similar to: Help with correlation matrices, thresholding

I am in need of someone's help in correlating gene expression. I'm somewhat new to R, and can't seem to find anyone local to help me with what I think is a simple problem. I need to obtain pearson and spearman correlation coefficients, and corresponding p-values for all of the genes in my dataset that correlate to one specific gene of interest. I'm working with mouse Affymetrix

Dear All, I am not familiar with R yet I want to use it to perform some task, hence my posting here. I hope someone can help. I have a set of data, genes (rows) and samples (columns). I want to do a Pearson correlation on all the possible pairwise combinations of all the genes (2000). Does anyone have an idea of how to execute this in R? Thanks in advance. -- View this message in context:

Dear All, I have two matrices A (40 x 732) and B (40 x 1230) and would like to calculate correlation between them.  I can use: cor(A,B, method="pearson") to calculate correlation between all possible pairs. But the issue is that there is one-many specific mappings between A and B and I just need to calculate correlations for those pairs (not all). Some variables in A (proteins, say p1)

I have the following problem. I want to use pairs function and get a matrix of scatterplots with the correlations in the upper panel and the ordinary scatterplots in the lower panel. Moreover, I want to have points colored in five differet ways in the lower panel, because I have five subgroups. In order to do that I tried to combine examples on pairs function help. I got a colored matrix

Hi Folks, I'm analysing some data which, in its simplest aspect, has 3 factors A, B, C each at 2 levels. If I do lm1 <- lm(y ~ A*B) say, and then summary(lm1, corr=T) I get the correlation matrix of the estimated coeffcients with numerical values for the correlations (3 coeffs in this case). Likewise with 'glm' instead of 'lm'. However, if I do lm2 <- lm(y ~

Dear All, I have a correlation matrix say 'M' (4000x4000) for 4000 genes and I want to subset it to 'N' (190x190) for 190 genes. The list of those 190 genes are in variable 't'. So the idea is to read the names of genes from variable 't' and subset the matrix M accordingly. Any thoughts are welcome! Best Lee [[alternative HTML version deleted]]

Hi, You may try: set.seed(49) m1 = matrix(rnorm(30), nrow = 3) m2 = matrix(rnorm(30), nrow = 3) ?corsP<-vector() ? for(i in 1:3) corsP[i] =? cor(m1[i,], m2[i,]) ?corsP #[1]? 0.58411274 -0.02382329? 0.03760757 diag(cor(t(m1),t(m2))) #[1]? 0.58411274 -0.02382329? 0.03760757 #or mNew<- rbind(m1,m2) ?indx<-rep(seq(nrow(mNew)/2),2) ?sapply(split(seq_len(nrow(mNew)),indx),function(x)

Hello! I have this problem: I want to create a Venn's diagram with three lists of genes'names. The first is all the genome, the second a subset of it comprising all mitochondrial genes, and the third including all genes that correlate with a given gene. This is what I do: > library(gplots) > A<-read.delim("F:/.../mito genes just names.txt") >

Hello, here is the problem: I want to demonstrate that, on average, the Pearson's correlations of a specified subset of genes from a huge list (>18,000 columns) are higher than any randomly chosen subset of that list. I would therefore like to do a number of tests between that specified subset and randomly chosen ones from the "mother" list. How could I do that? What would be

List members, Via searches I've seen similar discussion of this topic but have not seen resolution of the particular issue I am experiencing. If my search on this topic failed, I apologize for the redundancy. I am attempting to generate random covariance matrices but would like the corresponding correlations to be uniformly distributed between -1 and 1. The approach I have been using is:

Hello, I am using R to look at whole-genome gene expression data. This means about 27,000 genes, each with a vector of numbers reflecting expression at different tissues and times. I need to do an all against all co-expression calculation (basically, just calculate Pearson's r for every gene-gene pair). I try to store the result of such a thing in a 27000x27000 matrix, but r seems not to like

Thanks for the advice. My question is more on how to do this? Let me use a biology gene analysis example to illustrate: In biology, there are always some house keeping genes which differ little even at pathological conditions. We know that at different batches, there are external factors affect the measurements. For example, overall signal intensity might be different due to lab reagents. A

Hi there, When I make two correlations matrices of the same size from a <- cor(el[1:20]) b <- cor(fl[1:20]) how can I compare if this matrices are equal? Thanks, Martin -- Ihr Partner f?r Webdesign, Webapplikationen und Webspace. http://www.roomandspace.com/ Martin Kaffanke +43 650 4514224 -------------- next part -------------- A non-text attachment was scrubbed... Name: not available

Dear R-users, we are happy to announce the release of our R package parcor. The package contains tools to estimate the matrix of partial correlations based on different regularized regression methods: Lasso, adaptive Lasso, PLS, and Ridge Regression. In addition, parcor provides cross-validation based model selection for Lasso, adaptive Lasso and Ridge Regression. More details can be found

Dear R-users, we are happy to announce the release of our R package parcor. The package contains tools to estimate the matrix of partial correlations based on different regularized regression methods: Lasso, adaptive Lasso, PLS, and Ridge Regression. In addition, parcor provides cross-validation based model selection for Lasso, adaptive Lasso and Ridge Regression. More details can be found

Dear R-Users, I am currently looking for a way to test the equality of two correlations that are related in a very special way. Let me describe the situation with an example. - There are 100 respondents, and there are 2 points in time, t=1 and t=2. - For each of the respondents and at each of the time points, I have information on 10 X-variables and on 10 Y-variables. - Based on this

Hello, If I have two correlation matrices (e.g. one for each of two treatments) and then perform cor() on those two correlation matrices is this third correlation matrix interpreted as the correlation between the two treatments? In my sample below I would interpret that the treatments are 0.28 correlated. Is this correct? > var1<- c(.000000000008, .09, .1234, .5670008, .00110011002200,

Hi everyone, I am new to R-project. I did search through the list for my problem but i can't find it. I am sorry if this question has been asked. I would like to perform a correlation analysis between a hiv data and gene expression. Basically, i have a file that contains: hiv_name, start_position, end_position, chromosome. I would like to see if these data has anything to do with the

Given four known and fixed vectors, x1,x2,x3,x4, I am trying to generate a fifth vector,z, with specified known and fixed partial correlations. How can I do this? In the past I have used the following (thanks to Greg Snow) to generate a fifth vector based on zero order correlations---however I'd like to modify it so that it can generate a fifth vector with specific partial

Hi Now a more theoretical question. I have two correlation matrices - one of a set of variables under a particular condition, the other of the same set of variables under a different condition. Is there a statistical test I can use to see if these correlation matrices are "different"? Thanks Mick