Notebook for MOMIX benchmarking¶
SUB-BENCHMARK 2: Comparison of the jDR methods on cancer datasets¶
We here reproduce the second sub-benchmark proposed in the paper. We thus compare here the performances of the various jDR methods on the integration of multi-omics cancer datasets from TCGA. The methods are evaluated regarding associations to clinical annotations, survival and biological annotations (GO, REACTOME, Hallmarks).
Comparison based on clinical annotations¶
We define here a function that performs the jDR comparison based on their association to clinical annotations. In particular, we here compute the number of clinical annotations significantly associated with at least one factor, and their selectivity (see Methods paper). The clinical metadata need to be stored in a clinical folder containing files named according to each cancer type.
In [1]:
## Perform clinical annotation-based comparison
## INPUTS:
# factorizations = already computed factirizations
# clinical = clinical information associated with samples
# col = columns in clinical data on which the analysis will be performed
## OUPUTS: a list containing output values
# selectivity = Selectivity (fraction of significant annotations per all significant factors)
# nonZeroFacs = Number of unique factors that were significant at least once
# total_pathways = Number of clinical annotations with at least one significant factor component
clinical_comparison <- function(factorizations, clinical, col){
# Empty containers
selectivity <- numeric(0) # Updated labels to be meaningful
non_zero_facs <- numeric(0)
total_sig_clinical_measures <- numeric(0)
cancer_label <- list()
#line <- numeric(0)
#line2 <- numeric(0)
#line3 <- numeric(0)
# For each factorization
for(i in 1:length(factorizations)){
# Extract sample association
factors <- factorizations[[i]][[1]]
if(is.null(names(factors))) { names(factors) <- 1:ncol(factors) }
if(is.null(colnames(factors))) { colnames(factors) <- 1:ncol(factors) }
# Patient names in factorisation results
patient.names <- rownames(factors)
# Patient names in original data
patient.names.in.file <- as.character(clinical[, 1])
patient.names.in.file <- toupper(gsub('-', '\\.', patient.names.in.file))
# Remove non-matching patient names
is_in_file <- patient.names %in% patient.names.in.file
if(length(patient.names)!=sum(is_in_file)) {
factors <- factors[is_in_file, ]
patient.names <- patient.names[is_in_file]
rownames(factors)<-patient.names
}
# Match indices of patient names
indices <- match(patient.names, patient.names.in.file)
# Use indices to extract coresponding survival information
ordered.clinical.data <- clinical[indices,]
# Only use column names that are really present in the data
col_new <- col[col %in% colnames(ordered.clinical.data)]
# Stor all p-values
pvalues <- numeric(0)
# Store number of significant annotations
clin_erich <- 0
# Test significance association with clinical annotations
for(j in col_new){
# Perform the analysis if there is more than one possible value in current column
table_values <- table(ordered.clinical.data[,j])
if(sum(table_values>0)>1){
if(j == "age_at_initial_pathologic_diagnosis" ){
pvalues_col <- apply(factors, MARGIN=2,
function(x) kruskal.test(x~cut(as.numeric(ordered.clinical.data[,j]),
5, include.lowest=TRUE))$p.value)
pvalues <- c(pvalues, pvalues_col)
if(min(pvalues_col)<0.05){
clin_erich <- clin_erich+1
}
}else if(j == "days_to_new_tumor_event_after_initial_treatment"){
pvalues_col <- apply(factors,MARGIN=2,
function(x) kruskal.test(x~cut(as.numeric(ordered.clinical.data[,j]),
3, include.lowest=TRUE))$p.value)
pvalues <- c(pvalues, pvalues_col)
if(min(pvalues_col)<0.05){
clin_erich <- clin_erich+1
}
}else if(j == "gender" || j == "history_of_neoadjuvant_treatment"){
pvalues_col <- apply(factors, MARGIN=2,
function(x) wilcox.test(x~ordered.clinical.data[,j])$p.value)
pvalues <- c(pvalues, pvalues_col)
if(min(pvalues_col)<0.05){
clin_erich <- clin_erich+1
}
}
}
}
# Number of clinical annotations with at least one significant p-value
total_sig_clinical_measures <- rbind(total_sig_clinical_measures, clin_erich)# Updated line
# Total number of significant factors in all tested columns
column <- names(pvalues)[pvalues<0.05]
# Number of unique factors that were significant at least once
non_zero_facs <- rbind(non_zero_facs, length(unique(column))) # NUpdatedew line
# Add to cancer label
cancer_label <- rbind(cancer_label, current_cancer) # New line
# Number of times a p-value was found significant
signif <- length(column)
f<-length(unique(column))
# Selectivity
if (signif != 0) {
selectivity <- rbind(selectivity,((clin_erich/signif)+(f/signif))/2)
} else {
selectivity <- rbind(selectivity,0)
}
}
# Store and return results
out <- data.frame(selectivity=selectivity, nonZeroFacs=non_zero_facs, total_sig_clinical_measures=total_sig_clinical_measures)
# print(out)
return(out)
}
Comparison based on survival predictions¶
We here define the function that compares the performances of the various jDR methods based on their association to survival (computed through Cox regression).
In [2]:
library('survival')
## Perform survival annotation-based comparison
## INPUTS:
# factorizations = already computed factirizations
# method = methods used for factorization
# survival = survival data associated to the cancer
# out.folder = folder where results will be written
# cancer = name of currently analysed cancer
## OUPUTS: a list containing output values
survival_comparison <- function(factorizations, method, survival, out.folder, cancer){
# Initialize result containers
factors_cancer <- numeric(0)
surv_final <- numeric(0)
cancer_label <- list() # New line
# Adjust sample names for breast survival dataset
if(cancer=="breast"){survival[,1] <- paste0(survival[,1],"-01")}
# For each computed factorisation
for(i in 1:length(factorizations)){
# Extract sample factors
factors <- factorizations[[i]][[1]]
# Patient names in factorisation results
patient.names <- rownames(factors)
# Patient names in original data
patient.names.in.file <- as.character(survival[, 1])
patient.names.in.file <- toupper(gsub('-', '\\.', patient.names.in.file))
# Remove non-matching patient names
is_in_file <- patient.names %in% patient.names.in.file
if(length(patient.names)!=sum(is_in_file)) {
factors <- factors[is_in_file, ]
patient.names <- patient.names[is_in_file]
rownames(factors)<-patient.names
}
# Match indices of patient names
indices <- match(patient.names, patient.names.in.file)
# Use indices to extract coresponding survival information
ordered.survival.data <- survival[indices,]
# Clean data (assign 0 to NAs)
ordered.survival.data$Survival[is.na(ordered.survival.data$Survival)] <- 0
ordered.survival.data$Death[is.na(ordered.survival.data$Death)] <- 0
# # ------------------------------
# Calculate coxph
coxph_pvalues <- list()
for (f in 1:num.factors) {
# For each factor calc coxph separately
coxph_obj <- coxph(Surv(ordered.survival.data$Survival, ordered.survival.data$Death) ~ factors[, f])
pvalues <- coef(summary(coxph_obj))[5]
coxph_pvalues <- cbind(coxph_pvalues, pvalues)
}
# Correct p values
coxph_pvalues_adj <- p.adjust(coxph_pvalues, method = "BH", n = length(coxph_pvalues))
# Old section: Calculate coxph
#coxph_obj <- coxph(Surv(ordered.survival.data$Survival, ordered.survival.data$Death) ~ factors)
# P-values (corrected by the number of methods)
#pvalues <- length(factorizations)*as.matrix(coef(summary(coxph_obj))[,5])
# How many significant?
factors_cancer <- c(factors_cancer, sum(coxph_pvalues_adj<0.05))
# Store p-values
surv_final <- rbind(surv_final, unlist(coxph_pvalues_adj))
cancer_label <- rbind(cancer_label, current_cancer) # New line
}
# Keep -log10 of p-values
# Return useful information
rownames(surv_final) <- method
surv <- list()
for (m in 1:num.factors) {
surv <- cbind(surv, unlist(surv_final[, m]))
}
surv <- as.data.frame(surv)
surv$cancer <- cancer_label
surv$facs <- num.factors
return(surv)
}
Comparison based on association to biological annotations (REACTOME, Hallmarks, GO)¶
We define here a function that performs the jDR comparison based on their association to biological annotations. In particular, we here compute the number of factors significantly associated with at least a biological annotation and their selectivity (see Methods paper).
