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rfunc.shtml
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<html>
<title>PLINK</title>
<body>
<head>
<link rel="stylesheet" href="plink.css" type="text/css">
<META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=utf-8">
<title>PLINK: Whole genome data analysis toolset</title>
</head>
<!--<html>-->
<!--<title>PLINK</title>-->
<!--<body>-->
<font size="6" color="darkgreen"><b>plink...</b></font>
<div style="position:absolute;right:10px;top:10px;font-size:
75%"><em>Last original <tt>PLINK</tt> release is <b>v1.07</b>
(10-Oct-2009); <b>PLINK 1.9</b> is now <a href="plink2.shtml"> available</a> for beta-testing</em></div>
<h1>Whole genome association analysis toolset</h1>
<font size="1" color="darkgreen">
<em>
<a href="index.shtml">Introduction</a> |
<a href="contact.shtml">Basics</a> |
<a href="download.shtml">Download</a> |
<a href="reference.shtml">Reference</a> |
<a href="data.shtml">Formats</a> |
<a href="dataman.shtml">Data management</a> |
<a href="summary.shtml">Summary stats</a> |
<a href="thresh.shtml">Filters</a> |
<a href="strat.shtml">Stratification</a> |
<a href="ibdibs.shtml">IBS/IBD</a> |
<a href="anal.shtml">Association</a> |
<a href="fanal.shtml">Family-based</a> |
<a href="perm.shtml">Permutation</a> |
<a href="ld.shtml">LD calcualtions</a> |
<a href="haplo.shtml">Haplotypes</a> |
<a href="whap.shtml">Conditional tests</a> |
<a href="proxy.shtml">Proxy association</a> |
<a href="pimputation.shtml">Imputation</a> |
<a href="dosage.shtml">Dosage data</a> |
<a href="metaanal.shtml">Meta-analysis</a> |
<a href="annot.shtml">Result annotation</a> |
<a href="clump.shtml">Clumping</a> |
<a href="grep.shtml">Gene Report</a> |
<a href="epi.shtml">Epistasis</a> |
<a href="cnv.shtml">Rare CNVs</a> |
<a href="gvar.shtml">Common CNPs</a> |
<a href="rfunc.shtml">R-plugins</a> |
<a href="psnp.shtml">SNP annotation</a> |
<a href="simulate.shtml">Simulation</a> |
<a href="profile.shtml">Profiles</a> |
<a href="ids.shtml">ID helper</a> |
<a href="res.shtml">Resources</a> |
<a href="flow.shtml">Flow chart</a> |
<a href="misc.shtml">Misc.</a> |
<a href="faq.shtml">FAQ</a> |
<a href="gplink.shtml">gPLINK</a>
</em></font>
</p>
<table border=0>
<tr>
<td bgcolor="lightblue" valign="top" width=20%>
<font size="1">
<a href="index.shtml">1. Introduction</a> </p>
<a href="contact.shtml">2. Basic information</a> </p>
<ul>
<li> <a href="contact.shtml#cite">Citing PLINK</a>
<li> <a href="contact.shtml#probs">Reporting problems</a>
<li> <a href="news.shtml">What's new?</a>
<li> <a href="pdf.shtml">PDF documentation</a>
</ul>
<a href="download.shtml">3. Download and general notes</a> </p>
<ul>
<li> <a href="download.shtml#download">Stable download</a>
<li> <a href="download.shtml#latest">Development code</a>
<li> <a href="download.shtml#general">General notes</a>
<li> <a href="download.shtml#msdos">MS-DOS notes</a>
<li> <a href="download.shtml#nix">Unix/Linux notes</a>
<li> <a href="download.shtml#compilation">Compilation</a>
<li> <a href="download.shtml#input">Using the command line</a>
<li> <a href="download.shtml#output">Viewing output files</a>
<li> <a href="changelog.shtml">Version history</a>
</ul>
<a href="reference.shtml">4. Command reference table</a> </p>
<ul>
<li> <a href="reference.shtml#options">List of options</a>
<li> <a href="reference.shtml#output">List of output files</a>
<li> <a href="newfeat.shtml">Under development</a>
</ul>
