cped: Phenotype Object

Description

Creates, tests, reads, or writes an object of class cped.

Usage

as.cped(x,
  pid="pid", id="id", idfath="idfath",
  idmoth="idmoth", sex="sex", affection="AffectionStatus",
  clearSym=FALSE)
is.cped(obj)
read.cped(filename, lowercase=TRUE, sym=TRUE, max=100, ...)
fread.cped(filename, ...)
write.cped(file, cped)
# S3 method for cped
sort(x,decreasing=FALSE,...)
plotCPed( cped, sink=NULL )

Arguments

x

An object of class cped or data.frame as described below.

If x is of class cped, no other options are used.

When x is of class data.frame, the columns have entries that match the string parameters idped,...,AffectionStatus; copy number variation measurements markers consist of subsequent columns formated as follows: e.g. two copy number variants `cnv1' and `cnv2' with three intensities would need a total of six columns (three per cnv) past the first six named 'cnv1.1', 'cnv1.2', 'cnv1.3', 'cnv2.1', 'cnv2.2', 'cnv2.3'. See the examples below.

A slightly different format can be used when reading data from disk, that is more or less consistent with how a pedigree file is loaded from disk. See the details below.

pid

String corresponding to column name for pedigree id.

id

String corresponding to column name for subject id.

idfath

String corresponding to column name for father id.

idmoth

String corresponding to column name for mother id.

sex

String corresponding to column name for sex.

affection

String corresponding to column name for affection status.

filename

Filename to open; does not need .phe extension. See the details below for the file format.

lowercase

When TRUE (and sym is FALSE), enforces all headers to lowercase for convenience.

...

Options for read.table, used only when sym is FALSE. Do not put in header=TRUE, as this will cause an error, as the header is automatically loaded.

With the proper file formatting, this should not be used.

file

string representing filename, or a connection for file output

cped

an object of class cped

obj

an object

sym

When TRUE, only the header of the file is read in; only PBAT will load in the file. When FALSE, the entire file will be read in, and can be modified before using with PBAT.

max

When sym is TRUE, the amount of headers to read in before going pure symbolic (so that the SNP usage consistency will not be assessed by pbatR, only by PBAT).

clearSym

When TRUE, if a symbolic file is found, it will be read in; otherwise, it will stay symbolic.

decreasing

Whether to sort in decreasing/increasing order.

sink

For `plot.cped', this is the name of a pdf file to output all of the plots to (there will be one plot per page).

Details

When reading in a file on disk using read.cped, a `.cped' file should have the following format. The file should be formatted as follows. The first six columns are unlabeled (1) the pedigree id, (2) the individual id, (3) the father id, (4) the mother id, (5) sex [0=missing, 1=male, 2=female], and (6) AffectionStatus [0=missing, 1=unaffected, 2=affected]. The subsequent columns correspond to the intensities. So, suppose we have cnv1 and cnv2. The first line of the file would contain `cnv1 cnv2'. Then the subsequent lines would correspond to each individual, the first six columns being as described, and then NUMINTENSITY columns per cnv for a total of 6+2*NUMINTENSITY data columns. NUMINTENSITY is just however many intensities there are per cnv, you will need to specify this number at analysis time. NOTE: MISSING DATA in a cped file should be coded as `-1234.0', rather than the usual `.' or `-' (technically the `.' and `-' should still work with fread.cped, and when sym=FALSE).

The best way to see all of this in action is to run the code in the examples below, and look at the cped file produced from it.

`plotCPed' plots the data similar to the `plotPed' routine (in fact it transforms the data to use it).

Examples

Run this code

###################
## First Example ##

## A highly artificial example with not enough subjects to be run;
##  however, it demonstrates how to put data in it.
## We have two cnvs here, cnv1 and cnv2.
## The data is just completely random.
set.seed(13)
x <- data.frame( pid       = c(1,1,1,1,1),
                 id        = c(1,2,3,4,5),
                 idfath    = c(4,4,4,0,0),
                 idmoth    = c(5,5,5,0,0),
                 sex       = c(1,2,1,1,2),
                 AffectionStatus = c(1,0,0,1,0),
                 cnv1.1     = runif(5),
                 cnv1.2     = runif(5),
                 cnv1.3     = runif(5),
                 cnv2.1     = runif(5),
                 cnv2.2     = runif(5),
                 cnv2.3     = runif(5) )
x
myCPed <- as.cped( x ) # Mark it with the class 'cped'
myCPed

if (FALSE) {
####################
## Second Example ##

## Again, a completely random dataset.
## Here we go through an analysis of it.
## However, see pbat.m for many more details on all of the options.
## Create a completely random dataset with one cnv.
set.seed(13)
NUMTRIOS <- 500
## The data is completely random, it does not really make any sense.
cped <- as.cped(data.frame(
  pid             = kronecker(1:NUMTRIOS, rep(1,3)),
  id              = rep(1:3, NUMTRIOS),
  idfath          = rep(c(0,0,1), NUMTRIOS),
  idmoth          = rep(c(0,0,2), NUMTRIOS),
  sex             = rep(c(2,1,1), NUMTRIOS),
  AffectionStatus = rep(c(0,0,2), NUMTRIOS),
  cnv1.1          = runif(3*NUMTRIOS),
  cnv1.2          = runif(3*NUMTRIOS),
  cnv1.3          = runif(3*NUMTRIOS)))

## Print out part of the dataset
print(head(cped))

## Command line run
pbat.work() ## Makes the intermediate files go in ./pbatRwork directory

## - Analyzing the first intensity
res1 <- pbat.m(AffectionStatus ~ NONE, ped=cped, phe=NULL, fbat="gee",
                cnv.intensity=1, cnv.intensity.num=3, offset="none")
pbat.clean(res1, all.output=TRUE) ## Removes all intermediate files
## - Analyzing the second intensity
res2 <- pbat.m(AffectionStatus ~ NONE, ped=cped, phe=NULL, fbat="gee",
                cnv.intensity=2, cnv.intensity.num=3, offset="none")
pbat.clean(res2, all.output=TRUE)
## - Analyzing the third intensity
res3 <- pbat.m(AffectionStatus ~ NONE, ped=cped, phe=NULL, fbat="gee",
                cnv.intensity=3, cnv.intensity.num=3, offset="none")
pbat.clean(res3, all.output=TRUE)

pbat.unwork() ## Close up work (head to original working directory)

## Print all of the results
print(res1$results)
print(res2$results)
print(res3$results)

## Or put all the results together and write to file
res1$results <- rbind(res1$results, res2$results, res3$results)
write.pbat(res1, "cpedResults.csv")

## Otherwise, we could write the data to disk, 
##  and run with the GUI interface
## Write the data to disk:
write.cped("cped.cped", cped)
}

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Description

Usage

Arguments

Details

See Also

Examples