search for: tumour

Hi friends, I need suggestions/directions on how to producing a waterfall plot for present extend of change in tumour size for a set of respondents in a study.  Example of use of waterfall plot is in the following slides presented at ASCO 2007 by Axel Grothey. Link is http://media.asco.org/player/default.aspx?LectureID=AG265&conferenceFolder=GI2007&SessionFolder=Poster&slideonly=yes&TrackID=N929&...

Hello, I want to perform an lme on a database with this structure: ID Sequence Temperature Tumour Error 1 5 0 1 8.721872e-08 1 5 0 2 8.695348e-08 1 5 0 3 2.019604e-13 1 5 37 1...

Dear all, we have an exciting PhD position applying HPC to the analysis of large scale cancer datasets. The post will suit an applicant from a strong computational background who wishes to apply their knowledge to help develop a better understanding of the processes that control how tumours develop. Details below:- High Performance Computing applied to cancer research: Computational analysis of Noncoding RNA regulators of gene expression in individual tumour cells RNABiology/Computational Biology The goal of this project is to use computational tools to ask how patterns in gene ex...

...library(e1071) data <- read.table('http://www.iu.a.u-tokyo.ac.jp/~kadota/R/data_Singh_RMA_3274.txt', header=TRUE, row.names=1, sep="\t", quote="") data.cl <- rep(NA,ncol(data)) data.cl[grep('Normal',colnames(data))] <- 'Normal' data.cl[grep('Tumour',colnames(data))] <- 'Tumour' s <- sample(ncol(data)) m <- svm(x=t(data ), y=factor(data.cl ), scale=T, type="C-classification",kernel="linear") m.s <- svm(x=t(data[,s]), y=factor(data.cl[s]), scale=T, type="C-classification", kernel=&q...

...bcoh <- nwtco$in.subcohort selccoh <- with(nwtco, rel==1|subcoh==1) ccoh.data <- nwtco[selccoh,] ccoh.data$subcohort <- subcoh[selccoh] ## central-lab histology ccoh.data$histol <- factor(ccoh.data$histol,labels=c("FH","UH")) ## tumour stage ccoh.data$stage <- factor(ccoh.data$stage,labels=c("I","II","III","IV")) ccoh.data$age <- ccoh.data$age/12 # Age in years cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data, subcoh = ~subcohort, id=~seqno, cohort.size=...

Dear All. I have the following table that I want to analyze using multinom function freq segments sample 4271 Seg1 tumour 4311 Seg2 tumour 3515 Seg1 normal 3561 Seg2 normal I want to compare model with both factors to the one where only sample is present. model1=multinom(freq~segments+sample,data=table) model2=multinom(freq~ sample,data=table) anova(model2,model1) Likelihood ratio tests of Mu...

Hola. Como te dijo Carlos, el problema está en los nombres de las columnas y en los nombres de las filas. Cuando hice la importación (con dd<-read.csv('mortality.csv'), tuve problemas con las filas de nombre: - Malignant tumour of the larynx trachea bronchus and lungs - Malignant tumour of the lip pharynx and mouth - Other endocrinological metabolic and nutritional conditions que se me corrían a la derecha creándome una columna más. Corregido esto (dentro del archivo csv) procedí como sigue y todo corrió aparenteme...

...am currently using the aCGH package in R version 2.1.0 Windows with some supporting packages (eg. cluster) built under R 2.1.1.Using aCGH package, I am able to identify regions of genomic aberrations in my cell lines using the HMM model. However, when I tried to use aCGH for my paraffin embeded tumour sample, I got the following warning. Warning: MAD could not ben computed for one of the samples. I had used the same R commands for both my tumour samples and cell lines. I had performed HMM partioning using the model AIC with a merging step afterwards with the threshold set at 0.25. Therefor...

I have what I *think* should be a simple problem in R, and hope someone might be able to help me. I'm working with cancer survival data, and would like to calculate adjusted survival figures based on the age of the patient and the tumour classification. A friendly statistician told me I should use Cox proportional hazards to do this, and I've made some progress with using the coxph function. However, there doesn't seem to be a simple way to get adjusted survival figures, and I'm still a bit unsure whether I'...

Hello, I have the next function call: lme(fixed=Error ~ Temperature * Tumour ,random = ~1|ID, data=error_DB) which returns an lme object. I am interested on carrying out some kind of lsmeans on the data returned, but I cannot find any function to do this in R. I'have seen the effect() function, but it does not work with lme objects. Any idea? Best, Dani -- Danie...

