Description

The Oncogenic Gene Set was downloaded from MSigDB. A penalized AFT model was fit on the training data, and the linear predictors were then used as input features for the SBC model. This model (which was fit on the training data set) was then used to obtain linear predictor on the test set, which would be the test input features for the SBC.

Loading of libraries

Libraries needed to run the code must be loaded first

Loading/Splitting training and testing sets

Data is separated into training and testing

######## Input ###############

Clinical_TrainingSet <- read_csv("/Volumes/GoogleDrive/My Drive/Documents/Life Science Informatics Master/Master thesis/Data/Second_data_split/TrainingSet/Clinical_TrainingSet.csv")
mRNAArray_TrainingSet <- read_csv("/Volumes/GoogleDrive/My Drive/Documents/Life Science Informatics Master/Master thesis/Data/Second_data_split/TrainingSet/mRNAArray_TrainingSet.csv")
Clinical_TestSet <- read_csv("/Volumes/GoogleDrive/My Drive/Documents/Life Science Informatics Master/Master thesis/Data/Second_data_split/TestSet/Clinical_TestSet.csv")
mRNAArray_TestSet <- read_csv("/Volumes/GoogleDrive/My Drive/Documents/Life Science Informatics Master/Master thesis/Data/Second_data_split/TestSet/mRNAArray_TestSet.csv")
indexes.train<-which(is.na(Clinical_TrainingSet$days_to_death), arr.ind=TRUE)
indexes.test<-which(is.na(Clinical_TestSet$days_to_death), arr.ind=TRUE)
training_time<-Clinical_TrainingSet$days_to_death
test_time<-Clinical_TestSet$days_to_death
for (i in indexes.train){
  training_time[i]<-Clinical_TrainingSet$days_to_last_followup[i]
}
for (i in indexes.test){
  test_time[i]<-Clinical_TestSet$days_to_last_followup[i]
}
total_time<-append(training_time,test_time)

n<-length(Clinical_TrainingSet$X1)
n.new<-length(Clinical_TestSet$X1)


#### Overall Survival Time Vector (N*1)
time<-total_time[1:n]
time<-log(time)
########### Event or Not Vector (N * 1) ####
censoring<-Clinical_TrainingSet$vital_status
######### mRNA (or miRNA expression values) (N*D) ###
mRNAArray_TrainingSet$X1<-NULL
Y.pre.train<-mRNAArray_TrainingSet

#### Overall Survival Time Vector (N*1)
time.new<-total_time[-(1:n)]
time.new<-log(time.new)
########### Event or Not Vector (N * 1) ####
censoring.new<-Clinical_TestSet$vital_status
######### mRNA (or miRNA expression values) (N*D) ###
mRNAArray_TestSet$X1<-NULL
Y.pre.test<-mRNAArray_TestSet

print(paste0("Number of patients in training set: ",as.character(n)))
## [1] "Number of patients in training set: 160"
print(paste0("Number of patients in test set: ",as.character(n.new)))
## [1] "Number of patients in test set: 261"

Oncogene set PAFT fitting of model

PAFT fitting in training set

gs<-list()
reg.paft<-list()
linear.aft<-list()
lost<-list()
db_genesets<-msigdbr(species="Homo sapiens",category = "C6")
smod <-  Surv(exp(time), censoring)

for (i in unique(db_genesets$gs_name)){
  gs[i]<-list(unique(filter(db_genesets,db_genesets$gs_name==i)$gene_symbol))
  #Select all columns that aren't in the original dataframe Y.pre.train
  tokeep <- gs[[i]][(gs[[i]] %in% colnames(Y.pre.train))]
  #list with the percentages of ids lost
  lost[[i]]<-(1-(length(tokeep)/length(gs[[i]])))*100
  df<-Y.pre.train[,tokeep]
  
  reg.paft[[i]] <- glmnet(x = as.matrix(df), y = time, family = "gaussian")
  linear.aft[[i]] <- predict(object = reg.paft[[i]], newx = as.matrix(df), s = min(reg.paft[[i]]$lambda)) 
  
