search for: phenotype

Hello all, I'm having difficulty with setting up a mixed model using lme in the nlme package. To summarize my study, I am testing for effects of ornamentation on foraging behavior of wolf spiders. I tested spiders at two different ages (penultimate vs. mature) and of two different phenotypes (one species tested lacks ornamentation throughout life [non-ornamented males] while the other acquires ornamentation upon maturation [i.e. brush-legged males]). I tested a sample of brush-legged and non-ornamented males (as both penultimates and matures) in 2009, and an additional sample of brush...

Dear R users, Could you help my with the following problem? I want to repeat a glm analysis with 2 independent variables for all 900 variables (snps) in my data set. So, I want to check whether snp1 has a different effect on my outcome variable in patients and controls(phenotype). And repeat that for snp2 to snp900. Is there an easy way to get a summary of the data, e.g. a list of P values of all 900 variables? I tried something with a loop: for (i in 1:length(data)) { print (summary (glm (outcome~data[[i]]*phenotype, data=data))) } # This works, but gives 900 written s...

Hey, I have code that can check the quality of a data set we're working with (expression data), and I'm having some trouble writing code that would make the expression data we have tie to other data we want to link it to (called phenotype data). Does anyone have any advice on how I could make a single object that would do this? Other relevant info: I want to use the pdata() function, and I have all the phenotype data available as csv files. Here is what I have in regards to the expression data: library(affy) library(arrayQuality...

Dear R-experts, My problem is how to handle a 10GB data file containing genotype data. The file is in a particular format (Illumina final report) and needs to be altered and merged with phenotype data for further analysis. PERL seems to be an frequently used solution for this type of work, however I am inclined to think it should be doable with R. How do I open a text-file, line by line, evaluate it and write it back into a textfile in a different position; Phenotypeinfo.txt (contains p...

...ear all,   I have data from the following experimental design and trying to fit a mixed model with lme function according to following steps but struggling. Any help is deeply appreciated.   1) Experimental design: I have 40 plants each of which has 4 clones. Each clone planted to one of 4 blocks. Phenotypes were collected from each clone for 3 consecutive years. I have genotypes of plants. I need to relate phenotype to genotype.   2) I am reading data from a file with “read.table” function. Then grouping data as: my.Data<-groupedData( phenotype ~ Block | PlantID, data = as.data.frame( Data ) )  ...

I have a path: path = "/nfs/users/nfs_n/ns9/ Phenotype Analysis/Results/Run_AmplRatio_neg BinaryAll trained without akapn+tnik.csv" I wish to replace the spaces with "\ " so that it can be read by a system call to unix. Using gsub I try: > gsub(" ","\\ ",path) [1] "/nfs/users/nfs_n/ns9/Phenotype Analysis/Re...

...ut "Error: element 12 is empty". I'm wondering if my syntax is incorrect within legend.list. If anyone has any suggestions to sees something obvious that I am missing, I would greatly appreciate any help. Many Thanks, Patrick > # These are the symbols and colors to use for each phenotype in the model and test sets > # model samples: square symbols > # color symbol phenotype > legend.list <- c("green", 22, # ALL-B + "steelblue", 22, # ALL-T + "...

Hello R Forum users, I was hoping someone could help me with the following problem. Consider the following "toy" dataset: Accession SNP_CRY2 SNP_FLC Phenotype 1 NA A 0.783143079 2 BQ A 0.881714811 3 BQ A 0.886619488 4 AQ B 0.416893034 5 AQ B 0.621392903 6 AS B 0.031719125 7 AS NA 0.652375037 "Accession" = individual plants, arbitrarily identified by unique numbers "SNP_"...

Dear experts, I am trying to perform an association using snpStats. I have a snp matrix called 'plink' which contains my genotype data (as a list of $genotypes, $map, $fam), and a phenotype data frame which contains the outcomes (outcome1, outcome2,...) I would like to associate with the genotype. My question is, how do I loop through the outcomes? This type of data seems different from all the others and I am having trouble manipulating it, so I would also be grateful if you have sug...

Dear all, I have two data frames- x1 and y1 with same row names and column names(actually the names of the patients). x1 a b c d e a 1.0000000 0.4730679 0.6226994 0.6036036 0.6433333 b 0.4730679 1.0000000 0.6227273 0.6303855 0.5730858 c 0.6226994 0.6227273 1.0000000 0.7290503 0.6900585 d 0.6036036 0.6303855 0.7290503 1.0000000

Hi, I'm looking for an efficient code that will enable me to reshuffle data (phenotype) for certain number of individuals and creating a loop that will randomly simulate it for 10000 times *(permutation)*. I also need to find how I keep the information (p value for each SNP) gathered for all the 10000 iterations. My data set looks like this (n=500): Individual # Phenotype SNP1 S...

