dear group, how can expression data for a group of genes can be correlated to survival covariate data using cox model and plot a kaplan-mier curve. say i have subset of data from matrix MxN (M genes and N samples). I take expression values for YxN (subset of M genes is Y and N are collection of cancer and normal) and use recurrance time or survival time and check if Y genes are sifnificant under cox model for recurrance. if they are sifnificant plot them using kaplan-m curve. I want to be able to use coxph and survh functions. I do not know how to use both expression data and survival covariate data and see if set of genes are sifnificant. thanks Adrian

Hi Adrian, An example using the sample.ExpressionSet dataset. ## load packages > library("survival") > library("Biobase") > data(sample.ExpressionSet) ## fake up some survival time - there are 26 observations ## let's say we have survival time =< 36 months for all patients ## with some amount of censoring > surv.time <- Surv(sample(1:36, 26, replace=T), sample(0:1, 26, replace=T)) > surv.time [1] 6+ 35 17+ 18 11 35 15 15+ 7+ 14+ 31 12+ 15+ 1+ 14+ 24 30 19+ 8+ [20] 25+ 22 4+ 21+ 3 23 18+ ## fit model with first gene > mod <- coxph(surv.time~exprs(sample.ExpressionSet)[1,]) > summary(mod) Call: coxph(formula = surv.time ~ exprs(sample.ExpressionSet)[1, ]) n= 26 coef exp(coef) se(coef) z p exprs(sample.ExpressionSet)[1, ] 0.00656 1.01 0.00782 0.839 0.4 exp(coef) exp(-coef) lower .95 upper .95 exprs(sample.ExpressionSet)[1, ] 1.01 0.993 0.991 1.02 Rsquare= 0.026 (max possible= 0.793 ) Likelihood ratio test= 0.68 on 1 df, p=0.411 Wald test = 0.7 on 1 df, p=0.402 Score (logrank) test = 0.72 on 1 df, p=0.396 ##OK, so not significant - let's plot anyway > plot(survfit(mod)) You can just wrap this up in a call to apply to do all genes. In addition, you could pull out the LR test statistic/p-value as a first pass to see which genes are significant, and then go back and just plot those genes. Best, Jim Adrian Johnson wrote: > dear group, > how can expression data for a group of genes can be correlated to > survival covariate data using cox model and plot a kaplan-mier curve. > say i have subset of data from matrix MxN (M genes and N samples). I > take expression values for YxN (subset of M genes is Y and N are > collection of cancer and normal) and use recurrance time or survival > time and check if Y genes are sifnificant under cox model for > recurrance. if they are sifnificant plot them using kaplan-m curve. I > want to be able to use coxph and survh functions. I do not know how to > use both expression data and survival covariate data and see if set of > genes are sifnificant. > thanks > Adrian > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician Hildebrandt Lab 8220D MSRB III 1150 W. Medical Center Drive Ann Arbor MI 48109-5646 734-936-8662

Hi Adrian, An example using the sample.ExpressionSet dataset. ## load packages > library("survival") > library("Biobase") > data(sample.ExpressionSet) ## fake up some survival time - there are 26 observations ## let's say we have survival time =< 36 months for all patients ## with some amount of censoring > surv.time <- Surv(sample(1:36, 26, replace=T), sample(0:1, 26, replace=T)) > surv.time [1] 6+ 35 17+ 18 11 35 15 15+ 7+ 14+ 31 12+ 15+ 1+ 14+ 24 30 19+ 8+ [20] 25+ 22 4+ 21+ 3 23 18+ ## fit model with first gene > mod <- coxph(surv.time~exprs(sample.ExpressionSet)[1,]) > summary(mod) Call: coxph(formula = surv.time ~ exprs(sample.ExpressionSet)[1, ]) n= 26 coef exp(coef) se(coef) z p exprs(sample.ExpressionSet)[1, ] 0.00656 1.01 0.00782 0.839 0.4 exp(coef) exp(-coef) lower .95 upper .95 exprs(sample.ExpressionSet)[1, ] 1.01 0.993 0.991 1.02 Rsquare= 0.026 (max possible= 0.793 ) Likelihood ratio test= 0.68 on 1 df, p=0.411 Wald test = 0.7 on 1 df, p=0.402 Score (logrank) test = 0.72 on 1 df, p=0.396 ##OK, so not significant - let's plot anyway > plot(survfit(mod)) You can just wrap this up in a call to apply to do all genes. In addition, you could pull out the LR test statistic/p-value as a first pass to see which genes are significant, and then go back and just plot those genes. Best, Jim Adrian Johnson wrote: > dear group, > how can expression data for a group of genes can be correlated to > survival covariate data using cox model and plot a kaplan-mier curve. > say i have subset of data from matrix MxN (M genes and N samples). I > take expression values for YxN (subset of M genes is Y and N are > collection of cancer and normal) and use recurrance time or survival > time and check if Y genes are sifnificant under cox model for > recurrance. if they are sifnificant plot them using kaplan-m curve. I > want to be able to use coxph and survh functions. I do not know how to > use both expression data and survival covariate data and see if set of > genes are sifnificant. > thanks > Adrian > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician Hildebrandt Lab 8220D MSRB III 1150 W. Medical Center Drive Ann Arbor MI 48109-5646 734-936-8662

On Fri, Nov 14, 2008 at 2:04 AM, Adrian Johnson <oriolebaltimore@gmail.com>wrote: > dear group, > how can expression data for a group of genes can be correlated to > survival covariate data using cox model and plot a kaplan-mier curve. > say i have subset of data from matrix MxN (M genes and N samples). I > take expression values for YxN (subset of M genes is Y and N are > collection of cancer and normal) and use recurrance time or survival > time and check if Y genes are sifnificant under cox model for > recurrance. if they are sifnificant plot them using kaplan-m curve. I > want to be able to use coxph and survh functions. I do not know how to > use both expression data and survival covariate data and see if set of > genes are sifnificant. > You might want to look at the survival package. Sean [[alternative HTML version deleted]]