similar to: minor allele frequency comparison

Bottom Line Up Front: How does one reshape genetic 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

Hi, Following my earlier posts about having problems performing a PCA, I have worked out what the problem is. The problem lies within the PLINK to gds conversion. It seems as though the SNPs are imported as "samples" and in turn, the samples are recognised as SNPs: >snpsgdsSummary("chr2L") Some values of snp.position are invalid (should be > 0)! Some values of

Hello R-help community, I have another question about filtering datasets. Please consider the following "toy" data matrix example, called "x" for simplicity. There are 20 different individuals ("ID"), with information about the alleles (A,T, G, C) at six different loci ("Locus1" - "Locus6") for each of these 20 individuals. At any single locus

Hello All, Once again thanks for all of the help to date. I am climbing my R learning curve. I've got a few more questions that I hope I can get some guidance on though. I am not sure whether the etiquette is to break up multiple questions or not but I'll keep them together here for now as it may help put the questions in context despite the fact that the post may get a little long.

I am about one step away from heaven on earth. I think only one step! I am using dgc.genetics to run a TDT test on thousands of genetic loci. I have learnt (through the help of others on this mailing list) to send the complex output to useful data frames which in turn allow me to look at the big picture and screen the thousands of loci. Resultdt<-lapply(PGWide[,240:290], tdt) the above

I have a solution (actually a few) to this problem, but none are computationally efficient enough to be useful. I'm hoping someone can enlighten me to a better solution. I have data frame of chromosome/position pairs (along with other data for the location). For each pair I need to determine if it is with in a given data frame of ranges. I need to keep only the pairs that are within any of

Hello, I am working with a matrix of multilocus genotypes for ~180 individual snail samples, with substantial missing data. I am trying to calculate the pairwise genetic distance between individuals using the stats package 'dist' function, using euclidean distance. I took a subset of this dataset (3 samples x 3 loci) to test how euclidean distance is calculated: 3x3 subset used

> chromosomes id refseq name length 1 0 NC_000001.9 Homo sapiens chromosome 1 247249719 2 1 NC_000002.10 Homo sapiens chromosome 2 242951149 3 2 NC_000003.10 Homo sapiens chromosome 3 199501827 4 3 NC_000004.10 Homo sapiens chromosome 4 191273063 5 4 NC_000005.8 Homo sapiens chromosome 5 180857866 6 5 NC_000006.10 Homo sapiens chromosome 6

If I run an analysis which generates statistical tests on many SNPs I would naturally want to get more details on the most significant SNPs. Directly from within R one can get the information by loading RSNPer (from Bioconductor) and simply issuing a command SNPinfo(2073285). Unfortunately, the command cannot handle a vector and therefore only wants to do one at a time. I tried the lapply and

Dear all. I need to draw a scatter plot of 23 chromosome copy numbers (y axes) against chromosome and physical location within each chromosome in one plot. The data matrix looks as below: chr location copy_num 1 118345 1.320118 1 3776202 1.133879 1 4798845 0.989997 1 5350951 1.100967 . more data here . . 2 118345 2.459119 2 157739 1.915919 2 1530065 1.924372 2

I?m tryng to use the SPDEP package in my research in the field of population genetics. My data set has the following format: - x and y : coordinates, - Z: allelic frequency in each loci (in a total of 8 locis) - this variable can assume the values 0 ; 0.5 or 1. The objective is to verify if there is a possible spatial autocorrelation structure of the allelic frequency in a population of

#Hello, #I have a question about obtaining results from a loop I have written. #Below is a sample of individual genotypes from a genetic question I am working on called "P.genotype.sample ". P.genotype.sample<-matrix(10,10,10) P.genotype.sample[,1]<-c(2,2,1,5,1,1,5,6,1,3) P.genotype.sample[,2]<-c(6,3,3,6,8,1,6,7,2,3) P.genotype.sample[,3]<-c(2,2,2,3,3,2,2,2,3,3)

Hi all - I am NEW to R and NEW to any type of programming. I am making heatmaps using the heatmap.2 function within gplots package. At present, when the heatmap is plotted it uses the row identifiers as 1,2,3,4...etc. However, I much rather use my own labels. I was told my another well-versed R programmer to use the follow script: x<-as.matrix(test1[,-1]) ## skip column 1 rownames(x)<-

Dear R-Helpers, I am not very experienced in using lattice and I am still in the learning stage I have a data set which looks like this: (I have deleted a few lines in order to save space) Chromosome marker Marker.Name Distance 1 1 1 PeMm261 0.0000 2 1 2 Xtxp8 10.1013 .. 20 1 20 EbMi148 210.3099 21 1 21 Xtxp25

Hi, I am using the sim1000G R package to simulate data for case/control study. I can not figure out how to manipulate this code to be able to generate 10% or 50% causal SNPs in R. This is whole code provided as example on GitHub: library(sim1000G) vcf_file = "region-chr4-357-ANK2.vcf.gz" #nvariants = 442, ss=1000 vcf = readVCF( vcf_file, maxNumberOfVariants = 442 ,min_maf =

Dear all, Probably a very basic question but I need some help. I have a data frame (made by read.table from a text file) of microarray data, of which the first column is a factor and the rest of the columns are numeric. The factor column contains chromosome names, so values 1 through 22 plus X, Y and XY. The numeric columns contain positions or intensity measurements. What I need to do is

Hello, In a case control study how to calculate the risk allele or score allele. Regards GRR [[alternative HTML version deleted]]

Dear All, I am newbie to R, and I wanted to plot a barplots with R and in such a way that It will also show me position which I can plot on the bar line. Here is my code that I am using to plot, > chromosome <- c(40.2, 35.6, 36.1, 29.6, 31, 29.6, 31, 29.4, 28.2, 23, 23, 28.2) >barplot (chromosome, col="purple", xlab="Oryza sativa Chromosomes", border = NA, space =

Hello, I'm writing some genetic simulations in R where I would like to place genes along a chromosome proportional to the density of markers in a given region. For example, a chromosome can be presented as a list of marker locations: Chr1<-c(0, 6.5, 17.5, 26.2, 30.5, 36.4, 44.8, 45.7, 47.8, 48.7, 49.2, 50.9, 52.9, 54.5, 56.5, 58.9, 61.2, 64.1) Where the numbers refer to the locations of

My question is twofold. Part 1: My data looks like this: (example set, real data has 2*10^6 rows) binary<-c(1,1,1,0,0,0,1,1,1,0,0) Chromosome<-c(1,1,1,1,1,1,2,2,2,2,2) start<-c(12,17,18,20,25,36,12,15,16,17,19) Table<-cbind(Chromosome,start,binary) Chromosome start binary [1,] 1 12 1 [2,] 1 17 1 [3,] 1 18 1 [4,] 1