In [3]:
library("fgsea", quietly = TRUE)
## Perform biological annotation-based comparison
## INPUTS:
# factorizations = already computed factirizations
# path.database = path to a GMT annotation file
# pval.thr = p-value threshold (default to 0.05)
## OUPUTS: a list containing output values
# selectivity = Selectivity (fraction of significant annotations per all significant factors)
# nonZeroFacs = Number of unique factors that were significant at least once
# total_pathways = Number of clinical annotations with at least one significant factor component
biological_comparison <- function(factorizations, path.database, pval.thr=0.05){
# Load annotation database
pathways <- gmtPathways(path.database)
# Containers to report results
report_number <- numeric(0)
report_nnzero <- numeric(0)
report_select <- numeric(0)
# For each factorization method
for(i in 1:length(factorizations)){
# Extract metagenes found by factorization method
metagenes<-factorizations[[i]][[2]][[1]]
# Number of factors
num.factors <- ncol(metagenes)
# Rename columns
colnames(metagenes)<-1:num.factors
# Rename rows to remove "|" characters and keep only the gene name before
rownames(metagenes)<-gsub("\\|",".",rownames(metagenes))
rownames(metagenes)<-gsub("\\..*","",rownames(metagenes))
# Remove duplicated gene names that could confuse fgsea
duplicated_names <- unique(rownames(metagenes)[duplicated(rownames(metagenes))])
metagenes <- metagenes[!(rownames(metagenes) %in% duplicated_names), ]
# Variables
min_pval <- numeric(0)
path <- numeric(0)
n <- 0
# Calculate biological annotation enrichment.
# For each factor,
for(j in 1:num.factors){
# Assign gene names
rnk <- setNames(as.matrix(metagenes[,j]), rownames(metagenes))
# Compute fgsea
fgseaRes <- fgsea(pathways, rnk, minSize=15, maxSize=500, nperm=1000)
# ----------------------------------------------------
# If at least one pathway is significant (new section)
if (sum(fgseaRes$padj < pval.thr, na.rm = TRUE) != 0) { #if(sum(fgseaRes$padj < pval.thr)!=0){
# Need to add na.rm = TRUE otherwise you'll miss some rows that include a NA!
# Count this factor
n <- n+1
# Keep min adjusted p-value
min_pval <- rbind(min_pval, min(fgseaRes$padj))
# Keep names of significant pathways
path <- c(path, fgseaRes[fgseaRes$padj<pval.thr, "pathway"])
} else {
min_pval <- rbind(min_pval, NA)
}
}
# -----------------------------------------------
# Report number of unique significant pathways
path <- unlist(path) # Need to unlist otherwise the length will always be 1
# # -------------------------------------------------
# Report number of unique significant pathways
if(length(path)==0){
report_number <- rbind(report_number, 0)
}else{
report_number <- rbind(report_number, length(unique(path)))
}
# Report selectivity
if(length(unique(path))==0){
report_select <- rbind(report_select, NA)
}else{
# Updated to make more clear when I was debugging the above
num_unique_path <- length(unique(path))
num_path <- length(path)
al <- num_unique_path/num_path
fl <- length(which(!is.na(min_pval)))/num_path
#al<-length(unique(path))/length(path)
#fl<-length(which(!is.na(min_pval)))/length(path)
report_select <- rbind(report_select, (al+fl)/2)
}
# Report number of factors associated with at least one significant pathway
report_nnzero<-rbind(report_nnzero, n)
}
out <- data.frame(selectivity=report_select, nonZeroFacs=report_nnzero, total_pathways=report_number)
# print(out)
return(out)
}
Running the comparisons in cancer¶
We here run the three functions defined above to perform the comparison on the cancer data. The cancer data should be organized into the data folder, each of them having the name of a different cancer type and containin the various omics data (3 omics for our test cases). Using the script povided in our github this step is automatically performed.
In [13]:
# Load the function running the factorization, plus a support function
source("runfactorization.R")
source("log2matrix.R")
# List downloaded cancer data.
# Folder structure should be organized as discussed above.
# Exclude first result as it's the parent folder
cancers <- list.dirs(path = "../data/cancer", full.names = TRUE, recursive = TRUE)[-1]
cancer_names <- list.dirs(path = "../data/cancer", full.names = FALSE, recursive = TRUE)[-1]
# Annotation databases used for biological enrichment
path.database <- "../data/bio_annotations/c2.cp.reactome.v6.2.symbols.gmt" #REACTOME
#path.database <- "../data/bio_annotations/h.all.v6.2.symbols.gmt" #Hallmarks
#path.database <- "../data/bio_annotations/c5.all.v6.2.symbols.gmt" #GO
# Label to identify current run
tag <- format(Sys.time(), "%Y%m%d%H%M%S")
# Folder for comparison results
results_folder <- paste0("../results", tag, "/")
# Create output folder
dir.create(results_folder, showWarnings = FALSE)
# Number of factors used in the paper
num.factors <- 10
# Initialize result containers
clinical_analysis <- data.frame(
matrix(data = NA, ncol=5, nrow=0,
dimnames = list(c(), c("methods", "cancer", "selectivity", "nonZeroFacs", "total_pathways"))
),
stringsAsFactors = FALSE)
biological_analysis <- data.frame(
matrix(data = NA, ncol=5, nrow=0,
dimnames = list(c(), c("methods", "cancer", "selectivity", "nonZeroFacs", "total_pathways"))
),
stringsAsFactors = FALSE)
biological_analysis_hallmark <- data.frame(
matrix(data = NA, ncol=5, nrow=0,
dimnames = list(c(), c("methods", "cancer", "selectivity", "nonZeroFacs", "total_pathways"))
),
stringsAsFactors = FALSE)
biological_analysis_GO <- data.frame(
matrix(data = NA, ncol=5, nrow=0,
dimnames = list(c(), c("methods", "cancer", "selectivity", "nonZeroFacs", "total_pathways"))
),
stringsAsFactors = FALSE)
survival_analysis <- data.frame(
matrix(data = NA, ncol=5, nrow=0,
dimnames = list(c(), c("methods", "cancer", "selectivity", "nonZeroFacs", "total_pathways"))
),
stringsAsFactors = FALSE)
cancer.list <- list()
# Clinical categories to be used for clinical tests
col <- c("age_at_initial_pathologic_diagnosis",
"gender",
"days_to_new_tumor_event_after_initial_treatment",
"history_of_neoadjuvant_treatment")
In [14]:
cancers <- c('../data/cancer/aml',
'../data/cancer/breast',
'../data/cancer/colon',
'../data/cancer/kidney',
'../data/cancer/liver',
'../data/cancer/melanoma',
'../data/cancer/ovarian',
'../data/cancer/sarcoma')
# GBM and Lung had issues!!
In [15]:
# For each cancer dataset
for(i in cancers){
print(paste0("Now analysing ", i))
# Name of current cancer
current_cancer <- basename(i)
# If the expression and miRNA data are not log2-transformed as for those provided by XX et al.
log2matrix(i,"exp")
log2matrix(i,"mirna")
# Perform factorisation
print("Running factorisation...")
out <- runfactorization(i, c("log_exp","methy","log_mirna"), num.factors, sep=" ", filtering="sd")
save(out, file=paste0(results_folder, current_cancer, "results_out.rds"))
# Survival analysis
print("Running survival analysis...")
survival <- read.table(paste0(i, "/survival"), sep="\t", header=TRUE, stringsAsFactors=FALSE)
out_survival <- survival_comparison(out$factorizations, out$method, survival,
results_folder, current_cancer)
survival_analysis <- rbind(survival_analysis,
data.frame(methods=out$method, cancer=current_cancer, out_survival))
# Clinical analysis
print("Running clinical analysis...")
clinical <- read.table(paste0("../data/clinical/", current_cancer), sep="\t", header=TRUE)
out_clinical <- clinical_comparison(out$factorizations, clinical, col)
clinical_analysis <- rbind(clinical_analysis,
data.frame(methods=out$method, cancer=current_cancer, out_clinical))
# Biological analysis on 3 pathways
print("Running biological analysis...")
path.database <- "../data/bio_annotations/c2.cp.reactome.v6.2.symbols.gmt" #REACTOME
out_bio <- biological_comparison(out$factorizations, path.database, pval.thr=0.05)
biological_analysis <- rbind(biological_analysis,
data.frame(methods=out$method, cancer=current_cancer, out_bio))
path.database <- "../data/bio_annotations/h.all.v6.2.symbols.gmt" #Hallmarks
print("Running biological analysis...")
out_bio <- biological_comparison(out$factorizations, path.database, pval.thr=0.05)
biological_analysis_hallmark <- rbind(biological_analysis_hallmark,
data.frame(methods=out$method, cancer=current_cancer, out_bio))
path.database <- "../data/bio_annotations/c5.all.v6.2.symbols.gmt" #GO
print("Running biological analysis...")
