tumor:tumor           ser:ser
2 tumor_106:tumor_106             NA:NA tumor:tumor           ser:ser
3 tumor_108:tumor_108             NA:NA tumor:tumor           ser:ser
4 tumor_109:tumor_109             NA:NA tumor:tumor           ser:ser
5 tumor_110:tumor_110             NA:NA tumor:tumor           ser:ser
6 tumor_111:tumor_111             NA:NA tumor:tumor           ser:ser
  primarysite arrayedsite summarygrade summarystage tumorstage substage grade
1       ov:ov       NA:NA      low:low    late:late        3:3      a:c   2:1
2       NA:ov       NA:NA     low:high    late:late        3:3      c:c   2:3
3       NA:ov       NA:NA     high:low    late:late        3:3      b:b   3:1
4       NA:ov       NA:NA    high:high    late:late        3:3      c:c   3:3
5       NA:ov       NA:NA     low:high    late:late        3:3      c:c   2:3
6       NA:ov       NA:NA      low:low    late:late        3:3      c:c   2:2
  age_at_initial_pathologic_diagnosis pltx tax   neo days_to_tumor_recurrence
1                               NA:NA  y:y y:y NA:NA                1440:1290
2                               NA:NA  y:y y:y NA:NA                   NA:450
3                               NA:NA  y:y y:y NA:NA                  NA:1110
4                               NA:NA  y:y y:y NA:NA                   NA:210
5                               NA:NA  y:y y:y NA:NA                  NA:1800
6                               NA:NA  y:y y:y NA:NA                   NA:540
        recurrence_status days_to_death      vital_status os_binary
1 norecurrence:recurrence     1440:2070     living:living     NA:NA
2           NA:recurrence       780:780     living:living     NA:NA
3         NA:norecurrence     1110:1110     living:living     NA:NA
4           NA:recurrence       600:600 deceased:deceased     NA:NA
5         NA:norecurrence     2910:1800     living:living     NA:NA
6           NA:recurrence     1710:1710 deceased:deceased     NA:NA
  relapse_binary site_of_tumor_first_recurrence primary_therapy_outcome_success
1          NA:NA                          NA:NA                           NA:NA
2          NA:NA                          NA:NA                           NA:NA
3          NA:NA                          NA:NA                           NA:NA
4          NA:NA                          NA:NA                           NA:NA
5          NA:NA                          NA:NA                           NA:NA
6          NA:NA                          NA:NA                           NA:NA
              debulking percent_normal_cells percent_stromal_cells
1    optimal:suboptimal                NA:NA                 NA:NA
2 suboptimal:suboptimal                NA:NA                 NA:NA
3       optimal:optimal                NA:NA                 NA:NA
4 suboptimal:suboptimal                NA:NA                 NA:NA
5 suboptimal:suboptimal                NA:NA                 NA:NA
6 suboptimal:suboptimal                NA:NA                 NA:NA
  percent_tumor_cells batch  flag flag_notes
1               NA:NA NA:NA NA:NA      NA:NA
2               NA:NA NA:NA NA:NA      NA:NA
3               NA:NA NA:NA NA:NA      NA:NA
4               NA:NA NA:NA NA:NA      NA:NA
5               NA:NA NA:NA NA:NA      NA:NA
6               NA:NA NA:NA NA:NA      NA:NA
> ## Set phenoFinder.args=NULL to ignore similar phenotypes, and
> ## turn off ComBat batch correction:
> ##    results2 <- doppelgangR(testesets, corFinder.args=list(use.ComBat=FALSE), phenoFinder.args=NULL, cache.dir=NULL)
> ##    summary(results2)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>

doppelgangR

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Results