<a href="data.shtml">5. Basic usage/data formats</a>
<ul>
<li> <a href="data.shtml#plink">Running PLINK</a>
<li> <a href="data.shtml#ped">PED files</a>
<li> <a href="data.shtml#map">MAP files</a>
<li> <a href="data.shtml#tr">Transposed filesets</a>
<li> <a href="data.shtml#long">Long-format filesets</a>
<li> <a href="data.shtml#bed">Binary PED files</a>
<li> <a href="data.shtml#pheno">Alternate phenotypes</a>
<li> <a href="data.shtml#covar">Covariate files</a>
<li> <a href="data.shtml#clst">Cluster files</a>
<li> <a href="data.shtml#sets">Set files</a>
</ul>
<a href="dataman.shtml">6. Data management</a> </p>
<ul>
<li> <a href="dataman.shtml#recode">Recode</a>
<li> <a href="dataman.shtml#recode">Reorder</a>
<li> <a href="dataman.shtml#snplist">Write SNP list</a>
<li> <a href="dataman.shtml#updatemap">Update SNP map</a>
<li> <a href="dataman.shtml#updateallele">Update allele information</a>
<li> <a href="dataman.shtml#refallele">Force reference allele</a>
<li> <a href="dataman.shtml#updatefam">Update individuals</a>
<li> <a href="dataman.shtml#wrtcov">Write covariate files</a>
<li> <a href="dataman.shtml#wrtclst">Write cluster files</a>
<li> <a href="dataman.shtml#flip">Flip strand</a>
<li> <a href="dataman.shtml#flipscan">Scan for strand problem</a>
<li> <a href="dataman.shtml#merge">Merge two files</a>
<li> <a href="dataman.shtml#mergelist">Merge multiple files</a>
<li> <a href="dataman.shtml#extract">Extract SNPs</a>
<li> <a href="dataman.shtml#exclude">Remove SNPs</a>
<li> <a href="dataman.shtml#zero">Zero out sets of genotypes</a>
<li> <a href="dataman.shtml#keep">Extract Individuals</a>
<li> <a href="dataman.shtml#remove">Remove Individuals</a>
<li> <a href="dataman.shtml#filter">Filter Individuals</a>
<li> <a href="dataman.shtml#attrib">Attribute filters</a>
<li> <a href="dataman.shtml#makeset">Create a set file</a>
<li> <a href="dataman.shtml#tabset">Tabulate SNPs by sets</a>
<li> <a href="dataman.shtml#snp-qual">SNP quality scores</a>
<li> <a href="dataman.shtml#geno-qual">Genotypic quality scores</a>
</ul>
<a href="summary.shtml">7. Summary stats</a>
<ul>
<li> <a href="summary.shtml#missing">Missingness</a>
<li> <a href="summary.shtml#oblig_missing">Obligatory missingness</a>
<li> <a href="summary.shtml#clustermissing">IBM clustering</a>
<li> <a href="summary.shtml#testmiss">Missingness by phenotype</a>
<li> <a href="summary.shtml#mishap">Missingness by genotype</a>
<li> <a href="summary.shtml#hardy">Hardy-Weinberg</a>
<li> <a href="summary.shtml#freq">Allele frequencies</a>
<li> <a href="summary.shtml#prune">LD-based SNP pruning</a>
<li> <a href="summary.shtml#mendel">Mendel errors</a>
<li> <a href="summary.shtml#sexcheck">Sex check</a>
<li> <a href="summary.shtml#pederr">Pedigree errors</a>
</ul>
<a href="thresh.shtml">8. Inclusion thresholds</a>
<ul>
<li> <a href="thresh.shtml#miss2">Missing/person</a>
<li> <a href="thresh.shtml#maf">Allele frequency</a>
<li> <a href="thresh.shtml#miss1">Missing/SNP</a>
<li> <a href="thresh.shtml#hwd">Hardy-Weinberg</a>
<li> <a href="thresh.shtml#mendel">Mendel errors</a>
</ul>
<a href="strat.shtml">9. Population stratification</a>
<ul>
<li> <a href="strat.shtml#cluster">IBS clustering</a>
<li> <a href="strat.shtml#permtest">Permutation test</a>
<li> <a href="strat.shtml#options">Clustering options</a>
<li> <a href="strat.shtml#matrix">IBS matrix</a>
<li> <a href="strat.shtml#mds">Multidimensional scaling</a>
<li> <a href="strat.shtml#outlier">Outlier detection</a>
</ul>
