Question: gene expression data followup
0
gravatar for Adrian Johnson
10.9 years ago by
Adrian Johnson330 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
survival cancer • 1.1k views
ADD COMMENTlink modified 10.9 years ago by James W. MacDonald51k • written 10.9 years ago by Adrian Johnson330
Answer: gene expression data followup
0
gravatar for James W. MacDonald
10.9 years ago by
United States
James W. MacDonald51k wrote:
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
ADD COMMENTlink written 10.9 years ago by James W. MacDonald51k
Answer: gene expression data followup
0
gravatar for James W. MacDonald
10.9 years ago by
United States
James W. MacDonald51k wrote:
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
ADD COMMENTlink written 10.9 years ago by James W. MacDonald51k
Answer: gene expression data followup
0
gravatar for Sean Davis
10.9 years ago by
Sean Davis21k
United States
Sean Davis21k wrote:
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]]
ADD COMMENTlink written 10.9 years ago by Sean Davis21k
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 305 users visited in the last hour