similar to: Logistic regression to select genes and estimate cutoff point?

Hi, all, Anyone has experience on Logistf package? I am using logistftest in Logistf package to selelct diagnosis genes. The result seems not the same as I expected. I have 10 gene expression data for 27 tumor 1 and 11 tumor 0. I want to select the best one using Maximum likelihood ratio test in logistic regression model. This is the way my code works: 1. Read in 10 genes as independent

Hi All, I have an CBS segmentation algorithm output for 10 tumor samples each from 2 different tumors. Now, I am in an urgent need to assign gene (followed by all genes present) that belong to a particular segment after I removed all the CNVs from segment data. The format of the data is: Sample Chromosome Start End Num_Probes Segment_Mean Sample1A-TA 1 51598 76187 15

Dear R gurus I?m trying to use the GSCA package to a series of microarray data (prostate cancer normal vs tumor (29 vs 29 paired)) but I?m running into some problems. I have a matrix (named /data_final/) with 11k rows(genes) and 60 cols (58 samples (29N vs 29T), GO IDs, KEGG IDs). I also have a separate vector GS with the GO IDs mapped to genes (no duplicate genes but multiple IDs per gene like

Hello everyone, I have two problems which I am unable to solve : 1.I am trying to add the  row labels (g1-g2000) to the very left of a data table. The data table is 2000 rows  by 62 columns.I have used the following code. read.table(file="C:\\Documents and Settings\\Owner\\My Documents\\colon cancer1.txt",header=T,row.names=1) rowname(dat) <- paste("g", c(1:nrow(dat)),

Hi, I am relatively new to R and Bioconductor and am trying to filter the topTable that I generated of differentially expressed genes from my normlized eset file comprised of ~ 40 HG-133A Affy microarrays . I would like to see if particular probesets are represented in this list. Alternatively I would like to generate a topTable of differentially expressed genes using only specified probesets

Hi, I understand that dichotimization of the predicted probabilities after logistic regression is philosophically questionable, throwing out information, etc. But I want to do it anyway. I'd like to include as a measure of fit % of observations correctly classified because it's measured in units that non-statisticians can understand more easily than area under the ROC curve, Dxy, etc.

Hi, I am trying to plot the following data so that it can be visually represented well. I tried the dotchart but I felt it was too spread out. Then I tried the barplot which is good enough for me. Is there a way to give the labels for the y-axis as in the dot chart? Also, I feel the grey level is confusing, so is there options for designs within the bars? I cannot use color as the journal wants

I have a logistic model fitted with the following R function: glmfit<-glm(formula, data, family=binomial) A reasonable cutoff value in order to get a good data classification (or confusion matrix) with the fitted model is 0.2 instead of the mostly used 0.5. And I want to use the `cv.glm` function with the fitted model: cv.glm(data, glmfit, cost, K) Since the response in the fitted

Hello, I didn't give enough information when I sent an query before, so I'm trying again with a more detailed explanation: In this data set, each patient has a different number of measured variables (they represent tumors, so some people had 2 tumors, some had 5, etc). The problem I have is that often in later cycles for a patient, tumors that were originally measured are now missing (or

Hi, I am having troubles with creating an eSet and would appreciate any help on the following problem. I am trying to create an eSet using the following code pd <- read.table(file="pdata.txt",header =TRUE,row.names=1); colnames(pd) <- c("type","tumor","time","id"); pdN <- list(type =

Dear all, please could you advise me on the following : I've written a R script that reads 3 arguments from the command line, i.e. : " args <- commandArgs(TRUE) TUMOR <- args[1] GERMLINE <- args[2] CHR <- args[3] ". when I submit the R script to a PBS HPC scheduler, I do the following (below), but ... I am getting an error message. (I am not posting the error message,

Hi, The problem is most likely, you need to call a R CMD BATCH with your arguments and the R-script inside of a shell script that you submit to your qsub. Unfortunately we don't use qsub anymore so can't test it, but it should be as follows: R-script eg. test.R: > ##First read in the arguments listed at the command line > args=(commandArgs(TRUE)) > > ##args is now a list of

Hi! I would like to select probes (affy expression set) of genes that are "cancer-related". Conventionally the decision whether a gene is cancer-related or not is made by looking up the literature. Since this is not possible for all genes on the array I wonder if there is a way of doing this automatically? Best wishes Kristian -- _____ Dr Kristian Unger Imperial College London

This sounds like an operating system specific question, in that "submit the R script to a PBS HPC scheduler" would be the kind of action that would run R with very different environment variables and possibly different access credentials than your usual interactive terminal. A thorough reading of the "Installation and Administration Guide" and some study of your HPC

Hi all, I got one problem with comparing strings like if any string is like "*RIGHT, EPICARDIUM: FOCUS, GRAY-WHITE, SINGLE, APPROX 0.6 CM IN DIAMETER*." and i have to compare "*GRAY-WHITE*" with the above string or otherwise i have to compare " *TUMOR BENIGN*" this string with "*MEDULLRY TUMOR BENIGN,TYP PHEOCHROMOCYTOMA*" i

I'm doing prediction of the survival cases using gene expression profiles(Affymetrix chips). Can somebody tell me how to use the Cox PH model to select genes and make a prediction of survival? Thanks. Guangchun

Hello, Would you tell my how to extract a result from a test - it's justified because I need to run this test many times. Here is an example from authors' test: > library("coin") > lungtumor <- data.frame(dose = rep(c(0, 1, 2), c(40, 50, 48)), tumor = c(rep(c(0, 1), c(38, 2)), rep(c(0, 1), c(43, 7)), rep(c(0, 1), c(33, 15)))) > ca.test<-independence_test(tumor ~

Dear list, I need to read a big txt file (around 130Mb; 23800 rows and 49 columns) for downstream clustering analysis. I first used "Tumor <- read.table("Tumor.txt",header = TRUE,sep = "\t")" but it took a long time and failed. However, it had no problem if I just put data of 3 columns. Is there any way which can load this big file? Thanks for any suggestions!

Hello, I am trying to explore a classification with GGobi. I am trying to generate additional data according to the model so I can draw the decision boundaries on the GGobi plot. The problem is that I always get the same error: Error in predict.lda(model,data): wrong number of variables, even if I know that I used the same number of variables for the model generation (6) and for the additional

Dear sir, I am Shibu John from Thrombosis Research Institute India. It is a multidisciplinary organisation concerned with the interrelated problems of thrombosis and atherosclerosis. I was searching for Cochran armitage trend test program in R. Then I had seen your R coding for C-A trend test. I tried that in the R software. But I can?t run the program due the [Error: could not find function