<a href="ibdibs.shtml">10. IBS/IBD estimation</a>
<ul>
<li> <a href="ibdibs.shtml#genome">Pairwise IBD</a>
<li> <a href="ibdibs.shtml#inbreeding">Inbreeding</a>
<li> <a href="ibdibs.shtml#homo">Runs of homozygosity</a>
<li> <a href="ibdibs.shtml#segments">Shared segments</a>
</ul>
<a href="anal.shtml">11. Association</a>
<ul>
<li> <a href="anal.shtml#cc">Case/control</a>
<li> <a href="anal.shtml#fisher">Fisher's exact</a>
<li> <a href="anal.shtml#model">Full model</a>
<li> <a href="anal.shtml#strat">Stratified analysis</a>
<li> <a href="anal.shtml#homog">Tests of heterogeneity</a>
<li> <a href="anal.shtml#hotel">Hotelling's T(2) test</a>
<li> <a href="anal.shtml#qt">Quantitative trait</a>
<li> <a href="anal.shtml#qtmeans">Quantitative trait means</a>
<li> <a href="anal.shtml#qtgxe">Quantitative trait GxE</a>
<li> <a href="anal.shtml#glm">Linear and logistic models</a>
<li> <a href="anal.shtml#set">Set-based tests</a>
<li> <a href="anal.shtml#adjust">Multiple-test correction</a>
</ul>
<a href="fanal.shtml">12. Family-based association</a>
<ul>
<li> <a href="fanal.shtml#tdt">TDT</a>
<li> <a href="fanal.shtml#ptdt">ParenTDT</a>
<li> <a href="fanal.shtml#poo">Parent-of-origin</a>
<li> <a href="fanal.shtml#dfam">DFAM test</a>
<li> <a href="fanal.shtml#qfam">QFAM test</a>
</ul>
<a href="perm.shtml">13. Permutation procedures</a>
<ul>
<li> <a href="perm.shtml#perm">Basic permutation</a>
<li> <a href="perm.shtml#aperm">Adaptive permutation</a>
<li> <a href="perm.shtml#mperm">max(T) permutation</a>
<li> <a href="perm.shtml#rank">Ranked permutation</a>
<li> <a href="perm.shtml#genedropmodel">Gene-dropping</a>
<li> <a href="perm.shtml#cluster">Within-cluster</a>
<li> <a href="perm.shtml#mkphe">Permuted phenotypes files</a>
</ul>
<a href="ld.shtml">14. LD calculations</a>
<ul>
<li> <a href="ld.shtml#ld1">2 SNP pairwise LD</a>
<li> <a href="ld.shtml#ld2">N SNP pairwise LD</a>
<li> <a href="ld.shtml#tags">Tagging options</a>
<li> <a href="ld.shtml#blox">Haplotype blocks</a>
</ul>
<a href="haplo.shtml">15. Multimarker tests</a>
<ul>
<li> <a href="haplo.shtml#hap1">Imputing haplotypes</a>
<li> <a href="haplo.shtml#precomputed">Precomputed lists</a>
<li> <a href="haplo.shtml#hap2">Haplotype frequencies</a>
<li> <a href="haplo.shtml#hap3">Haplotype-based association</a>
<li> <a href="haplo.shtml#hap3c">Haplotype-based GLM tests</a>
<li> <a href="haplo.shtml#hap3b">Haplotype-based TDT</a>
<li> <a href="haplo.shtml#hap4">Haplotype imputation</a>
<li> <a href="haplo.shtml#hap5">Individual phases</a>
</ul>
<a href="whap.shtml">16. Conditional haplotype tests</a>
<ul>
<li> <a href="whap.shtml#whap1">Basic usage</a>
<li> <a href="whap.shtml#whap2">Specifying type of test</a>
<li> <a href="whap.shtml#whap3">General haplogrouping</a>
<li> <a href="whap.shtml#whap4">Covariates and other SNPs</a>
</ul>
<a href="proxy.shtml">17. Proxy association</a>
<ul>
<li> <a href="proxy.shtml#proxy1">Basic usage</a>
<li> <a href="proxy.shtml#proxy2">Refining a signal</a>
<li> <a href="proxy.shtml#proxy2b">Multiple reference SNPs</a>
<li> <a href="proxy.shtml#proxy3">Haplotype-based SNP tests</a>
</ul>
<a href="pimputation.shtml">18. Imputation (beta)</a>
<ul>
<li> <a href="pimputation.shtml#impute1">Making reference set</a>
<li> <a href="pimputation.shtml#impute2">Basic association test</a>
<li> <a href="pimputation.shtml#impute3">Modifying parameters</a>
<li> <a href="pimputation.shtml#impute4">Imputing discrete calls</a>