}

Percentage of ids lost per gene set in Training Set

toplot<-as.data.frame(x=unlist(lost))
colnames(toplot)<-"Percentage"
ggplot(toplot, aes(x=" ", y=Percentage)) + 
    geom_boxplot(fill="slateblue", alpha=0.2) + labs(title = "Percentage of lost ids per gene set", subtitle =" Training Set")


Y.pre.train<-do.call(cbind,linear.aft)
colnames(Y.pre.train)<-names(linear.aft)

PAFT fitting in test set

Percentage of ids lost per gene set in Test Set

toplot.new<-as.data.frame(x=unlist(lost.new))
colnames(toplot.new)<-"Percentage"
ggplot(toplot, aes(x=" ", y=Percentage)) + 
    geom_boxplot(fill="slateblue", alpha=0.2) + labs(title = "Percentage of lost ids per gene set", subtitle =" Test Set")


Y.pre.test<-do.call(cbind,linear.aft.new)
colnames(Y.pre.test)<-names(linear.aft.new)

Data visualization

Censoring

table(censoring) %>%  
  kable(caption = "Censoring frequency for the training set") %>%
  kable_styling(bootstrap_options = c("striped", "hover","responsive"),full_width = T, position = "center")

Censoring frequency for the training set
censoring	Freq
0	6
1	154

table(censoring.new) %>%
  kable(caption = "Censoring frequency for the testing set") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed","responsive"),full_width = T, position = "center")

Censoring frequency for the testing set
censoring.new	Freq
0	48
1	213

Header of mRNA matrixes

  Y.pre.train[1:5,1:5] %>%
  kable(caption = "mRNA MicroArray data for the Training set " ) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed","responsive"),full_width = T, position = "center")

mRNA MicroArray data for the Training set
AKT_UP_MTOR_DN.V1_DN	AKT_UP_MTOR_DN.V1_UP	AKT_UP.V1_DN	AKT_UP.V1_UP	ALK_DN.V1_DN
5.134619	5.010416	4.860404	5.323247	5.104891
6.574591	6.908797	6.681042	6.347407	6.625620
5.710860	5.806477	5.780028	5.817500	5.749574
7.202871	6.244260	7.058306	6.572306	6.849285
6.331762	6.523214	6.422343	6.244082	6.449727

  Y.pre.test[1:5,1:5] %>%
  kable(caption = "mRNA MicroArray data for the Testing set " ) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed","responsive"),full_width = F, position = "center")

mRNA MicroArray data for the Testing set
AKT_UP_MTOR_DN.V1_DN	AKT_UP_MTOR_DN.V1_UP	AKT_UP.V1_DN	AKT_UP.V1_UP	ALK_DN.V1_DN
6.876991	3.890184	2.847315	6.403560	7.5767734
4.518973	4.091609	5.642734	4.585454	-0.3478366
10.225450	7.979294	5.732973	3.070294	4.0343758
6.799209	2.669068	3.329664	6.904838	2.5633505
6.341744	6.750768	7.398075	6.491614	1.7388585

PCA of training and test set

Y.pre.train<-do.call(cbind,linear.aft)
colnames(Y.pre.train)<-names(linear.aft)

Y.pre.test<-do.call(cbind,linear.aft.new)
colnames(Y.pre.test)<-names(linear.aft.new)

          
Y.pre.train<-scale(Y.pre.train,scale=TRUE,center=TRUE)
Y.pre.test<-scale(Y.pre.test,scale=TRUE,center=TRUE)


totalmRNA<-rbind(Y.pre.train,Y.pre.test)
mRNA.pca<-as.data.frame(prcomp(totalmRNA,scale=TRUE, center= TRUE)$x[,1:2])
classes<-c()
for (i in 1:length(Clinical_TrainingSet$days_to_death)){
    classes<-append(classes,"Training")
  }
for (i in 1:length(Clinical_TestSet$days_to_death)){
    classes<-append(classes,"Validation")
  }

mRNA.pca$Classes<-classes
p1 <- ggplot(mRNA.pca, aes(x=PC1, y=PC2, colour=Classes)) + ggtitle(" PCA of the training and validation set \n GBM-TCGA Data set") + geom_point(shape=19) + labs(y = "PC1", x = "PC2", colour = "Classes") 
p1

pdf(file="OncogenicGeneSets+PAFT_files/PCA_Train&Test.pdf")
p1
dev.off()
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##                 2