out_bio <- biological_comparison(out$factorizations, path.database, pval.thr=0.05)
biological_analysis_GO <- rbind(biological_analysis_GO,
data.frame(methods=out$method, cancer=current_cancer, out_bio))
}
write.table(biological_analysis, paste0(results_folder, "results_biological_analysis.txt"),
sep="\t", row.names=FALSE)
write.table(clinical_analysis, paste0(results_folder, "results_clinical_analysis.txt"),
sep="\t", row.names=FALSE)
rownames(clinical_analysis) <- c()
rownames(biological_analysis) <- c()
[1] "Now analysing ../data/cancer/aml" [1] "Running factorisation..." Generating warm start... K=11:1234 [1] "Running survival analysis..." [1] "Running clinical analysis..." [1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Now analysing ../data/cancer/breast" [1] "Running factorisation..." Generating warm start... K=11:12
Warning message: “did not converge in 10 iterations”
3456789
Warning message in iCluster2(lapply(omics, function(x) t(x)), k = num.factors + : “Algorithm didn't converge. Check convergence history fit$RI. Cluster assignments may not be stable. Try increase the number of EM iterations by max.iter”
[1] "Running survival analysis..." [1] "Running clinical analysis..." [1] "Running biological analysis..." [1] "Running biological analysis..." [1] "Running biological analysis..." [1] "Now analysing ../data/cancer/colon" [1] "Running factorisation..." Generating warm start... K=11:12345678 [1] "Running survival analysis..." [1] "Running clinical analysis..." [1] "Running biological analysis..." [1] "Running biological analysis..." [1] "Running biological analysis..." [1] "Now analysing ../data/cancer/kidney" [1] "Running factorisation..." Generating warm start... K=11:1234 [1] "Running survival analysis..." [1] "Running clinical analysis..." [1] "Running biological analysis..." [1] "Running biological analysis..." [1] "Running biological analysis..." [1] "Now analysing ../data/cancer/liver" [1] "Running factorisation..." Generating warm start... K=11:123456 [1] "Running survival analysis..." [1] "Running clinical analysis..." [1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Now analysing ../data/cancer/melanoma" [1] "Running factorisation..." Generating warm start... K=11:1
Warning message: “did not converge in 10 iterations”
23
Warning message: “did not converge in 10 iterations”
4 [1] "Running survival analysis..." [1] "Running clinical analysis..." [1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Now analysing ../data/cancer/ovarian" [1] "Running factorisation..." Generating warm start... K=11:12345678910 [1] "Running survival analysis..." [1] "Running clinical analysis..." [1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Now analysing ../data/cancer/sarcoma" [1] "Running factorisation..." Generating warm start... K=11:123456 [1] "Running survival analysis..." [1] "Running clinical analysis..." [1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
[1] "Running biological analysis..."
Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.03% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in fgsea(pathways, rnk, minSize = 15, maxSize = 500, nperm = 1000): “There are ties in the preranked stats (0.02% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.”
Saving results to file¶
Here the obtained results are saved to file in the Results folder.
In [16]:
# Export results into separated tables
write.table(biological_analysis_hallmark, paste0(results_folder, "results_biological_analysis-hallmark.txt"),
sep="\t", row.names=FALSE)
write.table(clinical_analysis, paste0(results_folder, "results_clinical_analysis-complete.txt"),
sep="\t", row.names=FALSE)
In [17]:
write.table(biological_analysis, paste0(results_folder, "results_biological_analysis-reactome.txt"),
sep="\t", row.names=FALSE)
In [92]:
write.table(biological_analysis_GO, paste0(results_folder, "go-complete.txt"),
sep="\t", row.names=FALSE)
In [19]:
save(survival_analysis, file=paste0(results_folder, '', "surv.rds"))
Plots of the obtained results with respect to biological annotations¶
Plots are then created accordingly to Figure 4 and Supp Figure 2 of the paper.
In [20]:
library(ggplot2)
In [21]:
#tiff("BioEnr_go_fig1.tiff", units="in", width=8.95, height=6.05, res=300)
min_nonZero = min(biological_analysis_hallmark[, "nonZeroFacs"])
max_nonZero = max(biological_analysis_hallmark[, "nonZeroFacs"])
g <- ggplot(biological_analysis_hallmark,
aes(x=nonZeroFacs,y=selectivity)) +
geom_point(aes(colour = methods, shape = cancer), size=10, alpha=.6, position=position_jitter(h=0, w=0.25))+
theme_bw() +
scale_color_manual(values=c('#FF6E28', '#C8961E', '#FF0000', '#0000FF', '#A0A0A0', '#FF00FF', '#48D1CC', '#00FF00')) +
ylim(floor(min((biological_analysis[,"selectivity"]*10)-.4)) / 10,
ceiling(max((biological_analysis[,"selectivity"]*10)+.2)) / 10) +
labs(title="Hallmark annotation Selectivity",
x="# metagenes (factors) enriched in at least one annotation") +
theme(plot.title = element_text(size=14,face="bold"),
axis.text = element_text(size=11),
axis.title = element_text(size=13),
legend.text=element_text(size=10)) +
ylab("Selectivity") +
labs(colour = "Methods", shape = "Cancer") +
scale_shape_manual(values=c(16, 17, 15, 3, 7, 8, 23, 18)) +
guides(color = guide_legend(order = 1),shape = guide_legend(order = 2),size = guide_legend(order = 3)) +
#scale_x_discrete() + scale_x_discrete(limits=min_nonZero:max_nonZero) +
scale_x_discrete(limits=min_nonZero:max_nonZero, labels = c(min_nonZero:max_nonZero));
g
#dev.off()
ggsave(paste0(results_folder, "biological_selectivity_hallmark.pdf"),dpi=300)
ggsave(paste0(results_folder, "biological_selectivity_hallmark.png"),dpi=300)
Saving 7 x 7 in image Saving 7 x 7 in image
In [53]:
#tiff("BioEnr_go_fig1.tiff", units="in", width=8.95, height=6.05, res=300)
min_nonZero = min(biological_analysis_hallmark[, "total_pathways"])
max_nonZero = max(biological_analysis_hallmark[, "total_pathways"])
g <- ggplot(biological_analysis_hallmark,
aes(x=nonZeroFacs,y=total_pathways)) +
geom_point(aes(colour = methods, shape = cancer), size=10, alpha=.6, position=position_jitter(h=0, w=0.25))+
theme_bw() +
scale_color_manual(values=c('#FF6E28', '#C8961E', '#FF0000', '#0000FF', '#A0A0A0', '#FF00FF', '#48D1CC', '#00FF00')) +
labs(title="Hallmark annotations (total_pathways)",
x="# metagenes (factors) enriched in at least one annotation") +
theme(plot.title = element_text(size=14,face="bold"),
axis.text = element_text(size=11),
axis.title = element_text(size=13),
legend.text=element_text(size=10)) +
ylab("total_pathways") +
scale_shape_manual(values=c(16, 17, 15, 3, 7, 8, 23, 18)) +
labs(colour = "Methods", shape = "Cancer") +
guides(color = guide_legend(order = 1),shape = guide_legend(order = 2),size = guide_legend(order = 3)) +
scale_x_discrete() + scale_x_discrete(limits=0:10) +
scale_x_discrete(limits=0:10, labels = c(0:10));
g
#dev.off()
