search for: tissues

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, 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 subect1 1 P w 85441 subect1 1 T g 185241 subect1 1 T w 83183 subect2 1 F g 255309 subect2 1 F w 164335 subect2 1 O g...

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:

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 + patients%in%gender + stage%in%tissue >lme(gene ~ tissue + gender, random...

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 struggled to find the right syntaxis. How should I write the model I use with aov() using lmer()?? Specifying Subject as a random effect is straightforward mod2<-lmer(Volume~S...

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

Hello, I'm fitting linear mixed models to gene-expression data from microarrays, in a data set where 4608 genes are studied. For a sample of 5 subjects and for each gene we observe the expression level (Intensity) in four different tissues: N, Tp, Tx and M. I want to test whether the expression level is different accross tissues. Between-subject variability is modeled with a random intercept, and the within-subject by allowing heteroscedastic and correlated errors accross tissues. The proposed model can then be fitted by lme(Inte...

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

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))

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 Mod...

...ze" correlation coefficients from different sample sets? Could an expression such as "corr * (1 - pval)" be used for normalization? Maybe, it is not possible to normalize correlation coefficients? Would a barplot comparing the correlation coefficients between two genes for different tissues be meaningful? (Alternatively, I have tried to use (1-pval) to calculate the gray-level of the bars.) Any further suggestions would be appreciated very much. Best regards Christian Stratowa -----Original Message----- From: Stratowa,Dr.,Christian FEX BIG-AT-V Sent: Monday, July 19, 2004 15:00 T...

Hi Dear all, I have some gene expression data samples from different tissue types ----------------------------------------------- - 120 samples from blood (B) - 20 samples from Liver (L) - 15 samples from Kidney (K) - 6 samples from heart (H) ----------------------------------------------- All the samples are from different individuals, so there are in total 161 individuals from which the DNA was

...marker measurements (and hundreds of covariates) at different time points from different tissue samples of different patients. Suppose that the data were coming from animal model with very few subjects (n=6) that were followed up given a pathogen exposure, measured several times, sampling different tissues in the same days, until a certain outcome was reached (or outcome censored). Suppose that the pathogen can vary over time (might be a bacteria that selects for drug-resistance) and that also it can vary across different tissue reservoirs within the same patient. In other words: names(data) = patie...

...r each Type/Tissue combination. Nevertheless, I *think* this is doing what I want. Given my limited stats training, I also like the fact that the Corr b/w aL and aN is very low. Moving on, the second model I would like to fit assumes that the population of aN and aL values across the set of Tissues differ between Types. I have tried a number of different syntaxes, but I can't seem to get the output I was expecting. For example, if I run ------------------------------------------ > model2 = nlme(Count ~ quad.PBMC.model(aL, aN, T0), + data = tissueData, + start = list( fixe...

...efficients from different > sample sets? Could an expression such as "corr * (1 - pval)" be used > for normalization? Maybe, it is not possible to normalize correlation > coefficients? Would a barplot comparing the correlation coefficients > between two genes for different tissues be meaningful? (Alternatively, > I have tried to use (1-pval) to calculate the gray-level of the bars.) > > Any further suggestions would be appreciated very much. > > Best regards > Christian Stratowa > > -----Original Message----- > From: Stratowa,Dr.,Christian FEX BIG...

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 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, y:z), type="mean-int", gp.col=c("red", "blue"), by.order=TRUE, scale.to.gene=FALSE, use.symbol=TRUE, use.mt=FALSE, *main="PDE...

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

Hello, I am using Mechanize to post a form to a website. When I do this by hand in my browser the response takes about 35s to come back (it''s a long page full of tables and graphics). When I do this with Mechanize, the server starts to respond and then appears to hang. The obvious conclusion is that my code is wrong but I am reasonably sure that I haven''t altered it