Signature calculation

This chunk calculates SBC gene signature It’s based on the idea of Univariate testing of Survival Data features

######## Prefiltering of the Genes ############################### ###########################
######## Using Univariate Cox's Model for Ranking of the Survival Power of the Genes ########

  surv.obj <- Surv(time,censoring)
  coeff.sig <- c(0)
  
  pvalue.sig <- c(0)
  
  
  calcCox = function(x){
    q1 <- unlist(summary(coxph(surv.obj ~ ., data = as.data.frame(x))))
    return(q1$logtest.pvalue)
  }
  
  
  pvalue.sig <- apply(Y.pre.train,2,calcCox)
  
  
  ###### Adjusting p-values for Multiple Test Correction
  pvalue.sig.adj <- p.adjust(pvalue.sig, method = "fdr")
  
  #### As the number of features are quite variable choose first a very loose cut-off 
  
  signature.loose <- colnames(Y.pre.train)[(pvalue.sig.adj < params$pval.sbc)] 
  
  ### Combined the P-values
  pvalue.combined <- (pvalue.sig.adj) 
  names(pvalue.combined) <- colnames(Y.pre.train)
  ## Sort it
  pvalue.combined.sort <- sort(pvalue.combined)
  ## Only select those genes which are loosely in the signature
  pvalue.combined.adj <- pvalue.combined.sort[names(pvalue.combined.sort) %in% signature.loose]
  
  
  ### Take the top 50 genes ####
  signature.sbc <- names(pvalue.combined.adj[1:50])

The SBC signature on the dataset looks like this

  to_plot<-data.frame(Genes=signature.sbc)
   to_plot%>%
     kable(caption = "SBC Signature") %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed","responsive"),full_width = F, position = "center")%>%
     scroll_box(width = "500px", height = "200px")

SBC Signature
Genes
CAMP_UP.V1_DN
DCA_UP.V1_UP
NFE2L2.V2
KRAS.600_UP.V1_DN
KRAS.600.LUNG.BREAST_UP.V1_DN
KRAS.600.LUNG.BREAST_UP.V1_UP
E2F1_UP.V1_DN
TBK1.DF_UP
KRAS.600_UP.V1_UP
TBK1.DF_DN
IL21_UP.V1_UP
ESC_J1_UP_LATE.V1_UP
CYCLIN_D1_KE_.V1_DN
STK33_UP
IL21_UP.V1_DN
ATF2_S_UP.V1_UP
RAF_UP.V1_UP
STK33_DN
IL15_UP.V1_DN
LTE2_UP.V1_DN
STK33_NOMO_UP
PRC1_BMI_UP.V1_DN
LEF1_UP.V1_UP
GCNP_SHH_UP_LATE.V1_UP
E2F1_UP.V1_UP
MTOR_UP.N4.V1_UP
DCA_UP.V1_DN
RAF_UP.V1_DN
VEGF_A_UP.V1_DN
TGFB_UP.V1_DN
MTOR_UP.N4.V1_DN
LTE2_UP.V1_UP
IL15_UP.V1_UP
EGFR_UP.V1_DN
PRC1_BMI_UP.V1_UP
PRC2_SUZ12_UP.V1_DN
CYCLIN_D1_UP.V1_DN
CYCLIN_D1_UP.V1_UP
AKT_UP.V1_DN
ESC_J1_UP_LATE.V1_DN
PRC2_EED_UP.V1_DN
RAPA_EARLY_UP.V1_DN
TGFB_UP.V1_UP
MEK_UP.V1_UP
STK33_SKM_UP
NOTCH_DN.V1_DN
SNF5_DN.V1_UP
ESC_V6.5_UP_EARLY.V1_DN
AKT_UP_MTOR_DN.V1_DN
MEK_UP.V1_DN

Survival curves for both training and testing set

surv.fit <- survfit(Surv(total_time,append(Clinical_TrainingSet$vital_status,Clinical_TestSet$vital_status))~ classes)
p3 <- ggsurv(surv.fit, plot.cens=FALSE,main = " DPMM \n Kaplan Meier curves for training and validation set")+ ggplot2::guides(linetype = FALSE)
p3

pdf(file="OncogenicGeneSets+PAFT_files/KMC_Train&Test.pdf")
p3
dev.off()
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##                 2