ggsave(paste0(results_folder, "biological_hallmark_total_pathways.pdf"),dpi=300)
ggsave(paste0(results_folder, "biological_hallmark_total_pathways.png"),dpi=300)
Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale. Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale. Saving 7 x 7 in image Saving 7 x 7 in image
In [52]:
#tiff("BioEnr_go_fig1.tiff", units="in", width=8.95, height=6.05, res=300)
min_nonZero = min(biological_analysis_GO[, "total_pathways"])
max_nonZero = max(biological_analysis_GO[, "total_pathways"])
g <- ggplot(biological_analysis_GO,
aes(x=nonZeroFacs,y=total_pathways)) +
geom_point(aes(colour = methods, shape = cancer), size=10, alpha=.6, position=position_jitter(h=0, w=0.25))+
theme_bw() +
scale_color_manual(values=c('#FF6E28', '#C8961E', '#FF0000', '#0000FF', '#A0A0A0', '#FF00FF', '#48D1CC', '#00FF00')) +
labs(title="GO annotations (total_pathways)",
x="# metagenes (factors) enriched in at least one annotation") +
theme(plot.title = element_text(size=14,face="bold"),
axis.text = element_text(size=11),
axis.title = element_text(size=13),
legend.text=element_text(size=10)) +
ylab("total_pathways") +
scale_shape_manual(values=c(16, 17, 15, 3, 7, 8, 23, 18)) +
labs(colour = "Methods", shape = "Cancer") +
guides(color = guide_legend(order = 1),shape = guide_legend(order = 2),size = guide_legend(order = 3)) +
scale_x_discrete(limits=0:10, labels = c(0:10));
g
#dev.off()
ggsave(paste0(results_folder, "biological_GO_total_pathways.pdf"),dpi=300)
ggsave(paste0(results_folder, "biological_GO_total_pathways.png"),dpi=300)
Saving 7 x 7 in image Saving 7 x 7 in image
In [42]:
#tiff("BioEnr_go_fig1.tiff", units="in", width=8.95, height=6.05, res=300)
min_nonZero = min(biological_analysis_GO[, "nonZeroFacs"])
max_nonZero = max(biological_analysis_GO[, "nonZeroFacs"])
g <- ggplot(biological_analysis_GO,
aes(x=nonZeroFacs,y=selectivity)) +
geom_point(aes(colour = methods, shape = cancer), size=10, alpha=.6, position=position_jitter(h=0, w=0.25))+
theme_bw() +
scale_color_manual(values=c('#FF6E28', '#C8961E', '#FF0000', '#0000FF', '#A0A0A0', '#FF00FF', '#48D1CC', '#00FF00')) +
ylim(floor(min((biological_analysis[,"selectivity"]*10)-.4)) / 10,
ceiling(max((biological_analysis[,"selectivity"]*10)+.2)) / 10) +
labs(title="GO annotations selectivity",
x="# metagenes (factors) enriched in at least one annotation") +
theme(plot.title = element_text(size=14,face="bold"),
axis.text = element_text(size=11),
axis.title = element_text(size=13),
legend.text=element_text(size=10)) +
ylab("Selectivity") +
scale_shape_manual(values=c(16, 17, 15, 3, 7, 8, 23, 18)) +
labs(colour = "Methods", shape = "Cancer") +
guides(color = guide_legend(order = 1),shape = guide_legend(order = 2),size = guide_legend(order = 3)) +
scale_x_discrete() + scale_x_discrete(limits=min_nonZero:max_nonZero) +
scale_x_discrete(limits=min_nonZero:max_nonZero, labels = c(min_nonZero:max_nonZero));
g
#dev.off()
ggsave(paste0(results_folder, "biological_selectivity_GO.pdf"),dpi=300)
ggsave(paste0(results_folder, "biological_selectivity_GO.png"),dpi=300)
Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale. Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale. Warning message: “Removed 2 rows containing missing values (geom_point).” Saving 7 x 7 in image Warning message: “Removed 2 rows containing missing values (geom_point).” Saving 7 x 7 in image Warning message: “Removed 2 rows containing missing values (geom_point).”
In [43]:
#tiff("BioEnr_go_fig1.tiff", units="in", width=8.95, height=6.05, res=300)
min_nonZero = min(biological_analysis[, "nonZeroFacs"])
max_nonZero = max(biological_analysis[, "nonZeroFacs"])
g <- ggplot(biological_analysis,
aes(x=nonZeroFacs,y=selectivity)) +
geom_point(aes(colour = methods, shape = cancer), size=10, alpha=.6, position=position_jitter(h=0, w=0.25))+
theme_bw() +
scale_color_manual(values=c('#FF6E28', '#C8961E', '#FF0000', '#0000FF', '#A0A0A0', '#FF00FF', '#48D1CC', '#00FF00')) +
ylim(floor(min((biological_analysis[,"selectivity"]*10)-.4)) / 10,
ceiling(max((biological_analysis[,"selectivity"]*10)+.2)) / 10) +
labs(title="Reactome selectivity",
x="# metagenes (factors) enriched in at least one annotation") +
theme(plot.title = element_text(size=14,face="bold"),
axis.text = element_text(size=11),
axis.title = element_text(size=13),
legend.text=element_text(size=10)) +
ylab("Selectivity") +
labs(colour = "Methods", shape = "Cancer") +
scale_shape_manual(values=c(16, 17, 15, 3, 7, 8, 23, 18)) +
guides(color = guide_legend(order = 1),shape = guide_legend(order = 2),size = guide_legend(order = 3)) +
scale_x_discrete() + scale_x_discrete(limits=min_nonZero:max_nonZero) +
scale_x_discrete(limits=min_nonZero:max_nonZero, labels = c(min_nonZero:max_nonZero));
g
#dev.off()
ggsave(paste0(results_folder, "Reactome_biological_selectivity.pdf"),dpi=300)
ggsave(paste0(results_folder, "Reactome_biological_selectivity.png"),dpi=300)
Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale. Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale. Warning message: “Removed 2 rows containing missing values (geom_point).” Saving 7 x 7 in image Warning message: “Removed 2 rows containing missing values (geom_point).” Saving 7 x 7 in image Warning message: “Removed 2 rows containing missing values (geom_point).”
In [50]:
#tiff("BioEnr_go_fig1.tiff", units="in", width=8.95, height=6.05, res=300)
min_nonZero = min(biological_analysis[, "total_pathways"])
max_nonZero = max(biological_analysis[, "total_pathways"])
g <- ggplot(biological_analysis,
aes(x=nonZeroFacs,y=total_pathways)) +
geom_point(aes(colour = methods, shape = cancer), size=10, alpha=.6, position=position_jitter(h=0, w=0.25))+
theme_bw() +
scale_color_manual(values=c('#FF6E28', '#C8961E', '#FF0000', '#0000FF', '#A0A0A0', '#FF00FF', '#48D1CC', '#00FF00')) +
labs(title="Reactome annotations (total_pathways)",
x="# metagenes (factors) enriched in at least one annotation") +
theme(plot.title = element_text(size=14,face="bold"),
axis.text = element_text(size=11),
axis.title = element_text(size=13),
legend.text=element_text(size=10)) +
ylab("total_pathways") +
scale_shape_manual(values=c(16, 17, 15, 3, 7, 8, 23, 18)) +
labs(colour = "Methods", shape = "Cancer") +
guides(color = guide_legend(order = 1),shape = guide_legend(order = 2),size = guide_legend(order = 3)) +
scale_x_discrete() + scale_x_discrete(limits=0:10)
#scale_x_discrete(limits=min_nonZero:max_nonZero, labels = c(min_nonZero:max_nonZero));
g
#dev.off()
ggsave(paste0(results_folder, "reactome_annotations_total_pathways.pdf"),dpi=300)
ggsave(paste0(results_folder, "reactome_annotations_total_pathways.png"),dpi=300)
Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale. Saving 7 x 7 in image Saving 7 x 7 in image
Plots of the obtained results with respect to clinical annotations¶
Plots are then created accordingly to Figure 4 and Supp Figure 2 of the paper.