<li> <a href="pimputation.shtml#impute5">Verbose output options</a>
</ul>
<a href="dosage.shtml">19. Dosage data</a>
<ul>
<li> <a href="dosage.shtml#format">Input file formats</a>
<li> <a href="dosage.shtml#assoc">Association analysis</a>
<li> <a href="dosage.shtml#output">Outputting dosage data</a>
</ul>
<a href="metaanal.shtml">20. Meta-analysis</a>
<ul>
<li> <a href="metaanal.shtml#basic">Basic usage</a>
<li> <a href="metaanal.shtml#opt">Misc. options</a>
</ul>
<a href="annot.shtml">21. Annotation</a>
<ul>
<li> <a href="annot.shtml#basic">Basic usage</a>
<li> <a href="annot.shtml#opt">Misc. options</a>
</ul>
<a href="clump.shtml">22. LD-based results clumping</a>
<ul>
<li> <a href="clump.shtml#clump1">Basic usage</a>
<li> <a href="clump.shtml#clump2">Verbose reporting</a>
<li> <a href="clump.shtml#clump3">Combining multiple studies</a>
<li> <a href="clump.shtml#clump4">Best single proxy</a>
</ul>
<a href="grep.shtml">23. Gene-based report</a>
<ul>
<li> <a href="grep.shtml#grep1">Basic usage</a>
<li> <a href="grep.shtml#grep2">Other options</a>
</ul>
<a href="epi.shtml">24. Epistasis</a>
<ul>
<li> <a href="epi.shtml#snp">SNP x SNP</a>
<li> <a href="epi.shtml#case">Case-only</a>
<li> <a href="epi.shtml#gene">Gene-based</a>
</ul>
<a href="cnv.shtml">25. Rare CNVs</a>
<ul>
<li> <a href="cnv.shtml#format">File format</a>
<li> <a href="cnv.shtml#maps">MAP file construction</a>
<li> <a href="cnv.shtml#loading">Loading CNVs</a>
<li> <a href="cnv.shtml#olap_check">Check for overlap</a>
<li> <a href="cnv.shtml#type_filter">Filter on type </a>
<li> <a href="cnv.shtml#gene_filter">Filter on genes </a>
<li> <a href="cnv.shtml#freq_filter">Filter on frequency </a>
<li> <a href="cnv.shtml#burden">Burden analysis</a>
<li> <a href="cnv.shtml#burden2">Geneset enrichment</a>
<li> <a href="cnv.shtml#assoc">Mapping loci</a>
<li> <a href="cnv.shtml#reg-assoc">Regional tests</a>
<li> <a href="cnv.shtml#qt-assoc">Quantitative traits</a>
<li> <a href="cnv.shtml#write_cnvlist">Write CNV lists</a>
<li> <a href="cnv.shtml#report">Write gene lists</a>
<li> <a href="cnv.shtml#groups">Grouping CNVs </a>
</ul>
<a href="gvar.shtml">26. Common CNPs</a>
<ul>
<li> <a href="gvar.shtml#cnv2"> CNPs/generic variants</a>
<li> <a href="gvar.shtml#cnv2b"> CNP/SNP association</a>
</ul>
<a href="rfunc.shtml">27. R-plugins</a>
<ul>
<li> <a href="rfunc.shtml#rfunc1">Basic usage</a>
<li> <a href="rfunc.shtml#rfunc2">Defining the R function</a>
<li> <a href="rfunc.shtml#rfunc2b">Example of debugging</a>
<li> <a href="rfunc.shtml#rfunc3">Installing Rserve</a>
</ul>
<a href="psnp.shtml">28. Annotation web-lookup</a>
<ul>
<li> <a href="psnp.shtml#psnp1">Basic SNP annotation</a>
<li> <a href="psnp.shtml#psnp2">Gene-based SNP lookup</a>
<li> <a href="psnp.shtml#psnp3">Annotation sources</a>
</ul>
<a href="simulate.shtml">29. Simulation tools</a>
<ul>
<li> <a href="simulate.shtml#sim1">Basic usage</a>
<li> <a href="simulate.shtml#sim2">Resampling a population</a>
<li> <a href="simulate.shtml#sim3">Quantitative traits</a>
</ul>
<a href="profile.shtml">30. Profile scoring</a>
<ul>
<li> <a href="profile.shtml#prof1">Basic usage</a>
<li> <a href="profile.shtml#prof2">SNP subsets</a>
<li> <a href="profile.shtml#dose">Dosage data</a>
<li> <a href="profile.shtml#prof3">Misc options</a>
</ul>
<a href="ids.shtml">31. ID helper</a>
<ul>
<li> <a href="ids.shtml#ex">Overview/example</a>
<li> <a href="ids.shtml#intro">Basic usage</a>
<li> <a href="ids.shtml#check">Consistency checks</a>