PCA of the signature genes


Y <- Y.pre.train[,signature.sbc]
Y.new <- Y.pre.test[,signature.sbc]
Y<-scale(Y,scale=TRUE,center=TRUE)
Y.new<-scale(Y.new,scale=TRUE,center=TRUE)

mRNA.signature<-rbind(Y,Y.new)
mRNA.signature.pca<-as.data.frame(prcomp(mRNA.signature,scale=TRUE, center= TRUE)$x[,1:2])
mRNA.signature.pca$Classes<-classes
p6 <- ggplot(mRNA.signature.pca, aes(x=PC1, y=PC2, colour=Classes)) + ggtitle(" PCA of the training and validation set \n SBC Signature mRNAArray Data of GBM") + geom_point(shape=19) + labs(y = "PC1", x = "PC2", colour = "Classes") 
p6

pdf(file="OncogenicGeneSets+PAFT_files/PCA_Signature.pdf")
p6
dev.off()
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##                 2

Training

Preparation of the data needed for the training of the model

########################################################################
###### We prepare the Data Structures Needed for the Running of the SBC ####
#############################################################################

######## Verhaak Signature ###############
Verhaak_gene_signature <-read_csv("/Volumes/GoogleDrive/My Drive/Documents/Life Science Informatics Master/Master thesis/Data/Verhaak_Data/Verhaak_GS_according_to_Training_Set_Split1.csv", 
                                  col_names = FALSE, col_types = cols(X1 = col_skip()))
signature.vk<-Verhaak_gene_signature$X2
######## Verhaak Labels #################
labels.vk<-Clinical_TrainingSet$Subtype

#######Getting signature matrix on training and testing######
Y <- Y.pre.train[,signature.sbc]
c.true <- as.factor(labels.vk)
c.true<-unclass(c.true)

Y.new <- Y.pre.test[,signature.sbc]

Training results

##############################################################################
############ This file takes in training data for Glioblastoma #################
################################################################################

###### Scaling the data #########
Y <- scale(Y, center = TRUE, scale = TRUE)

######
N <- nrow(Y)
D <- ncol(Y)
smod <-  Surv(exp(time), censoring)

##### Initial number of clusters
k =4
F =k


##################### STATE OF THE ART TECHNIQUES #################################
##################### BASIC METHODS + SOME ADVANCED METHODS ########################

source('Comparisonx.R')
Comparisonx()

source('ComparisionFLX.R')
ComparisionFLX()


############################# PARAMETERS for GIBB's SAMPLING ####
iter = params$iter
iter.burnin = params$iter.burnin
iter.thin  = params$iter.thin
Nps = as.integer(iter/ iter.thin)


######################### Initialize the Parameters ##############################
source('initialize.R')
initialize()

###################### Start with a good configuration ###########################
#source('startSBC.R')
#startSBC()


########### Train the Model #########################################
source('burninDPMM.R')
burninDPMM()

source('gibbsDPMM.R')
gibbsDPMM()

########## Analyze the fit ##########################################
### Good feature selection from heatmap plus cindex plus randindex
source('MCMCanalyze.R')
MCMCanalyze()

print(paste0("Time (hours) necessary for burnin :",as.character(round(sum(burnintime)/3600),digits=5)))
## [1] "Time (hours) necessary for burnin :1"
print(paste0("Time (hours) necessary for Gibb's sampling :",as.character(round(sum(gibbstime/3600),digits=5))))
## [1] "Time (hours) necessary for Gibb's sampling :0.64175"

Plots of results from training


###############################
### Some plots and analysis ####
#################################
#### Generating some plots and results ON Training data ###
logrank <- survdiff(smod ~ c.final)
pval<-1 - pchisq(unlist(logrank)$chisq,df =3)
surv.fit <- survfit(smod ~ c.final)
p4 <- ggsurv(surv.fit,surv.col=c("#b16ed3","#d37f6e","#90d36e","#6ec2d3"),plot.cens=FALSE,main = " DPMM \n Kaplan Meier Estimators \n Training Set")+ ggplot2::guides(linetype = FALSE)+labs(subtitle = paste("P-value:",toString(round(pval,digits = 10))))
p4

pdf(file="OncogenicGeneSets+PAFT_files/KMC_Training.pdf")
p4
dev.off()
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##                 2

