Displaying 4 results from an estimated 4 matches for "allele2".
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2006 Apr 26
2
help in R
...hd/bs871/hw/genfile.txt",nline=1,what="character")
Read 100 items
> n.snp <- length(snp.name)
> n.id <- 1 #number of fields for ids, sex and affection status
>
> ###form gntp using the two alleles of each SNP
> allele1 <- snp.dat[,seq(1,2*n.snp,2)+n.id ]
> allele2 <- snp.dat[,seq(2,2*n.snp,2)+n.id ]
> temp <- matrix(paste(allele1,allele2,sep="|"),dim(allele1))
> temp <- data.frame(temp)
> convt <- function(x) x <- factor(as.character(x),exclude="0|0")
>
> gntp <- as.data.frame(lapply(temp,convt))
>
&g...
2010 Nov 03
0
how to handle 'gwaa@gtdata' ?
...'s easy to add these
rows, but for 'gwaa at gtdata', I think I need to create SNP data as '0 0 0 0
0.....' for all the dummy parents first. I am using the function
'convert.snp.ped', so I need a 'pedfile' of this format:
#ped id fa mo sex trait snp1.allele1 snp1.allele2 snp2.allele1 snp2.allele2
...#
1 1 0 0 1 2 0 0 0 0 ...
1 2 0 0 1 0 0 0 0 0 ...
1 3 0 0 2 1 0 0 0 0 ...
.
.
100 101 0 0 2 1 0 0 0 0 ...
If we use the 1M microarray, usually, after QC, there will be ~800 thousands
SNPs, so this file is really huge. I created this matrix in R, and then try
to export...
2008 Apr 09
2
GLM fitting in R and Statistica
...tatistica clicking on "Estimate" button instead of "1LR"
(what does my supervisor which gives only log-likelihood and p of
variables but doesn't estimate parameter).
But there is still one problem I can't explain. When we fit
interaction terms (site x allele1, site x allele2 and so on) the
results are completely different. I tried several different contrasts
in R, such as contr.SAS, contr.treatment etc but I couldn't get
nothing similar to Statistica output.
Have anybody any idea how to deal with that? Or how to explain why R
results are different (and hopeful...
2006 Apr 06
4
Reshaping genetic data from long to wide
...enetic data from long to wide?
I currently have a lot of data. About 180 individuals (some
probands/patients, some parents, rare siblings) and SNP data from 6000 loci
on each. The standard formats seem to be something along the lines of Famid,
pid, fatid, motid, affected, sex, locus1Allele1, locus1Allele2,
locus2Allele1, locus2Allele2, etc
In other words one human, one row. If there were multiple loci then the
variables would continue to be heaped up on the right. This kind of
orientation, shall be referred to as "wide".
Given how big my dataset is, it is easier to manage the data in the...