<li> <a href="ids.shtml#alias">Aliases</a>
<li> <a href="ids.shtml#joint">Joint IDs</a>
<li> <a href="ids.shtml#lookup">Lookups</a>
<li> <a href="ids.shtml#replace">Replace values</a>
<li> <a href="ids.shtml#match">Match files</a>
<li> <a href="ids.shtml#qmatch">Quick match files</a>
<li> <a href="ids.shtml#misc">Misc.</a>
</ul>
<a href="res.shtml">32. Resources</a>
<ul>
<li> <a href="res.shtml#hapmap">HapMap (PLINK format)</a>
<li> <a href="res.shtml#teach">Teaching materials</a>
<li> <a href="res.shtml#mmtests">Multimarker tests</a>
<li> <a href="res.shtml#sets">Gene-set lists</a>
<li> <a href="res.shtml#glist">Gene range lists</a>
<li> <a href="res.shtml#attrib">SNP attributes</a>
</ul>
<a href="flow.shtml">33. Flow-chart</a>
<ul>
<li> <a href="flow.shtml">Order of commands</a>
</ul>
<a href="misc.shtml">34. Miscellaneous</a>
<ul>
<li> <a href="misc.shtml#opt">Command options/modifiers</a>
<li> <a href="misc.shtml#output">Association output modifiers</a>
<li> <a href="misc.shtml#species">Different species</a>
<li> <a href="misc.shtml#bugs">Known issues</a>
</ul>
<a href="faq.shtml">35. FAQ & Hints</a>
</p>
<a href="gplink.shtml">36. gPLINK</a>
<ul>
<li> <a href="gplink.shtml">gPLINK mainpage</a>
<li> <a href="gplink_tutorial/index.html">Tour of gPLINK</a>
<li> <a href="gplink.shtml#overview">Overview: using gPLINK</a>
<li> <a href="gplink.shtml#locrem">Local versus remote modes</a>
<li> <a href="gplink.shtml#start">Starting a new project</a>
<li> <a href="gplink.shtml#config">Configuring gPLINK</a>
<li> <a href="gplink.shtml#plink">Initiating PLINK jobs</a>
<li> <a href="gplink.shtml#view">Viewing PLINK output</a>
<li> <a href="gplink.shtml#hv">Integration with Haploview</a>
<li> <a href="gplink.shtml#down">Downloading gPLINK</a></p>
</ul>
</font>
</td><td width=5%>
<td valign="top">
</p>
<h1>R plugin functions</h1>
This page describes PLINK's limited support for R-based 'plug-in'
functions. In this manner, users can extend the basic functionality
of PLINK to better meet their own needs.
</p>
<a href="http://www.r-project.org/">R</a> is a powerful, freely-available
package for statistical computing. PLINK uses the <a
href="http://www.rforge.net/Rserve/">Rserve</a> package to communicate
with R. There are some notes on installing and running the Rserve
package below.
</p>
The idea is that some analyses, such as survival analysis for example,
are already implemented in R but not available in
PLINK. Having a simple interface for accessing such R functionality,
allows one to benefit from both the data-handling features of PLINK
(i.e. it being designed specifically to handle large SNP datasets
efficiently, in a way that the basic R package is not) as well as the
ever-increasing library of statistical tools in R. Also, this should
provide an easy way to prototype new methods, etc.
</p>
Currently there is only support for SNP-based analyses. As of version
1.05, multiple values can be returned for each SNP, as defined by the
user. Potentially (if there is interest/specific suggestions) these
features will be expanded to allow other units of analysis and
broader communcation with R.
</p><strong>Note</strong> Currently, there is only support for
R-plugins for Linux-based and Mac OS PLINK distributions.
</p><strong>Note</strong> Version 1.04 onwards of PLINK has updated
the client code to support the latest version of Rserve. You should
re-install Rserve (see notes below) to make sure you have the latest
version.