############ Generating some Plots ##########################
Y<-Y.pre.train[,signature.sbc]
pc <- prcomp(Y)
pc.pred <- predict(pc,newdata = Y)
p5 <- ggplot(as.data.frame(pc.pred), aes(x=pc.pred[,1], y= pc.pred[,2], colour= as.factor(c.final))) + ggtitle("PCA after SBC Clustering \n Training Set") + geom_point(shape=19) + labs(y = "PC1", x = "PC2", colour = "Classes")+scale_color_manual(values=c("#b16ed3","#d37f6e","#90d36e","#6ec2d3","#C70039"))
p5

pdf(file="OncogenicGeneSets+PAFT_files/PCA_Training.pdf")
p5
dev.off()
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##                 2

to_plot<-as.data.frame(likli.burnin)
p7<-qplot(seq_along(to_plot$likli.burnin), to_plot$likli.burnin)
p7

pdf(file="OncogenicGeneSets+PAFT_files/LikBurn.pdf")
p7
dev.off()
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##                 2

Testing

#Defining again Y and Y.new
Y <- Y.pre.train[,signature.sbc]
Y.new <- Y.pre.test[,signature.sbc]

Y<-scale(Y, scale=TRUE,center= TRUE)
Y.new<-scale(Y.new,scale=TRUE,center=TRUE)

#PCA
Y.big.unscaled<-rbind(Y,Y.new)
pc.unscaled <- prcomp(Y.big.unscaled,center=TRUE,scale = TRUE)
labels<-c.sbc
for (i in 1:dim(Y.new)[1]){labels<-append(labels,5)}
p8 <- ggplot(as.data.frame(pc.unscaled$x), aes(x=pc.unscaled$x[,1], y= pc.unscaled$x[,2], colour= as.factor(labels))) + ggtitle("PCA \n Training set with SBC labels and unlabeled test set") + geom_point(shape=19) + labs(y = "PC1", x = "PC2", colour = "Classes")+scale_color_manual(values=c("#b16ed3","#d37f6e","#90d36e","#6ec2d3","#BDBDBF"))
p8

######## Predict on New Data Set  BASED ON JUST THE MOLECULAR DATA #####################################
smod.new <- Surv(time.new, censoring.new)
source('predictCLASS.R')
#What does this function do?: Outputs a matrix that has dimension number of test samples * number of MCMC samples
#The output matrix is c.matrix.new
predictCLASS(Y.new)
## Check the predicted Rand Index

#### Choose that configuration which has the highest difference in survival curves ####################
lr <- c(0)
for (j in 1:Nps){
  lr[j] <-  1 - pchisq(unlist(survdiff(smod.new ~ c.matrix.new[,j]))$chisq,df=length(table(c.matrix.new[,j]))-1)
}

significant<-c()
for (i in 1:Nps){
  if(lr[i]<0.05 & length(table(c.matrix.new[,i]))==4){
    significant<-append(significant,i)
  }
}

if (length(significant)>0){
  index<-significant[which.min(lr[significant])]
  c.sbc.new <- c.matrix.new[,index]

}else{
 print("There was no MCMC iteration that found 4 clusters and was statistically significant (p_value=0.05)")
 c.sbc.new <- c.matrix.new[,which.min(lr)]
}

###############################################################################
################################# Other ways of obtaining predictions #########
## If we fit cluster-specific models ###########################################

pre.sbc <- c(0)
for (q in unique(c.sbc.new)){
  ind <- which(c.sbc == q)
  ind.new <- which(c.sbc.new == q)

  time.tmp <- time[ind]
  time.tmp.new<- time.new[ind.new]

  Y.tmp <- Y[ind,]
  Y.tmp.new <- Y.new[ind.new,]

  reg <- cv.glmnet(x = Y.tmp, y = time.tmp, family = "gaussian")
  
  if (class(Y.tmp.new)=="numeric"){ 
    pre.sbc[ind.new] <- predict(object = reg, newx = t(matrix(Y.tmp.new)), s = "lambda.min")
  }else{
    pre.sbc[ind.new] <- predict(object = reg, newx = Y.tmp.new, s = "lambda.min")
  }
}
predCIndex.sbc.aft  <<- as.numeric(concordance(smod.new ~ exp(pre.sbc))[1])