Hi, My query is regarding permutation test and reshuffling of genotype/phenotype data I have been using the haplo.stats package of R. for haplotype analysis and I would like to perform an analysis which I'm requesting your advice. I have a data set of individuals genotyped for 12 SNP and a dichotomous phenotype. At first, I have tested each of those SNP independently in ord...

Hello, I have data in a dataframe with 139104 rows which is multiple of 96x1449. i have a phenotype file which contains the phenotype information for the 96 samples. the snp name is repeated 1449X96 samples. I haveto merge the two dataframes based on sid and sen. this is how my two dataframes look like dat<-data.frame(snpname=rep(letters[1:12],12),sid=rep(1:12,each=12), genotype=r...

...y. getInteractions2 = function(data, fSNPcol, ccCol) { #fSNPcol is the number of the column that contains the first SNP #ccCol is the number of the column that contains the outcome variable require(lmtest) a = data.frame() snps = names(data)[-1:-(fSNPcol-1)] names(data)[ccCol] = "PHENOTYPE" terms = as.data.frame(t(combn(snps,2))) attach(data) fit1 = c() fit2 = c() pval = c() for(i in 1:length(terms$V1)) { fit1 = glm(PHENOTYPE~get(as.character(terms$V1[i]))+get(as.character(terms$V2[i])),family="binomial") fit2 = glm(PHENOTYPE~get(as.char...

....05. How can I cite the P-valus from lm result ? Ping The code: #using LM to model general fati for (j in 48:52) { for (i in 3:46){ gen.fat<-y_x[,j] gen.fat<-as.numeric(gen.fat) snp_marker<-y_x[,i] x<-colnames(y_x) #snp_marker<-as.matrix(snp_marker) #mode(snp_marker) cat("phenotype is = ",x[j] , "\n") cat("snp marker is = ",x[i] , "\n") zz<-summary(lm.D9 <- lm(gen.fat~snp_marker)) print(zz) return } } [[alternative HTML version deleted]]

...I am currently writing code to input a few thousand files, run them through the Random Forests package, and then output corresponding results. When I use the code below: zz<-textConnection("ex.lm.out", "w") sink(zz) tempData<-read.delim(paste("allSnps",1,"Phenotype.phn",sep=""),header=TRUE,sep=",",quote="\"",dec=".") tempData[[1]]<-factor(tempData[[1]]) tempData.rf<-randomForest(tempData[[1]]~.,data=tempData,importance=TRUE,proximity=TRUE,outscale=TRUE,replace=TRUE) tempData.rf zz<-file(paste("ex...

...etermination of breakpoints and comparison of slopes from broken-line regression models. Although this is rather a standard problem in data analysis, all information I gathered so far, did not answer my questions. I added a subsetted example of my data. Basically it is a timeseries of recorded phenotypes in three different groups of plants. You will see in the plot that the phenotype between groups behaves differently over time. Plotted data of Group 3 look like a nice linear relationship between phenotype (y) and time (x). Group 1 seems to have 1 breakpoint (at x=31) and Group 3 has 2 breakpoi...

In this years biostat teaching I will include Rcommander (it indeed simplifies syntax problems that makes students frequently miss the core statistical problems). But I could not find how to make a simple chisquare comparison between observed frequencies and expected frequencies (eg in genetics where you expect phenotypic frequencies corresponding to 3:1 in standard dominant/recessif

Dear R-helpers & bioconductor Sorry for cross-posting, this concerns R-programming stuff applied on Bioconductor context. Also sorry for this long message, I try to be complete in my request. I am trying to write a subset method for a specific class (ExpressionSet from Bioconductor) allowing selection more flexible than "[" method . The schema I am thinking for is the following:

...y <- data.frame(pid = c(1,1,1,2,2,2,2,3,3,3), id = c(1,2,3,1,2,3,4,1,2,3), age = c(55,50,22,38,37,15,11,42,41,17), weight = c(185,170,130,165,170,90,60,170,160,120)) y1 <- as.phe(y) 1. I first consider a model with the disease as a phenotype, and two SNPs as predictors (on covariates) as bellow: pbat.m(AffectionStatus ~ NONE, y1, x1, fbat="gee", distribution='categorical', offset='none') But some error messages were returned: Error in writeCommandStrMatch("distribution", distribution, c("defau...