<a name="rfunc1">
<h2>Basic usage for R plug-ins</h2></a>
</p>
Assuming Rserve has been installed and is running locally
(see <a href="#rfunc3">below</a>) and that the file
<tt>myscript.R</tt> contains the R code conforming to the standard for a
PLINK plug-in (see <a href="#rfunc2">here</a>), then the command is simply
<p><h5>
plink --file mydata --R myscript.R
</h5></p>
which generates a file
<pre>
plink.auto.R
</pre>
This file contains the raw output for each SNP, which is whatever vector
of numeric values the user returned from their script, and some details about
the SNP. There is no header row; each row has the following fields.
<pre>
Chromosome position
SNP id
Physical position (base-pair)
Minor allele (A1)
First return value from R-plugin
Second return value from R-plugin
...
</pre>
Depending on how you set up the R script, each row may or may not have the same
number of columns. Currently it is not possible to return strings of other R
objects.
</p>
If <tt>Rserve</tt> is running on anything other than the default port, you can specify an alternate port number by adding
<pre>
--R-port 8221
</pre>
for example.
<a name="rfunc2">
<h2>Defining the R plug-in function</h2></a>
</p>
PLINK expects a function in the exact form
<pre>
Rplink <- function(PHENO,GENO,CLUSTER,COVAR)
</pre>
to be defined in the supplied file. This function is expected to return
a numeric vector, with as many elements are there are SNPs. Internally,
PLINK will call the <tt>Rplink</tt> function -- it must be written exactly
as shown here. The objects refer to:
<pre>
PHENO vector of phenotypes (n)
GENO matrix of genotypes (n x l)
CLUSTER vector of cluster membership codes (n)
COVAR matrix of covariates (n x c)
</pre>
where <tt>n</tt> is the number of individuals (after pruning, filtering,
etc) and <tt>c</tt> is the number of covariates (if any). PLINK
generates these objects internally, so the user can assume these exist
for when the <tt>Rplink()</tt> function is called. (In practice, the
number of SNPs, <tt>l</tt> will probably be smaller than the total number
of SNPs in the file, as PLINK passes the genotype data into R in batches
rather than all in one go).
</p>
Genotypes are coded 0, 1 or 2 copies of the minor allele,
and NA, as per the <tt>--recodeA</tt> option.
</p>
For each SNP, PLINK expects the function to return a numeric vector of values. This need not
have the same number of values for each SNP (although this will make subsequently parsing of the
output file harder, potentionally). If the desired return vector is <tt>r</tt>, then the actual
return vector must be
<pre>
c( length(r) , r )
</pre>
That is, PLINK expects back a long string of values, where it reads how many values to read for the first SNP, reads
them, then reads how many values to read for the second SNP, reads them, etc. By also using the abve formulation to
specify the return vector, PLINK will be able to parse the output.
</p>
An example R plug-in is shown here -- this is probably the most
straightforward template for an R-plugin, in which the <tt>apply()</tt>
function is used to iteratively call the nested function (<tt>f1()</tt>),
once per SNP, in this case. For example, the file <tt>myscript.R</tt>
might contain the following plug-in:
<pre>
Rplink <- function(PHENO,GENO,CLUSTER,COVAR)
{
f1 <- function(x)
{
r <- mean(x, na.rm=T) / 2
c( length(r) , r )
}
as.numeric( apply(GENO, 2 , f1) )
}
</pre>
If you are not familiar with the R language, there are a number of
excellent resources available from the main R <a
href="http://www.r-project.org/">webpage</a>.
</p>
Within the body of the main <tt>Rplink()</tt> function, there are no
constraints on what you can do, as long as the return value is in the
proper format, as described above. In this example, within the main
body of the <tt>Rplink()</tt> function we first define a function
that will be applied to each SNP, called <tt>f1()</tt>. Unlike
the <tt>Rplink()</tt> function, you can call this whatever you want,
or have as many functions as you want. The function <tt>f1()</tt>
calculates the allele frequency for each
SNP (as the genotypes are coded as the count of the minor allele,
0,1,2). The second line applies this function to each column of the
genotype data, using the <tt>apply( <em>data , row/col , function</em>
)</tt> command.
</p>
Another, perhaps more useful, example is implementing survival analysis within PLINK: here
we define a function, <tt>f1()</tt> to return the p-value for the first coefficient; we assume
here that a censoring variable was loaded into PLINK as the first covariate (i.e. the R <tt>Surv</tt>
function takes two parameters, the survival time and censoring status). (This is probably not the optimal
way to implement this analysis, but is intended purely as an example of what can be done.)
