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Last data update: 2014.03.03

R: SNP cluster plots

genoClusterPlot

R Documentation

SNP cluster plots

Description

Generates either X,Y or R,Theta cluster plots for specified SNP's.

Usage

genoClusterPlot(intenData, genoData, plot.type = c("RTheta", "XY"), 
                snpID, main.txt = NULL, by.sex = FALSE,
                scan.sel = NULL, scan.hilite = NULL,
                start.axis.at.0 = FALSE,
		colors = c("default", "neon", "primary"),
                verbose = TRUE, ...)

genoClusterPlotByBatch(intenData, genoData, plot.type = c("RTheta", "XY"), 
                       snpID, batchVar, main.txt = NULL, scan.sel = NULL, 
                       colors = c("default", "neon", "primary"),
                       verbose = TRUE, ...)

Arguments

`intenData`	`IntensityData` object containing 'X' and 'Y' values.
`genoData`	`GenotypeData` object
`plot.type`	The type of plots to generate. Possible values are "RTheta" (default) or "XY".
`snpID`	A numerical vector containing the SNP number for each plot.
`batchVar`	A character string indicating which annotation variable should be used as the batch.
`main.txt`	A character vector containing the title to give to each plot.
`by.sex`	Logical value specifying whether to indicate sex on the plot. If `TRUE`, sex must be present in intenData or genoData.
`scan.sel`	integer vector of scans to include in the plot. If `NULL`, all scans will be included.
`scan.hilite`	integer vector of scans to highlight in the plot with different colors. If `NULL`, all scans will be plotted with the same colors.
`start.axis.at.0`	Logical for whether the min value of each axis should be 0.
`colors`	Color scheme to use for genotypes. "default" is colorblind safe (colorbrewer Set2), "neon" is bright orange/green/fuschia, and "primary" is red/green/blue.
`verbose`	Logical value specifying whether to show progress.
`...`	Other parameters to be passed directly to `plot`.

Details

Either 'RTheta' (default) or 'XY' plots can be generated. R and Theta values are computed from X and Y using the formulas r <- x+y and theta <- atan(y/x)*(2/pi).

If by.sex==TRUE, females are indicated with circles and males with crosses.

Author(s)

Caitlin McHugh

Examples

# create data object
library(GWASdata)
data(illuminaScanADF, illuminaSnpADF)

xyfile <- system.file("extdata", "illumina_qxy.gds", package="GWASdata")
xy <- GdsIntensityReader(xyfile)
xyData <-  IntensityData(xy, scanAnnot=illuminaScanADF, snpAnnot=illuminaSnpADF)

genofile <- system.file("extdata", "illumina_geno.gds", package="GWASdata")
geno <- GdsGenotypeReader(genofile)
genoData <-  GenotypeData(geno, scanAnnot=illuminaScanADF, snpAnnot=illuminaSnpADF)

# select first 9 snps
snpID <- illuminaSnpADF$snpID[1:9]
rsID <- illuminaSnpADF$rsID[1:9]

par(mfrow=c(3,3)) # plot 3x3
genoClusterPlot(xyData, genoData, snpID=snpID, main.txt=rsID)

# select samples
scan.sel <- illuminaScanADF$scanID[illuminaScanADF$race == "CEU"]
genoClusterPlot(xyData, genoData, snpID=snpID, main.txt=rsID,
                scan.sel=scan.sel, by.sex=TRUE)

genoClusterPlot(xyData, genoData, snpID=snpID, main.txt=rsID,
                scan.hilite=scan.sel)
close(xyData)
close(genoData)

## affy data - cluster plots by plate
data(affyScanADF, affySnpADF)

xyfile <- system.file("extdata", "affy_qxy.nc", package="GWASdata")
xy <- NcdfIntensityReader(xyfile)
xyData <-  IntensityData(xy, scanAnnot=affyScanADF, snpAnnot=affySnpADF)

genofile <- system.file("extdata", "affy_geno.nc", package="GWASdata")
geno <- NcdfGenotypeReader(genofile)
genoData <-  GenotypeData(geno, scanAnnot=affyScanADF, snpAnnot=affySnpADF)

# select first 9 snps
snpID <- affySnpADF$snpID[1:9]
rsID <- affySnpADF$rsID[1:9]

genoClusterPlotByBatch(xyData, genoData, snpID=snpID, main.txt=rsID,
                       batchVar="plate")
close(xyData)
close(genoData)