In [27]:
library(ggplot2)
In [28]:
#tiff("BioEnr_go_fig1.tiff", units="in", width=8.95, height=6.05, res=300)
min_nonZero = min(clinical_analysis[, "nonZeroFacs"])
max_nonZero = max(clinical_analysis[, "nonZeroFacs"])
g <- ggplot(clinical_analysis,
aes(x=nonZeroFacs, y=selectivity)) +
geom_point(aes(colour = methods, shape = cancer), size=10, alpha=.6, position=position_jitter(h=0, w=0.25))+
theme_bw() +
scale_color_manual(values=c('#FF6E28', '#C8961E', '#FF0000', '#0000FF', '#A0A0A0', '#FF00FF', '#48D1CC', '#00FF00')) +
ylim(floor(min((clinical_analysis[,"selectivity"]*10)-.4)) / 10,
ceiling(max((clinical_analysis[,"selectivity"]*10)+.2)) / 10) +
labs(title="Clinical annotations",
x="# metagenes (factors) enriched in at least one annotation") +
theme(plot.title = element_text(size=14,face="bold"),
axis.text = element_text(size=11),
axis.title = element_text(size=13),
legend.text=element_text(size=10)) +
ylab("Selectivity") +
scale_shape_manual(values=c(16, 17, 15, 3, 7, 8, 23, 18)) +
labs(colour = "Methods",shape="Cancer") +
guides(color = guide_legend(order = 1),shape = guide_legend(order = 2),size = guide_legend(order = 3)) +
#scale_x_discrete() + scale_x_discrete(limits=min_nonZero:max_nonZero) +
scale_x_discrete(limits=min_nonZero:max_nonZero, labels = c(min_nonZero:max_nonZero));
g
#dev.off()
ggsave(paste0(results_folder, "clinical_annotations.pdf"),dpi=300)
ggsave(paste0(results_folder, "clinical_annotations.png"),dpi=300)
Saving 7 x 7 in image Saving 7 x 7 in image
In [39]:
#tiff("BioEnr_go_fig1.tiff", units="in", width=8.95, height=6.05, res=300)
min_nonZero = min(clinical_analysis[, "total_sig_clinical_measures"])
max_nonZero = max(clinical_analysis[, "total_sig_clinical_measures"])
g <- ggplot(clinical_analysis,
aes(x=nonZeroFacs, y=total_sig_clinical_measures)) +
geom_point(aes(colour = methods, shape = cancer), size=10, alpha=.6, position=position_jitter(h=0, w=0.25))+
theme_bw() +
scale_color_manual(values=c('#FF6E28', '#C8961E', '#FF0000', '#0000FF', '#A0A0A0', '#FF00FF', '#48D1CC', '#00FF00')) +
labs(title="Clinical annotations",
x="# metagenes (factors) enriched in at least one annotation") +
theme(plot.title = element_text(size=14,face="bold"),
axis.text = element_text(size=11),
axis.title = element_text(size=13),
legend.text=element_text(size=10)) +
ylab("total_sig_clinical_measures") +
scale_shape_manual(values=c(16, 17, 15, 3, 7, 8, 23, 18)) +
labs(colour = "Methods",shape="Cancer") +
guides(color = guide_legend(order = 1),shape = guide_legend(order = 2),size = guide_legend(order = 3)) +
#scale_x_discrete() + scale_x_discrete(limits=min_nonZero:max_nonZero) +
scale_x_discrete(limits=0:10, labels = c(0:10));
g
#dev.off()
ggsave(paste0(results_folder, "clinical_annotations_number.pdf"),dpi=300)
ggsave(paste0(results_folder, "clinical_annotations_number.png"),dpi=300)
Saving 7 x 7 in image Saving 7 x 7 in image
In [54]:
survival_analysis$padj <- unlist(survival_analysis$V2)
survival_analysis$log10padj <- -1*log10(unlist(survival_analysis$V2))
In [72]:
BiocManager::install("survcomp")
library(survcomp)
'getOption("repos")' replaces Bioconductor standard repositories, see
'?repositories' for details
replacement repositories:
CRAN: https://cran.r-project.org
Bioconductor version 3.8 (BiocManager 1.30.16), R 3.5.1 (2018-07-02)
Installing package(s) 'survcomp'
also installing the dependencies ‘listenv’, ‘parallelly’, ‘future’, ‘globals’, ‘future.apply’, ‘progressr’, ‘SQUAREM’, ‘lava’, ‘rpart’, ‘class’, ‘prodlim’, ‘ipred’, ‘SuppDists’, ‘survivalROC’, ‘bootstrap’, ‘rmeta’
Updating HTML index of packages in '.Library'
Making 'packages.html' ...
done
Old packages: 'ade4', 'backports', 'beeswarm', 'BH', 'bibtex', 'BiocManager',
'BiocParallel', 'bitops', 'blob', 'boot', 'cli', 'clipr', 'clue', 'cluster',
'codetools', 'colorspace', 'corrplot', 'cowplot', 'cpp11', 'crayon', 'curl',
'data.table', 'DBI', 'DEoptimR', 'Deriv', 'desc', 'digest', 'doParallel',
'dplyr', 'evaluate', 'fansi', 'farver', 'fastmatch', 'fit.models', 'foreach',
'forecast', 'formatR', 'fs', 'gdata', 'generics', 'GenomeInfoDb', 'GGally',
'ggplot2', 'ggrepel', 'glue', 'GPArotation', 'gplots', 'gtools', 'htmltools',
'ICtest', 'IRdisplay', 'IRkernel', 'isoband', 'iterators', 'jsonlite',
'KernSmooth', 'labeling', 'lattice', 'lifecycle', 'lmtest', 'lubridate',
'magrittr', 'MASS', 'Matrix', 'matrixStats', 'mclust', 'mime', 'mnormt',
'MultiAssayExperiment', 'mvtnorm', 'nlme', 'NMF', 'nnet', 'pbdZMQ', 'pcaPP',
'pillar', 'pixmap', 'pkgbuild', 'pkgload', 'pkgmaker', 'plyr', 'pracma',
'processx', 'ps', 'psych', 'quantmod', 'r.jive', 'R6', 'rappdirs',
'RColorBrewer', 'Rcpp', 'RcppArmadillo', 'RCurl', 'repr', 'reshape',
'reticulate', 'rlang', 'rngtools', 'robust', 'robustbase', 'rprojroot',
'rrcov', 'scales', 'snow', 'sp', 'statmod', 'stringi', 'survey', 'survival',
'tensorBSS', 'testthat', 'tibble', 'tidyr', 'tidyselect', 'tinytex', 'tsBSS',
'tseries', 'TTR', 'tzdb', 'utf8', 'uuid', 'vctrs', 'viridisLite', 'withr',
'xfun', 'xml2', 'xts', 'yaml', 'zoo'
Loading required package: prodlim
In [62]:
# Survival has whether a thing was significantly enriched, so for each row, we want to calculate the total
# that were significantly associated with survival
columns <- c('V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10')
surv_rows <- as.matrix(survival_analysis[columns])
survival_analysis$num_sig_facs <- rowSums(surv_rows <= 0.05)
# Get the Fishers exact p value
In [93]:
survival_analysis
| methods | cancer | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | cancer.1 | facs | padj | log10padj | num_sig_facs | combinedvals | log10pvals | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <fct> | <fct> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list[,1]> | <dbl> | <dbl> | <dbl> | <dbl> | <list> | <dbl> | |
| scivae | scivae | aml | 0.03498773 | 0.4034696 | 0.851101 | 0.01864446 | 0.01864446 | 0.851101 | 0.001500313 | 0.03498773 | 0.851101 | 0.5272676 | aml | 10 | 4.034696e-01 | 0.394189196 | 5 | 0.03498773 | 1.456084214 |
| RGCCA | RGCCA | aml | 0.577179 | 0.01854128 | 0.7869179 | 0.009525792 | 0.7869179 | 0.3677111 | 0.7869179 | 0.678091 | 0.9857022 | 0.9414523 | aml | 10 | 1.854128e-02 | 1.731860249 | 2 | 0.577179 | 0.238689464 |
