similar to: Questions on 'lme' function, urgent!

Hi, I am doing an analysis to see if these is tissue specific effects on the gene expression data . Our data were collected from 6 different labs (batch effects). lab 1 has tissue type 1 and tissue type 2, lab 2 has tissue 3, 4,5,6. The other labs has one tissue type each. The 'sample' data is as below:

Hi, I have a question about how to do covariate adjustment. I have two sets of 'gene expression' data. They are from two different tissue types, 'liver' and 'brain', respectively. The purpose of my analysis is to compare the pattern of the whole genome 'gene expression' between the two tissue types. I have 'age' and 'sex' as covariates. Since

Dear fellow R-help members, I hope to seek your advice on how to parse/manage a dataset with hundreds of columns. Two examples of these columns, 'cancer.problems', and 'neuro.problems' are depicted below. Essentially, I need to parse this into a useful dataset, and unfortunately, I am not familiar with perl or any such language. data <- data.frame(id=c(1:10))

Hello, I'm using aov() to analyse changes in brain volume between males and females. For every subject (there are 331 in total) I have 8 volume measurements (4 different brain lobes and 2 different tissues (grey/white matter)). The data looks like this: Subject Sex Lobe Tissue Volume subect1 1 F g 262374 subect1 1 F w 173758 subect1 1 O g 67155 subect1 1 O w 30067 subect1 1 P g 117981

I'm working with a nested model (mixed). I have four factors: Patients, Tissue, sex, and tissue_stage. Totally I have 10 patients, for each patient, there are 2 tissues (Cancer vs. Normal). I think Tissue and sex are fixed. Patient is nested in sex,Tissue is nested in patient, and tissue_stage is nested in Tissue. I tried aov and lme as the following, > aov(gene ~ tissue + gender +

Dear R users, I have a data frame in the form below, on which I would like to make normality tests on the values in the ExpressionLevel column. > head(df) ID Plant Tissue Gene ExpressionLevel 1 1 p1 t1 g1 366.53 2 2 p1 t1 g2 0.57 3 3 p1 t1 g3 11.81 4 4 p1 t2 g1 498.43 5 5 p1 t2 g2 2.14 6 6 p1 t2 g3 7.85 I

My small brain is having trouble getting to grips with lme() I wonder if anyone can help me correctly set the random = argument to lme() for this kind of setup with (I think) 9 variance/covariance components ... Study.1 Study.2 ... Study.10 Treatment.A: subject: 1 2 3 4 5 6 etc. 28 29 30 Treatment.B: subject: 31

Hello, I've used this command to analyse changes in brain volume: mod1<-aov(Volume~Sex*Lobe*Tissue+Error(Subject/(Lobe*Tissue)),data.vslt) I'm comparing males/females. For every subject I have 8 volume measurements (4 different brain lobes and 2 different tissues (grey/white matter)). As aov() provides only type I anovas, I would like to use lmer() with type II, however, I have

Hi, There are 20 subjects grouped by Gender, each subject has 2 tissues (normal vs. cancer). In fact, it is a 2-way anova (factors: Gender and tissue) with tissue nested in subject. I've tried the following: Model 1: lme(response ~ tissue*Gender, random = ~1|subject) Model 2: response ~ tissue*Gender + subject Model 3: response ~ tissue*Gender It seems like Model 1 is the correct one

Dear all, I would like to know if it is possible to fit in R a Cox ph model with time-dependent covariates and to account for hierarchical effects at the same time. Additionally, I'd like also to know if it would be possible to perform any feature selection on this model fit. I have a data set that is composed by multiple marker measurements (and hundreds of covariates) at different time

Dear R users, I have a huge database and I need to adjust it somewhat. Here is a very little cut out from database: CODE NAME DATE DATA1 4813 ADVANCED TELECOM 1987 0.013 3845 ADVANCED THERAPEUTIC SYS LTD 1987 10.1 3845 ADVANCED THERAPEUTIC SYS LTD 1989 2.463 3845 ADVANCED THERAPEUTIC SYS LTD 1988 1.563 2836 ADVANCED TISSUE

Hello, unfortunately I have I big problem I can't solve. I have to analyse if a gene is tissue specific. For example for the gene xyz I have following expression values: Heart Liver Brain 8.998497 10.013561 12.277407 9.743556 10.137574 11.033957 For every tissue I have two values from two different experiments. Now I want to test if Heart is significant higher

Dear all I apologize for cross-posting, but first it is accepted custom to thank the repliers and give a summary, and second I have still the feeling that this problem might be a general statistical problem and not necessarily related to microarrays only, but I might be wrong. First, I want to thank Robert Gentleman, Mark Kimpel and Mark Reiners for their kind replies. Robert Gentleman kindly

I am trying to sample a subset from a matrix using sample. The size of the matrix is 20X 1532. It works fine with this, but when I transpose the matrix and try to sample it, it returns null. pick.set<-sample(tissue.exp.t,5,replace=FALSE,prob=NULL) Is there something that I am missing here ? Thanks ../Murli

Hello, Excuse me for posting two questions in one day, but I figured it would be better to ask my questions in separate emails. I will again give the caveat that I'm not a statistician by training, but have a fairly decent understanding of probability and likelihood. As before, I'm trying to fit a nonlinear model to a dataset which has two main factors using nlme. Within the dataset

mybp <- boxplot(count ~ geno * tissue, data = mydata, plot = FALSE) mybp$names <- gsub("\\.", "\n", mybp$names) bxp(mybp) See ?boxplot for details. Best, Ista On Thu, Sep 28, 2017 at 12:40 PM, Ed Siefker <ebs15242 at gmail.com> wrote: > I have data I'd like to plot using the formula interface to boxplot. > I call boxplot like so: > > with(mydata,

Hi, I've lost my mind on it... I have to scatterplot two vectors, grouped by a third variable, with two different dimensions according to whether each cell line in the plot is sensitive or resistant to a given drug, and with a different color for each of 9 tissues of origin. Here's what I've done:

I have data I'd like to plot using the formula interface to boxplot. I call boxplot like so: with(mydata, boxplot(count ~ geno * tissue)) I get a boxplot with x axis labels like "wt.kidney". I would like to change the '.' to a newline. Where is this separator configured? Thanks, -Ed

hi, everyone: i have a question on the reshape function. i have the following dataset : gene tissue patient1 patient2 patient3............. _________________________________________________ gene1 breast 10 20 50 gene2 breast 20 40 60 gene3 breast 100 200 300 which i hope to convert to the following format: gene patientID

dear members, i would like to write a variable in a plot title (main="") but i don't know the right syntax:(...i tried a lot of different ways without success. here my example: y=30 z=33 for (i in 10:length(tissue)) { png(filename = tissues[i], width = 1024, height = 768, pointsize = 12, bg = "white") gene.graph("ENSG00000115252", rma.affy, gps=list(1:3,