#### Use the adhoc-prediction algorithm ####################
source('predictADHOCverhaak.R')

source('predictTIME.R')
predictchineseAFTtime(Y.new)

Plot results from testing

logrank.new <- survdiff(smod.new ~ c.sbc.new)
#df= Degrees of freedom should be number of clusters-1
pval.new<-1 - pchisq(unlist(logrank.new)$chisq,df =length(logrank.new$n)-1)
surv.fit <- survfit(smod.new ~ c.sbc.new)
p5.new <- ggsurv(surv.fit, surv.col=c("#b16ed3","#d37f6e","#90d36e","#6ec2d3"),plot.cens=FALSE,main = " DPMM \n Kaplan Meier Estimators \n Validation Set")+ ggplot2::guides(linetype = FALSE)+labs(subtitle = paste("P-value:",toString(round(pval.new,digits = 5))))

par(mfrow=c(2,1))
p4

p5.new

pdf(file="OncogenicGeneSets+PAFT_files/KMC_Test.pdf")
p5.new
dev.off()
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##                 2

pc <- prcomp(Y.new,center=TRUE,scale = TRUE)
pc.pred <- predict(pc,newdata = Y.new)
p4.new <- ggplot(as.data.frame(pc.pred), aes(x=pc.pred[,1], y= pc.pred[,2], colour= as.factor(c.sbc.new))) + ggtitle(" PCA after SBC Clustering \n Validation set") + geom_point(shape=19) + labs(y = "PC1", x = "PC2", colour = "Classes") +scale_color_manual(values=c("#b16ed3","#d37f6e","#90d36e","#6ec2d3"))
p4.new

pdf(file="OncogenicGeneSets+PAFT_files/PCA_Test.pdf")
p4.new
dev.off()
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##                 2

pdf(file="OncogenicGeneSets+PAFT_files/PosteriorProbability.pdf")
heatmapdata <- as.data.frame(final.betahat)
heatmap.2(t(as.matrix(heatmapdata)),dendrogram="none", margins=c(6,10), main = "Posterior probability \n for Selection \n ", cexCol = 0.85, cexRow = 0.7, Rowv = FALSE)
dev.off()
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##                 2

table(c.sbc) %>%
  kable(caption = "Clusters for the training set") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed","responsive"),full_width = T, position = "center")

Clusters for the training set
c.sbc	Freq
1	105
2	3
3	28
4	24

table(c.sbc.new) %>%
  kable(caption = "Clusters for the testing set") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed","responsive"),full_width = T, position = "center")

Clusters for the testing set
c.sbc.new	Freq
1	157
2	2
3	59
4	43

Methods<-c("Flexmix","K-Means + Penalized AFT","K-Means + Penalized Cox","AFT","Cox","SBC")
recovCIndex<-round(c(recovCIndex.flx,recovCIndex.km.paft,recovCIndex.km.pcox,recovCIndex.na.aft,recovCIndex.na.cox,max(recovCIndex.sbc)),digits = 5)
predCIndex<-c(predCIndex.flx,"NA",predCIndex.kk.pcox,predCIndex.na.aft,predCIndex.na.cox,max(predCIndex.sbc))
sbc_and_comparisons<-as.data.frame(Methods)
sbc_and_comparisons$recovCIndex<-recovCIndex
sbc_and_comparisons$predCIndex<-predCIndex
sbc_and_comparisons %>%
  kable(caption = "SBC vs other methods") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed","responsive"),full_width = T, position = "center")

SBC vs other methods
Methods	recovCIndex	predCIndex
Flexmix	0.98398	0.877180739706909
K-Means + Penalized AFT	0.98612	NA
K-Means + Penalized Cox	0.93345	0.882910346347376
AFT	0.98374	0.873985382157417
Cox	0.98414	0.874462849377456
SBC	0.95888	0.528317478973078

SBC on the GBM dataset from the whole Firebrowse Database(TCGA).

Oncogene Sets + PAFT

Camila Duitama (Based on Ashar Ahmad’s code for the SBC model)

11th July 2019