<pre>
library(survival)
Rplink <- function(PHENO,GENO,CLUSTER,COVAR)
{
f1 <- function(s)
{
m <- summary( coxph( Surv( PHENO , COVAR[,1] ) ~ s ) )
r <- c( m$coef , m$loglik, m$n )
c( length(r) , r )
}
apply( GENO , 2 , f1 )
}
</pre>
In other words, the general format is
<pre>
<em>{ load any libraries or auxiliary data from a file first }</em>
<b>Rplink <- function(PHENO,GENO,CLUSTER,COVAR)
{</b>
f1 <- function(x)
{
<em> { do something to generate per-SNP return vector r }</em>
c( length(r) , r )
}
apply( GENO , 2 , f1 )
<b>}</b>
</pre>
<a name="rfunc2b">
<h2>Example of debugging an R plug-in</h2></a>
</p>
</p>
To generate a text file that contains the R commands PLINK would have
run (rather than actually trying to run them -- this is useful for
debugging purposes), add the following flag
<p><h5> plink --file mydata --R myscript.R --R-debug
</h5></p>
To illustrate the debug function, consider this example, in which we
try to implement a logistic regression. The file
<pre>
mylog.R
</pre>
which contains the function
<pre>
Rplink <- function(PHENO,GENO,CLUSTER,COVAR)
{
f1 <- function(s)
{
m <- glm( PHENO ~ s , family="binomial" )
r <- summary(m)$coef[8]
c( length(r) , r )
}
apply( GENO , 2 , f1 )
}
</pre>
and we have a dataset with three SNPs; the internal PLINK logistic regression command
<h5>
plink --file mydata --logistic
</h5></p>
yields
<pre>
CHR SNP BP A1 TEST NMISS ODDS STAT P
1 snp0 10000 A ADD 200 1.256 1.15 0.2501
1 snp1 10001 B ADD 200 0.9028 -0.5112 0.6092
1 snp2 10002 B ADD 200 0.6085 -2.242 0.02499
</pre>
Trying to run the <tt>R</tt> implementation:
<h5>
plink --file mydata --R mylog.R
</h5></p>
we obtain a set of invalid p-values in <tt>plink.auto.R</tt>
<pre>
1 snp0 10000 A NA
1 snp1 10001 B NA
1 snp2 10002 B NA
</pre>
To find out what is happening, we will run the same command with the debug option
<h5>
plink --file mydata --R mylog.R --R-debug
</h5></p>
This writes to the file <tt>plink.auto.R</tt> the actual commands that would be passed to
R, including the data and the function:
<pre>
n <- 200
PHENO <- c( 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2,
1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2,
2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1,
2, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1,
2, 2, 1, 2, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1,
1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1,
1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1,
1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1,
2, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1 )
COVAR <- matrix( NA , nrow = n , ncol = 0 , byrow = T)
CLUSTER <- c( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
l <- 3
g <- c( 1, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 0, 0, 1, 1, 0, 1, 2, 1, 1, 1,
2, 1, 1, 2, 1, 1, 0, 1, 1, 1, 0, 1, 2, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 0, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 2, 0, 1,
1, 1, 2, 1, 1, 1, 1, 1, 0, 2, 2, 0, 0, 2, 1, 1, 1, 2, 1, 1, 0, 1, 1,
1, 1, 2, 2, 2, 1, 0, 2, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1,
0, 2, 2, 1, 0, 0, 0, 1, 0, 1, 2, 2, 2, 1, 0, 0, 0, 2, 1, 2, 2, 1, 1,
1, 1, 0, 0, 1, 1, 1, 1, 1, 2, 1, 1, 0, 1, 2, 2, 1, 2, 2, 1, 2, 0, 1,
1, 1, 1, 2, 1, 1, 0, 1, 0, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1,
2, 2, 1, 2, 1, 1, 2, 2, 0, 0, 1, 2, 1, 0, 0, 1, 1, 2, 1, 2, 2, 2, 0,
1, 1, 0, 2, 1, 1, 2, 1, 0, 1, 1, 0, 1, 1, 1, 1, 2, 1, 1, 0, 1, 1, 0,
0, 1, 1, 2, 1, 0, 1, 2, 0, 2, 1, 1, 1, 0, 0, 2, 1, 1, 1, 2, 0, 1, 1,
1, 1, 1, 2, 1, 2, 0, 1, 1, 0, 1, 0, 2, 1, 0, 2, 1, 2, 2, 0, 0, 0, 1,