...Biology Group - Crispin Miller ========================== Postdoctoral Scientist in Computational Biology: A position is now available for a highly motivated postdoctoral research scientist to develop computational models of gene expression across populations of cells, and to ask how they change in tumours. The goal is to identify novel regulatory noncoding RNA molecules that are disrupted in cancer. 4 Year PhD Studentship: This project will involve applying machine learning and pattern recognition techniques to high volumes of RNA-sequencing data representing a combination of single cells and bulk...

...std. error t1* 0.09967665 0.03579701 0.04973614 I want to apply the Fast bootstrap method from Salibian-Barrera and Zamar (2003) and Salibian-Barrera, M., Van Aels, S. and Willems, G. (2007) to the previous example, i.e., to produce a confidence interval for the exponent of the coefficient of tumour thickness in the Melanoma dataset . Moreover, I want to compare the performance of the Fast bootstrap with that of the classical bootstrap, which requires of course computing power and time. How I can adjust the previous code to do what I want. I asked Angelo Canty for helping me to do this, but...

...es for any offence caused by posting the same question but it's difficult for me to proceed until I get some kind of response, even if it is that this list is not the right place for this question. Thanks for your patience, Richard Dr Richard Birnie Scientific Officer Section of Pathology and Tumour Biology Welcome Brenner Building, LIMM St James University Hospital Beckett St, Leeds, LS9 7TF Tel:0113 3438624 e-mail: r.birnie at leeds.ac.uk

...;utils" "datasets" [7] "base" other attached packages: pvclust "1.1-0" Can someone please advise how to handle this. If any more info is needed just tell me what commands. regards, Richard Dr Richard Birnie Scientific Officer Section of Pathology and Tumour Biology Welcome Brenner Building, LIMM St James University Hospital Beckett St, Leeds, LS9 7TF Tel:0113 3438624 e-mail: r.birnie at leeds.ac.uk

.... error t1* 0.09967665 0.03579701 0.04973614 I want to apply the Fast bootstrap method from Salibian-Barrera and Zamar (2003) and Salibian-Barrera, M., Van Aels, S. and Willems, G. (2007) to the previous example, i.e., to produce a confidence interval for the exponent of the coefficient of tumour thickness in the Melanoma dataset . Moreover, I want to compare the performance of the Fast bootstrap with that of the classical bootstrap, which requires of course computing power and time. How I can adjust the previous code to do what I want. I asked Angelo Canty for helping me to do this, but...

.... error t1* 0.09967665 0.03579701 0.04973614 I want to apply the Fast bootstrap method from Salibian-Barrera and Zamar (2003) and Salibian-Barrera, M., Van Aels, S. and Willems, G. (2007) to the previous example, i.e., to produce a confidence interval for the exponent of the coefficient of tumour thickness in the Melanoma dataset . Moreover, I want to compare the performance of the Fast bootstrap with that of the classical bootstrap, which requires of course computing power and time. How I can adjust the previous code to do what I want. I asked Angelo Canty for helping me to do this, but...

Estimados Queremos con el paquete FactoMineR hacer este tipo de tabla de mortalidad que lea los datos desde de una tabla csv Realizamos lo que viene en la ayuda y es muy interesante, sin embargo cuando mandamos a leer desde la tabla csv original de los autores no hace el análisis porque algo falta y no nos percatamos de que es. Adjunto tabla original Saludos cordiales #ESTO ES LO QUE

.... error t1* 0.09967665 0.03579701 0.04973614 I want to apply the Fast bootstrap method from Salibian-Barrera and Zamar (2003) and Salibian-Barrera, M., Van Aels, S. and Willems, G. (2007) to the previous example, i.e., to produce a confidence interval for the exponent of the coefficient of tumour thickness in the Melanoma dataset . Moreover, I want to compare the performance of the Fast bootstrap with that of the classical bootstrap, which requires of course computing power and time. How I can adjust the previous code to do what I want. I asked Angelo Canty for helping me to do this, but...

----- begin included message In case cohort study, we can fit proportional hazard regression model to case-cohort data. In R, the function is cch() in Survival package Now I am working on case cohort analysis with time dependent covariates using cch() of "Survival" R package. I wonder if cch() provide this utility or not? The cch() manual does not say if time dependent covariate is

I want to take the first row of each unique ID value from a data frame. For instance > ddTable <- data.frame(Id=c(1,1,2,2),name=c("Paul","Joe","Bob","Larry")) I want a dataset that is Id Name 1 Paul 2 Bob > unique(ddTable) Will give me all 4 rows, and > unique(ddTable$Id) Will give me c(1,2), but not accompanied by the name column.