| MCIA | MCIA | aml | 0.09845619 | 0.01660443 | 0.2290268 | 0.4954931 | 0.4954931 | 0.01660443 | 0.4643065 | 0.9984336 | 0.2290268 | 0.8920132 | aml | 10 | 1.660443e-02 | 1.779775923 | 2 | 0.09845619 | 1.006756953 |
| iCluster | iCluster | aml | 0.9446362 | 0.07925052 | 0.6462015 | 0.01479379 | 0.4282431 | 0.3432563 | 0.8009971 | 0.4282431 | 0.5179414 | 0.5687697 | aml | 10 | 7.925052e-02 | 1.100997853 | 1 | 0.9446362 | 0.024735401 |
| intNMF | intNMF | aml | 0.5612005 | 0.5612005 | 0.5732093 | 0.04581529 | 0.1068234 | 0.08743822 | 0.04581529 | 0.1602273 | 0.04581529 | 0.9081898 | aml | 10 | 5.612005e-01 | 0.250881932 | 3 | 0.5612005 | 0.250881932 |
| JIVE | JIVE | aml | 0.1653313 | 0.9969533 | 0.002351374 | 0.4220073 | 0.8604182 | 0.7539287 | 0.1653313 | 0.8604182 | 0.3424456 | 0.8604182 | aml | 10 | 9.969533e-01 | 0.001325206 | 1 | 0.1653313 | 0.781644862 |
| tICA | tICA | aml | 0.8514052 | 0.8514052 | 0.8514052 | 0.01541895 | 0.8514052 | 0.04319786 | 0.8514052 | 0.5320929 | 0.1294422 | 0.1493582 | aml | 10 | 8.514052e-01 | 0.069863692 | 2 | 0.8514052 | 0.069863692 |
| scivae1 | scivae | breast | 0.4850375 | 0.5367704 | 0.4850375 | 0.5367704 | 0.9817527 | 0.8851041 | 0.4850375 | 0.5367704 | 0.8851041 | 0.677522 | breast | 10 | 5.367704e-01 | 0.270211439 | 0 | 0.4850375 | 0.314224699 |
| RGCCA1 | RGCCA | breast | 0.7228516 | 0.08150212 | 0.08150212 | 0.7228516 | 0.3663289 | 0.001529672 | 0.06566508 | 0.7228516 | 0.7228516 | 0.7228516 | breast | 10 | 8.150212e-02 | 1.088831105 | 1 | 0.7228516 | 0.140950868 |
| MCIA1 | MCIA | breast | 0.6446539 | 0.05390532 | 0.6446539 | 0.6117583 | 0.4955347 | 0.05390532 | 0.05629511 | 0.05880803 | 0.02298899 | 0.6117583 | breast | 10 | 5.390532e-02 | 1.268368350 | 1 | 0.6446539 | 0.190673377 |
| iCluster1 | iCluster | breast | 0.9950847 | 0.4977844 | 0.9334352 | 0.7299385 | 0.4977844 | 0.3958043 | 0.05888213 | 0.0863816 | 0.0863816 | 0.05888213 | breast | 10 | 4.977844e-01 | 0.302958733 | 0 | 0.9950847 | 0.002139965 |
| intNMF1 | intNMF | breast | 0.05860942 | 0.3086518 | 0.3221858 | 0.2857767 | 0.9150312 | 0.0009279364 | 0.2857767 | 0.3086518 | 0.3974081 | 0.3221858 | breast | 10 | 3.086518e-01 | 0.510531172 | 1 | 0.05860942 | 1.232032575 |
| JIVE1 | JIVE | breast | 0.8161499 | 0.07765908 | 0.8150449 | 0.1793233 | 0.8168842 | 0.003104433 | 0.07765908 | 0.07765908 | 0.7909869 | 0.07765908 | breast | 10 | 7.765908e-02 | 1.109807770 | 1 | 0.8161499 | 0.088230051 |
| tICA1 | tICA | breast | 0.0930841 | 0.003750981 | 0.5772502 | 0.003750981 | 0.01029604 | 0.4745634 | 0.8910863 | 0.8910863 | 0.8910863 | 0.1747179 | breast | 10 | 3.750981e-03 | 2.425855082 | 3 | 0.0930841 | 1.031124512 |
| scivae2 | scivae | colon | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.8800942 | 0.87352 | 0.87352 | colon | 10 | 8.735200e-01 | 0.058727143 | 0 | 0.87352 | 0.058727143 |
| RGCCA2 | RGCCA | colon | 0.6845027 | 0.6845027 | 0.4534483 | 0.2783878 | 0.4696353 | 0.6845027 | 0.6845027 | 0.6845027 | 0.6845027 | 0.9316657 | colon | 10 | 6.845027e-01 | 0.164624850 | 0 | 0.6845027 | 0.164624850 |
| MCIA2 | MCIA | colon | 0.7268303 | 0.7268303 | 0.3866666 | 0.2567604 | 0.4170189 | 0.8204053 | 0.7268303 | 0.7268303 | 0.7268303 | 0.0183408 | colon | 10 | 7.268303e-01 | 0.138566987 | 1 | 0.7268303 | 0.138566987 |
| iCluster2 | iCluster | colon | 0.9430608 | 0.9430608 | 0.8879562 | 0.9430608 | 0.9430608 | 0.8879562 | 0.9229776 | 0.9430608 | 0.9430608 | 0.9430608 | colon | 10 | 9.430608e-01 | 0.025460286 | 0 | 0.9430608 | 0.025460286 |
| intNMF2 | intNMF | colon | 0.9728918 | 0.790393 | 0.7384476 | 0.7384476 | 0.9728918 | 0.790393 | 0.5887348 | 0.790393 | 0.9728918 | 0.790393 | colon | 10 | 7.903930e-01 | 0.102156916 | 0 | 0.9728918 | 0.011935475 |
| JIVE2 | JIVE | colon | 0.7750902 | 0.7750902 | 0.6810251 | 0.6810251 | 0.6810251 | 0.7750902 | 0.6810251 | 0.6810251 | 0.6810251 | 0.6810251 | colon | 10 | 7.750902e-01 | 0.110647748 | 0 | 0.7750902 | 0.110647748 |
| tICA2 | tICA | colon | 0.6067935 | 0.7504688 | 0.3362704 | 0.6067935 | 0.6067935 | 0.6067935 | 0.6067935 | 0.6067935 | 0.8593003 | 0.5142843 | colon | 10 | 7.504688e-01 | 0.124667362 | 0 | 0.6067935 | 0.216959071 |
| scivae3 | scivae | kidney | 0.4374853 | 0.1212966 | 0.6681088 | 0.9648711 | 0.2752951 | 0.4374853 | 0.4374853 | 0.1212966 | 0.4374853 | 0.2752951 | kidney | 10 | 1.212966e-01 | 0.916151255 | 0 | 0.4374853 | 0.359036493 |
| RGCCA3 | RGCCA | kidney | 0.5537313 | 8.713777e-05 | 0.5537313 | 0.02758201 | 0.5366432 | 0.5868637 | 0.5868637 | 0.5366432 | 0.3509294 | 0.5868637 | kidney | 10 | 8.713777e-05 | 4.059793547 | 2 | 0.5537313 | 0.256700919 |
| MCIA3 | MCIA | kidney | 0.6770211 | 0.5281441 | 2.149008e-05 | 0.796189 | 0.796189 | 0.001663955 | 0.1363642 | 0.796189 | 0.1363642 | 0.796189 | kidney | 10 | 5.281441e-01 | 0.277247607 | 2 | 0.6770211 | 0.169397807 |
| iCluster3 | iCluster | kidney | 0.4408349 | 0.0246705 | 0.5431745 | 0.0246705 | 0.323935 | 0.4408349 | 0.4408349 | 0.8185494 | 0.2502599 | 0.5431745 | kidney | 10 | 2.467050e-02 | 1.607821997 | 2 | 0.4408349 | 0.355724004 |
| intNMF3 | intNMF | kidney | 0.2741288 | 0.8344266 | 0.04722633 | 0.6897873 | 0.04910352 | 0.2132502 | 0.7765445 | 0.0139217 | 0.6652412 | 0.001561997 | kidney | 10 | 8.344266e-01 | 0.078611846 | 4 | 0.2741288 | 0.562045306 |
| JIVE3 | JIVE | kidney | 0.4912491 | 0.002390182 | 0.05585059 | 0.8306092 | 0.05585059 | 0.05585059 | 0.05585059 | 0.9652067 | 0.6703824 | 0.8306092 | kidney | 10 | 2.390182e-03 | 2.621568969 | 1 | 0.4912491 | 0.308698234 |
| tICA3 | tICA | kidney | 0.7082194 | 0.7082194 | 0.9803135 | 0.9803135 | 0.9803135 | 0.7082194 | 0.9803135 | 9.346661e-06 | 0.008954057 | 0.7082194 | kidney | 10 | 7.082194e-01 | 0.149832201 | 2 | 0.7082194 | 0.149832201 |
| scivae4 | scivae | liver | 0.9085322 | 0.8852581 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | liver | 10 | 8.852581e-01 | 0.052930075 | 0 | 0.9085322 | 0.041659698 |
| RGCCA4 | RGCCA | liver | 0.6531042 | 0.1285983 | 0.1352407 | 0.5760708 | 0.6531042 | 0.6983761 | 0.000640696 | 0.2450111 | 0.8488238 | 0.6983761 | liver | 10 | 1.285983e-01 | 0.890764684 | 1 | 0.6531042 | 0.185017501 |