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(GWASTools)
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")'.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/GWASTools/genoClusterPlot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: genoClusterPlot
> ### Title: SNP cluster plots
> ### Aliases: genoClusterPlot genoClusterPlotByBatch
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # create data object
> library(GWASdata)
> data(illuminaScanADF, illuminaSnpADF)
> 
> xyfile <- system.file("extdata", "illumina_qxy.gds", package="GWASdata")
> xy <- GdsIntensityReader(xyfile)
> xyData <-  IntensityData(xy, scanAnnot=illuminaScanADF, snpAnnot=illuminaSnpADF)
> 
> genofile <- system.file("extdata", "illumina_geno.gds", package="GWASdata")
> geno <- GdsGenotypeReader(genofile)
> genoData <-  GenotypeData(geno, scanAnnot=illuminaScanADF, snpAnnot=illuminaSnpADF)
> 
> # select first 9 snps
> snpID <- illuminaSnpADF$snpID[1:9]
> rsID <- illuminaSnpADF$rsID[1:9]
> 
> par(mfrow=c(3,3)) # plot 3x3
> genoClusterPlot(xyData, genoData, snpID=snpID, main.txt=rsID)
start time = 2016-07-05 21:58:33
plot number = 1  system time = 2016-07-05 21:58:33
plot number = 2  system time = 2016-07-05 21:58:33
plot number = 3  system time = 2016-07-05 21:58:33
plot number = 4  system time = 2016-07-05 21:58:33
plot number = 5  system time = 2016-07-05 21:58:33
plot number = 6  system time = 2016-07-05 21:58:33
plot number = 7  system time = 2016-07-05 21:58:33
plot number = 8  system time = 2016-07-05 21:58:33
plot number = 9  system time = 2016-07-05 21:58:33
> 
> # select samples
> scan.sel <- illuminaScanADF$scanID[illuminaScanADF$race == "CEU"]
> genoClusterPlot(xyData, genoData, snpID=snpID, main.txt=rsID,
+                 scan.sel=scan.sel, by.sex=TRUE)
start time = 2016-07-05 21:58:33
plot number = 1  system time = 2016-07-05 21:58:33
plot number = 2  system time = 2016-07-05 21:58:33
plot number = 3  system time = 2016-07-05 21:58:33
plot number = 4  system time = 2016-07-05 21:58:33
plot number = 5  system time = 2016-07-05 21:58:33
plot number = 6  system time = 2016-07-05 21:58:33
plot number = 7  system time = 2016-07-05 21:58:33
plot number = 8  system time = 2016-07-05 21:58:33
plot number = 9  system time = 2016-07-05 21:58:33
> 
> genoClusterPlot(xyData, genoData, snpID=snpID, main.txt=rsID,
+                 scan.hilite=scan.sel)
start time = 2016-07-05 21:58:33
plot number = 1  system time = 2016-07-05 21:58:33
plot number = 2  system time = 2016-07-05 21:58:33
plot number = 3  system time = 2016-07-05 21:58:33
plot number = 4  system time = 2016-07-05 21:58:33
plot number = 5  system time = 2016-07-05 21:58:33
plot number = 6  system time = 2016-07-05 21:58:33
plot number = 7  system time = 2016-07-05 21:58:33
plot number = 8  system time = 2016-07-05 21:58:33
plot number = 9  system time = 2016-07-05 21:58:33
> close(xyData)
> close(genoData)
> 
> ## affy data - cluster plots by plate
> data(affyScanADF, affySnpADF)
> 
> xyfile <- system.file("extdata", "affy_qxy.nc", package="GWASdata")
> xy <- NcdfIntensityReader(xyfile)
> xyData <-  IntensityData(xy, scanAnnot=affyScanADF, snpAnnot=affySnpADF)
> 
> genofile <- system.file("extdata", "affy_geno.nc", package="GWASdata")
> geno <- NcdfGenotypeReader(genofile)
> genoData <-  GenotypeData(geno, scanAnnot=affyScanADF, snpAnnot=affySnpADF)
> 
> # select first 9 snps
> snpID <- affySnpADF$snpID[1:9]
> rsID <- affySnpADF$rsID[1:9]
> 
> genoClusterPlotByBatch(xyData, genoData, snpID=snpID, main.txt=rsID,
+                        batchVar="plate")
start time = 2016-07-05 21:58:33
i= 1 j= 1 2016-07-05 21:58:33
i= 1 j= 2 2016-07-05 21:58:33
i= 1 j= 3 2016-07-05 21:58:33
i= 2 j= 1 2016-07-05 21:58:33
i= 2 j= 2 2016-07-05 21:58:33
i= 2 j= 3 2016-07-05 21:58:33
i= 3 j= 1 2016-07-05 21:58:33
i= 3 j= 2 2016-07-05 21:58:33
i= 3 j= 3 2016-07-05 21:58:33
i= 4 j= 1 2016-07-05 21:58:33
i= 4 j= 2 2016-07-05 21:58:33
i= 4 j= 3 2016-07-05 21:58:33
i= 5 j= 1 2016-07-05 21:58:33
i= 5 j= 2 2016-07-05 21:58:34
i= 5 j= 3 2016-07-05 21:58:34
i= 6 j= 1 2016-07-05 21:58:34
i= 6 j= 2 2016-07-05 21:58:34
i= 6 j= 3 2016-07-05 21:58:34
i= 7 j= 1 2016-07-05 21:58:34
i= 7 j= 2 2016-07-05 21:58:34
i= 7 j= 3 2016-07-05 21:58:34
i= 8 j= 1 2016-07-05 21:58:34
i= 8 j= 2 2016-07-05 21:58:34
i= 8 j= 3 2016-07-05 21:58:34
i= 9 j= 1 2016-07-05 21:58:34
i= 9 j= 2 2016-07-05 21:58:34
i= 9 j= 3 2016-07-05 21:58:34
> close(xyData)
> close(genoData)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>