1, 2, 1, 1, 1, 0, 2, 1, 0, 2, 2, 1, 1, 2, 1, 1, 1, 2, 0, 1, 1, 0, 1,
2, 2, 2, 0, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 0, 0, 2, 0, 2, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 0, 0, 1, 1, 0, 2, 1, 1, 1, 2,
2, 2, 1, 1, 0, 0, 2, 2, 1, 2, 2, 0, 2, 2, 2, 2, 0, 1, 2, 2, 2, 2, 0,
0, 0, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 0, 1, 2, 0, 0, 1, 1, 1,
0, 1, 1, 1, 0, 0, 1, 1, 2, 1, 0, 1, 0, 2, 2, 1, 2, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 2, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 2, 2,
2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 0, 0, 1, 0, 1, 2, 1, 0, 2, 0, 1, 1, 0,
1, 0, 1, 1, 0, 2, 0, 1, 2, 1, 1, 2, 2, 1, 2, 0, 2, 0, 2, 0, 0, 1, 1,
1, 1, 2, 1, 0, 2, 0, 1, 1, 0, 1, 2, 2, 2, 1, 0, 1, 2, 1, 2, 1, 2, 0,
0, 1, 0, 1, 1, 2, 0, 1, 1, 2, 1, 0, 1, 2, 1, 0, 2, 2, 2, 2, 2, 2, 1,
0, 2, 1, 2, 1, 1, 1, 1, 2, 0, 1, 1, 1, 2, 2, 1, 0, 1, 1, 2, 1, 1, 0,
1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 0, 2, 0, 2, 2, 1, 0, 1, 2, 1,
0, 2, 0, 0, 1, 0, 2, 1, 0, 2, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 2, 2,
0, 1, 2, 1 )
GENO <- matrix( g , nrow = n ,byrow=T)
GENO[GENO == -1 ] <- NA
Rplink <- function(PHENO,GENO,CLUSTER,COVAR)
{
f1 <- function(s) {
m <- glm( PHENO-1 ~ s , family="binomial" )
r <- summary(m)$coef[8]
c( length(r), r)
}
apply( GENO , 2 , f1 )
}
</pre>
In R, load this function
<h5>
source("plink.auto.R")
</h5></p>
and then try to run the <tt>Rplink</tt> function
<h5>
Rplink(PHENO,GENO,CLUSTER,COVAR)
</h5></p>
and you will see the error message
<pre>
Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1
</pre>
which indicates that <tt>R</tt> is expecting a 0/1 coding for this
particular function, not the default 1/2 coding used by PLINK for the
phenotype/dependent variable. You might therefore want to change the
relevant line of the function from
<pre>
m <- glm( PHENO ~ s , family="binomial" )
</pre>
to
<pre>
m <- glm( PHENO==2 ~ s , family="binomial" )
</pre>
for example. Then, repeating the above debug procedure, you would see in R
<h5>
Rplink(PHENO,GENO,CLUSTER,COVAR)
</h5></p>
gives
<pre>
[1] 0.25013412 0.60921037 0.02499268
</pre>
which are the correct p-values. So, now the function is fixed running
<h5>
plink --file mydata --R mylog.R
</h5></p>
would generate the same set of p-values as the PLINK
logistic command, in <tt>plink.auto.R</tt>
<pre>
1 snp0 10000 A 0.250134
1 snp1 10001 B 0.60921
1 snp2 10002 B 0.0249927
</pre>
This basic function could then be extended to return the coefficients also, or to use different analytic approaches
available in R.
<a name="rfunc3">
<h2>Setting up the Rserve package</h2></a>
</p>
First, you must ensure that you have <tt>Rserve</tt> installed on your
system. Normally, this will involve just typing, <b><em>at the R command
prompt (not the system shell prompt)</em></b>
<h5>
install.packages("Rserve")
</h5></p>
<strong>HINT</strong> For this to work, R must have been configured with
<tt>--enable-R-shlib</tt>.
</p>
When using any R-based PLINK plug-in, Rserve must be running in the
background before invoking the PLINK command. To start Rserve, just type
at the shell prompt
<h5>
R CMD Rserve
</h5></p>
(note, you may need to change <tt>Rserve</tt> to the full path of where
Rserve was installed), or, within R, type at the R prompt
<h5>
library(Rserve) <br>
Rserve()
</h5></p>
Please see the <a href="http://www.rforge.net/Rserve/doc.html">Rserve
documentation</a> for further support.
</td>
<td width=5%> </td>
</tr>
</table>
<em>
This document last modified Wednesday, 25-Jan-2017 11:39:28 EST
</em>
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