| MCIA4 | MCIA | liver | 0.8014378 | 0.007538289 | 0.3522051 | 0.6584276 | 0.305833 | 0.005877229 | 0.1414228 | 0.365308 | 0.0424988 | 0.9747084 | liver | 10 | 7.538289e-03 | 2.122727189 | 3 | 0.8014378 | 0.096130175 |
| iCluster4 | iCluster | liver | 0.06324608 | 0.9816484 | 0.9816484 | 0.9952585 | 0.9952585 | 0.9816484 | 0.9952585 | 0.06324608 | 0.6784651 | 0.257104 | liver | 10 | 9.816484e-01 | 0.008044039 | 0 | 0.06324608 | 1.198966397 |
| intNMF4 | intNMF | liver | 0.6494593 | 0.6494593 | 0.1106518 | 0.6494593 | 0.6494593 | 0.6494593 | 0.6494593 | 0.1106518 | 0.1106518 | 0.699813 | liver | 10 | 6.494593e-01 | 0.187448055 | 0 | 0.6494593 | 0.187448055 |
| JIVE4 | JIVE | liver | 0.7431439 | 0.0009248186 | 0.6889358 | 0.7464017 | 0.6889358 | 0.2570553 | 0.01280808 | 0.6889358 | 0.6889358 | 0.7464017 | liver | 10 | 9.248186e-04 | 3.033943428 | 2 | 0.7431439 | 0.128927095 |
| tICA4 | tICA | liver | 0.9424653 | 0.454957 | 0.9424653 | 0.454957 | 0.454957 | 0.454957 | 0.454957 | 0.454957 | 0.6985288 | 0.9424653 | liver | 10 | 4.549570e-01 | 0.342029628 | 0 | 0.9424653 | 0.025734638 |
| scivae5 | scivae | melanoma | 0.1304159 | 0.07654574 | 0.1137157 | 0.3374772 | 0.1304159 | 0.07654574 | 0.758011 | 0.1137157 | 0.07654574 | 0.3979006 | melanoma | 10 | 7.654574e-02 | 1.116078975 | 0 | 0.1304159 | 0.884669577 |
| RGCCA5 | RGCCA | melanoma | 0.008763755 | 0.6675814 | 8.732694e-08 | 0.742436 | 0.5064252 | 0.5064252 | 0.6675814 | 0.03494139 | 0.07538833 | 0.6675814 | melanoma | 10 | 6.675814e-01 | 0.175495756 | 3 | 0.008763755 | 2.057309790 |
| MCIA5 | MCIA | melanoma | 0.09323865 | 0.05891835 | 0.0002436218 | 0.804762 | 0.0008816326 | 0.03279005 | 0.7119304 | 0.2168784 | 0.03386561 | 0.8038506 | melanoma | 10 | 5.891835e-02 | 1.229749441 | 4 | 0.09323865 | 1.030404016 |
| iCluster5 | iCluster | melanoma | 0.0009345873 | 3.75896e-06 | 0.5990119 | 0.7279161 | 0.7279161 | 0.890351 | 0.5990119 | 0.5990119 | 0.3708371 | 0.08171892 | melanoma | 10 | 3.758960e-06 | 5.424932342 | 2 | 0.0009345873 | 3.029380130 |
| intNMF5 | intNMF | melanoma | 0.1465673 | 0.6719592 | 0.1465673 | 0.005668112 | 0.1465673 | 0.3740275 | 0.4502339 | 0.07138578 | 9.996027e-06 | 0.1736893 | melanoma | 10 | 6.719592e-01 | 0.172657124 | 2 | 0.1465673 | 0.833962855 |
| JIVE5 | JIVE | melanoma | 0.08815332 | 0.5182627 | 0.3125852 | 8.295589e-06 | 0.4521105 | 0.1132352 | 0.002069068 | 0.4005567 | 0.8126514 | 0.5420346 | melanoma | 10 | 5.182627e-01 | 0.285450062 | 2 | 0.08815332 | 1.054761324 |
| tICA5 | tICA | melanoma | 0.1481763 | 0.1250969 | 1.085854e-05 | 0.4590133 | 0.08744965 | 0.4994696 | 0.1250969 | 0.09207737 | 0.09207737 | 0.003079848 | melanoma | 10 | 1.250969e-01 | 0.902753468 | 2 | 0.1481763 | 0.829221333 |
| scivae6 | scivae | ovarian | 0.121396 | 0.02921971 | 0.1367591 | 0.02921971 | 0.05835234 | 0.02921971 | 0.02921971 | 0.7325978 | 0.3023191 | 0.1006367 | ovarian | 10 | 2.921971e-02 | 1.534324112 | 4 | 0.121396 | 0.915795632 |
| RGCCA6 | RGCCA | ovarian | 0.9687845 | 0.3328547 | 0.9687845 | 0.9687845 | 0.3328547 | 0.9687845 | 0.4094383 | 0.9687845 | 0.9687845 | 0.9687845 | ovarian | 10 | 3.328547e-01 | 0.477745367 | 0 | 0.9687845 | 0.013772812 |
| MCIA6 | MCIA | ovarian | 0.9963681 | 0.9963681 | 0.4856017 | 0.8644176 | 0.9963681 | 0.9963681 | 0.9963681 | 0.4856017 | 0.4856017 | 0.9630502 | ovarian | 10 | 9.963681e-01 | 0.001580192 | 0 | 0.9963681 | 0.001580192 |
| iCluster6 | iCluster | ovarian | 0.9684332 | 0.9684332 | 0.9684332 | 0.9684332 | 0.8815489 | 0.9684332 | 0.8815489 | 0.9684332 | 0.8815489 | 0.9684332 | ovarian | 10 | 9.684332e-01 | 0.013930335 | 0 | 0.9684332 | 0.013930335 |
| intNMF6 | intNMF | ovarian | 0.9620995 | 0.9620995 | 0.4585659 | 0.9620995 | 0.9620995 | 0.9620995 | 0.4585659 | 0.4585659 | 0.4585659 | 0.9620995 | ovarian | 10 | 9.620995e-01 | 0.016780000 | 0 | 0.9620995 | 0.016780000 |
| JIVE6 | JIVE | ovarian | 0.859074 | 0.859074 | 0.3109906 | 0.859074 | 0.4184484 | 0.3109906 | 0.859074 | 0.770928 | 0.3109906 | 0.859074 | ovarian | 10 | 8.590740e-01 | 0.065969410 | 0 | 0.859074 | 0.065969410 |
| tICA6 | tICA | ovarian | 0.9062334 | 0.9062334 | 0.9062334 | 0.4188126 | 0.9062334 | 0.9062334 | 0.9062334 | 0.9062334 | 0.9062334 | 0.4188126 | ovarian | 10 | 9.062334e-01 | 0.042759923 | 0 | 0.9062334 | 0.042759923 |
| scivae7 | scivae | sarcoma | 0.8784908 | 0.9881741 | 0.4992487 | 0.4634695 | 0.2841599 | 0.4634695 | 0.4634695 | 0.4992487 | 0.4634695 | 0.7462966 | sarcoma | 10 | 9.881741e-01 | 0.005166512 | 0 | 0.8784908 | 0.056262799 |
| RGCCA7 | RGCCA | sarcoma | 0.3003338 | 0.009348357 | 0.003162501 | 0.419127 | 0.3228952 | 0.7294782 | 0.5759544 | 0.8200843 | 0.1237538 | 0.09980813 | sarcoma | 10 | 9.348357e-03 | 2.029264694 | 2 | 0.3003338 | 0.522395733 |
| MCIA7 | MCIA | sarcoma | 0.257462 | 0.0004413724 | 0.01089201 | 0.3778846 | 0.6572043 | 0.4851062 | 0.6572043 | 0.257462 | 0.4723466 | 0.3778846 | sarcoma | 10 | 4.413724e-04 | 3.355194794 | 2 | 0.257462 | 0.589286887 |
| iCluster7 | iCluster | sarcoma | 0.3697605 | 0.1276649 | 0.01745401 | 0.8435923 | 0.3697605 | 0.1805633 | 0.007330939 | 0.007330939 | 0.1849386 | 0.3697605 | sarcoma | 10 | 1.276649e-01 | 0.893928475 | 3 | 0.3697605 | 0.432079510 |
| intNMF7 | intNMF | sarcoma | 0.1542796 | 0.123546 | 0.002268153 | 0.123546 | 0.6661077 | 0.0148154 | 0.8502676 | 0.686137 | 0.001449921 | 0.5219471 | sarcoma | 10 | 1.235460e-01 | 0.908171248 | 3 | 0.1542796 | 0.811691426 |
| JIVE7 | JIVE | sarcoma | 0.1218231 | 0.1218231 | 0.003316721 | 0.2426025 | 0.6901569 | 0.8813984 | 0.6901569 | 0.8512053 | 0.1937397 | 0.2426025 | sarcoma | 10 | 1.218231e-01 | 0.914270337 | 1 | 0.1218231 | 0.914270337 |
| tICA7 | tICA | sarcoma | 0.8819494 | 0.09647813 | 0.2658254 | 0.001811467 | 0.4997008 | 0.2658254 | 0.3081159 | 0.7623773 | 0.2658254 | 0.05267573 | sarcoma | 10 | 9.647813e-02 | 1.015571108 | 1 | 0.8819494 | 0.054556313 |
In [83]:
# Survival has whether a thing was significantly enriched, so for each row, we want to calculate the total
# that were significantly associated with survival
columns <- c('V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10')
surv_rows <- as.matrix(survival_analysis[columns])
survival_analysis$num_sig_facs <- rowSums(surv_rows <= 0.05)
