Here is the dataset you need to run this code..

http://mypage.iu.edu/~mirhan/msoutput.3932.100

thank you.


On 1/21/07, miraceti <miraceti@gmail.com> wrote:
>
> Hi,
> I am new to R.
> and even though I've made my code to run and do what it needs to .
> It is taking forever and I can't use it like this.
> I was wondering if you could help me find ways to fix the code to run
> faster.
> Here are my codes..
> the data set is a bunch of 0s and 1s in a data.frame.
> What I am doing is this.
> I pick a column and make up a new column Y with values associated with
> that  column I picked.
> then I remove the column.
> and see if any other columns in the data have a significant association
> with the Y column I've generated.
> If you see anything obvious that takes a long time, any suggestions would
> be appreciated.
> thanks a lot.
>
> Mira
>
>
> ----------------------------------------------------------------------------------------------
> #sub function for finding index
> rfind <- function(x)seq(along=x)[x*(1-x)>MAF*(1-MAF)]
>
> #sub function for permutation test
> perm.F = function(y,x,nperms,sites)
> {
>   maxF = c();
>   for (i in 1:nperms)
>   {
>     F=numeric(S)   #create an empty vector to store the F-values
>     newY=sample(y,length(y))     #permute the cancer types
>     newX = cbind(x, newY);
>     # anova for all sites
>     for ( i in sites )
>     {
>       a <- anova(lm(newY~factor(newX[,i])));
>       F[i] <- a$`F value`[1];
>     }
>     MSSid <- which (F == max(F)); # index of MSS (Most Significant Site)
>     maxF = cbind(maxF,max(F));
>   }
>   maxF;
> }
>
>
> # set the output file
> sink("/tmp/R.out.3932.100")
> # load the dataset
> snp = read.table(file("/tmp/msoutput.3932.100"))
> #print (snp);
>
> # pi: desired proportion of variation due to QTN
> pi = 0.05;
> print (paste("pi:", pi));
> MAF = 0.05;
> print (paste("MAF:", MAF));
> # S: number of segregating sites
> S = length(snp[1,]);
> # N: number of samples
> N = length(snp[,1]);
> Dips = sample(1:N,N)
> DipA = Dips[1:50]
> DipB = Dips[51:100]
> disnp = snp[DipA,]+snp[DipB,]
> snp = as.data.frame(disnp, row.names=NULL);
> N = length(snp[,1]);
>
> # get allele freq for all SNPs
> allele_f <- mean(snp[,1:S])/2;
> print (allele_f);
> sites = rfind(allele_f);
> print(sites);
>
> # collapse sites that have perfect correlation
> newsites <- sites;
> for (i in 1:(length(sites)-1))
> {
>   for (j in (i+1):length(sites))
>   {
>     test = (snp[sites[i]] == snp[sites[j]])
>     if ( all(test) || all(!test) )
>     {
>       print (paste("perfect correlation with", sites[i]));
>       print (paste("removing alleles", sites[j]));
>       newsites <- newsites[newsites!=sites[j]];
>     }
>   }
> }
> sites <- newsites;
> print(sites);
>
> # QTN: the site nearest right to S/4
> sitesid = floor(length(sites)/4);
> QTNid = sites[sitesid];
> QTN = snp[,QTNid];
>
> print (paste("QTN:", names(snp[QTNid])));
> print (QTN);
>
> # remove QTN from sites
> sites <- sites [ sites != QTNid ];
> print(sites);
> print (paste("Number of usable SNPs:", length(sites)));
>
> # p: allele frequency of QTN
> p0 = allele_f[QTNid];
> p = min(p0, 1-p0);
> print (paste("QTN_freq:", p));
>
> # z: random normal deviate
> z = rnorm(N, mean = 0, sd = 1);
> # foreach sample give quantitative phenotype
> # each row is a sample
> # phenotype value depends on QTN genotype, pi, p, and z
> Y <- sqrt(10-(10*pi))*z + QTN*sqrt((10*pi)/(2*p*(1-p)));
> snp = data.frame(cbind(snp, Y));
> # anova for QTN
> df=data.frame(Y=Y, QTN=factor(QTN));
> QTN_a <- anova(lm(Y~QTN, data=df));
> print (QTN_a);
> SSB <- QTN_a$`Sum Sq`[1];
> SSW <- QTN_a$`Sum Sq`[2];
> QTN_PRE <- SSB / (SSB + SSW);
> print (paste("var_QTN/var_tot:", QTN_PRE));
>
> # anova for all sites
> F=numeric(S)   #create an empty vector to store the F-values
> Pval=rep(1,S)   #create an empty vector to store the Pval
> PRE=numeric(S)   #create an empty vector to store the PRE
>
> for ( i in sites )
> {
>   a <- anova(lm(Y~factor(snp[,i])));
>   print (a);
>   F[i] <- a$`F value`[1];
>   Pval[i] <- a$`Pr`[1];
>   SSB <- a$`Sum Sq`[1];
>   SSW <- a$`Sum Sq`[2];
>   PRE[i] <- SSB / (SSB + SSW);
>
> }
> print (paste("Max F:", max(F)));
> MSSid <- which (F == max(F)); # index of MSS (Most Significant Site)
> MSS = snp[,MSSid];
> print (paste("MSS(Most Significant Site):", MSSid));
> p0 = length(MSS[MSS==0])/N;
> p = min(p0, 1-p0);
> print (paste("assoc_freq:", p));
> print (paste("assoc_var:", PRE[MSSid]));
> #lets do a permutation test
> Fdist <- perm.F(Y, snp[,1:S], 1000, sites);
> print ("permutation test maxF dist");
> print (Fdist);
> pvalue <- mean(Fdist>F[MSSid]);
> print (paste("assoc_prob:", pvalue));
>
> # close the output file
> sink()
>
> --------------------------------------------------------------------------------------------------
>
>

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