## [1] "Number of variant nodes:"
## [1] 93
## [1] "out of"
## [1] 98
We plot the distributions across cohort of Proliferation and Apoptosis
Since a MKI67 score is available in META dataset, we can study the correlation between the simulated Proliferation phenotype and the MKI67 score.
## [1] "Correlation between MKI67 and simulated Proliferation:"
## [1] 0.6244703
## [1] "Correlation between NPI and simulated Proliferation:"
## [1] 0.2910975
The same kind of analysis is possible with every Node score of the model simulations
Is it possible to stratify patients (i.e. infer PAM50 subtype), based on simulation results? Let’s have a look on PCA
## Warning: package 'rgl' was built under R version 3.4.3
For the sake of comparison we perform the same analyses with original RNA data, focusing only on RNA realated to model nodes
First simple Cox model with Proliferation…
## [1] "Survival Analysis with Proliferation score"
## Call:
## coxph(formula = Surv(time = OS_MONTHS_lim, event = Status_lim) ~
## Proliferation, data = survival_data)
##
## n= 1866, number of events= 495
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Proliferation 0.9991 2.7157 0.1432 6.979 2.98e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Proliferation 2.716 0.3682 2.051 3.595
##
## Concordance= 0.609 (se = 0.013 )
## Rsquare= 0.024 (max possible= 0.979 )
## Likelihood ratio test= 44.65 on 1 df, p=2.352e-11
## Wald test = 48.7 on 1 df, p=2.981e-12
## Score (logrank) test = 50.02 on 1 df, p=1.522e-12
## [1] "Survival Analysis with binarized Proliferation"
## [1] "With a threshold derived from median"
## [1] "With GMM threshold"
## [1] 0.14
## [1] "Proliferation subtype repartition"
##
## 0 1
## 1007 859
…and Apoptosis
## [1] "Survival Analysis with binarized Apoptosis"
## [1] "With a threshold derived from median"
## [1] "With GMM threshold"
## [1] 0.756231
## [1] "Apoptosis subtype repartition"
##
## 0 1
## 937 929
And with a combination