# Get the Fishers exact p value
# combine p values using survcomp package
values <- list()
for (i in 1:nrow(surv_rows))
values[[i]] = combine.test(surv_rows[i], 10, method ="fisher",
hetero = FALSE, na.rm = FALSE)
survival_analysis$combinedvals <- values
survival_analysis$log10pvals <- -1 * log10(unlist(values))
survival_analysis
In [86]:
survival_analysis$combinedvals <- values
In [89]:
survival_analysis$log10pvals <- -1 * log10(unlist(values))
| methods | cancer | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | cancer.1 | facs | padj | log10padj | num_sig_facs | combinedvals | log10pvals | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <fct> | <fct> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list[,1]> | <dbl> | <dbl> | <dbl> | <dbl> | <list> | <dbl> | |
| scivae | scivae | aml | 0.03498773 | 0.4034696 | 0.851101 | 0.01864446 | 0.01864446 | 0.851101 | 0.001500313 | 0.03498773 | 0.851101 | 0.5272676 | aml | 10 | 4.034696e-01 | 0.394189196 | 5 | 0.03498773 | 1.456084214 |
| RGCCA | RGCCA | aml | 0.577179 | 0.01854128 | 0.7869179 | 0.009525792 | 0.7869179 | 0.3677111 | 0.7869179 | 0.678091 | 0.9857022 | 0.9414523 | aml | 10 | 1.854128e-02 | 1.731860249 | 2 | 0.577179 | 0.238689464 |
| MCIA | MCIA | aml | 0.09845619 | 0.01660443 | 0.2290268 | 0.4954931 | 0.4954931 | 0.01660443 | 0.4643065 | 0.9984336 | 0.2290268 | 0.8920132 | aml | 10 | 1.660443e-02 | 1.779775923 | 2 | 0.09845619 | 1.006756953 |
| iCluster | iCluster | aml | 0.9446362 | 0.07925052 | 0.6462015 | 0.01479379 | 0.4282431 | 0.3432563 | 0.8009971 | 0.4282431 | 0.5179414 | 0.5687697 | aml | 10 | 7.925052e-02 | 1.100997853 | 1 | 0.9446362 | 0.024735401 |
| intNMF | intNMF | aml | 0.5612005 | 0.5612005 | 0.5732093 | 0.04581529 | 0.1068234 | 0.08743822 | 0.04581529 | 0.1602273 | 0.04581529 | 0.9081898 | aml | 10 | 5.612005e-01 | 0.250881932 | 3 | 0.5612005 | 0.250881932 |
| JIVE | JIVE | aml | 0.1653313 | 0.9969533 | 0.002351374 | 0.4220073 | 0.8604182 | 0.7539287 | 0.1653313 | 0.8604182 | 0.3424456 | 0.8604182 | aml | 10 | 9.969533e-01 | 0.001325206 | 1 | 0.1653313 | 0.781644862 |
| tICA | tICA | aml | 0.8514052 | 0.8514052 | 0.8514052 | 0.01541895 | 0.8514052 | 0.04319786 | 0.8514052 | 0.5320929 | 0.1294422 | 0.1493582 | aml | 10 | 8.514052e-01 | 0.069863692 | 2 | 0.8514052 | 0.069863692 |
| scivae1 | scivae | breast | 0.4850375 | 0.5367704 | 0.4850375 | 0.5367704 | 0.9817527 | 0.8851041 | 0.4850375 | 0.5367704 | 0.8851041 | 0.677522 | breast | 10 | 5.367704e-01 | 0.270211439 | 0 | 0.4850375 | 0.314224699 |
| RGCCA1 | RGCCA | breast | 0.7228516 | 0.08150212 | 0.08150212 | 0.7228516 | 0.3663289 | 0.001529672 | 0.06566508 | 0.7228516 | 0.7228516 | 0.7228516 | breast | 10 | 8.150212e-02 | 1.088831105 | 1 | 0.7228516 | 0.140950868 |
| MCIA1 | MCIA | breast | 0.6446539 | 0.05390532 | 0.6446539 | 0.6117583 | 0.4955347 | 0.05390532 | 0.05629511 | 0.05880803 | 0.02298899 | 0.6117583 | breast | 10 | 5.390532e-02 | 1.268368350 | 1 | 0.6446539 | 0.190673377 |
| iCluster1 | iCluster | breast | 0.9950847 | 0.4977844 | 0.9334352 | 0.7299385 | 0.4977844 | 0.3958043 | 0.05888213 | 0.0863816 | 0.0863816 | 0.05888213 | breast | 10 | 4.977844e-01 | 0.302958733 | 0 | 0.9950847 | 0.002139965 |
| intNMF1 | intNMF | breast | 0.05860942 | 0.3086518 | 0.3221858 | 0.2857767 | 0.9150312 | 0.0009279364 | 0.2857767 | 0.3086518 | 0.3974081 | 0.3221858 | breast | 10 | 3.086518e-01 | 0.510531172 | 1 | 0.05860942 | 1.232032575 |
| JIVE1 | JIVE | breast | 0.8161499 | 0.07765908 | 0.8150449 | 0.1793233 | 0.8168842 | 0.003104433 | 0.07765908 | 0.07765908 | 0.7909869 | 0.07765908 | breast | 10 | 7.765908e-02 | 1.109807770 | 1 | 0.8161499 | 0.088230051 |
| tICA1 | tICA | breast | 0.0930841 | 0.003750981 | 0.5772502 | 0.003750981 | 0.01029604 | 0.4745634 | 0.8910863 | 0.8910863 | 0.8910863 | 0.1747179 | breast | 10 | 3.750981e-03 | 2.425855082 | 3 | 0.0930841 | 1.031124512 |
| scivae2 | scivae | colon | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.8800942 | 0.87352 | 0.87352 | colon | 10 | 8.735200e-01 | 0.058727143 | 0 | 0.87352 | 0.058727143 |
| RGCCA2 | RGCCA | colon | 0.6845027 | 0.6845027 | 0.4534483 | 0.2783878 | 0.4696353 | 0.6845027 | 0.6845027 | 0.6845027 | 0.6845027 | 0.9316657 | colon | 10 | 6.845027e-01 | 0.164624850 | 0 | 0.6845027 | 0.164624850 |
| MCIA2 | MCIA | colon | 0.7268303 | 0.7268303 | 0.3866666 | 0.2567604 | 0.4170189 | 0.8204053 | 0.7268303 | 0.7268303 | 0.7268303 | 0.0183408 | colon | 10 | 7.268303e-01 | 0.138566987 | 1 | 0.7268303 | 0.138566987 |
| iCluster2 | iCluster | colon | 0.9430608 | 0.9430608 | 0.8879562 | 0.9430608 | 0.9430608 | 0.8879562 | 0.9229776 | 0.9430608 | 0.9430608 | 0.9430608 | colon | 10 | 9.430608e-01 | 0.025460286 | 0 | 0.9430608 | 0.025460286 |
| intNMF2 | intNMF | colon | 0.9728918 | 0.790393 | 0.7384476 | 0.7384476 | 0.9728918 | 0.790393 | 0.5887348 | 0.790393 | 0.9728918 | 0.790393 | colon | 10 | 7.903930e-01 | 0.102156916 | 0 | 0.9728918 | 0.011935475 |
| JIVE2 | JIVE | colon | 0.7750902 | 0.7750902 | 0.6810251 | 0.6810251 | 0.6810251 | 0.7750902 | 0.6810251 | 0.6810251 | 0.6810251 | 0.6810251 | colon | 10 | 7.750902e-01 | 0.110647748 | 0 | 0.7750902 | 0.110647748 |
| tICA2 | tICA | colon | 0.6067935 | 0.7504688 | 0.3362704 | 0.6067935 | 0.6067935 | 0.6067935 | 0.6067935 | 0.6067935 | 0.8593003 | 0.5142843 | colon | 10 | 7.504688e-01 | 0.124667362 | 0 | 0.6067935 | 0.216959071 |
| scivae3 | scivae | kidney | 0.4374853 | 0.1212966 | 0.6681088 | 0.9648711 | 0.2752951 | 0.4374853 | 0.4374853 | 0.1212966 | 0.4374853 | 0.2752951 | kidney | 10 | 1.212966e-01 | 0.916151255 | 0 | 0.4374853 | 0.359036493 |
| RGCCA3 | RGCCA | kidney | 0.5537313 | 8.713777e-05 | 0.5537313 | 0.02758201 | 0.5366432 | 0.5868637 | 0.5868637 | 0.5366432 | 0.3509294 | 0.5868637 | kidney | 10 | 8.713777e-05 | 4.059793547 | 2 | 0.5537313 | 0.256700919 |
| MCIA3 | MCIA | kidney | 0.6770211 | 0.5281441 | 2.149008e-05 | 0.796189 | 0.796189 | 0.001663955 | 0.1363642 | 0.796189 | 0.1363642 | 0.796189 | kidney | 10 | 5.281441e-01 | 0.277247607 | 2 | 0.6770211 | 0.169397807 |
| iCluster3 | iCluster | kidney | 0.4408349 | 0.0246705 | 0.5431745 | 0.0246705 | 0.323935 | 0.4408349 | 0.4408349 | 0.8185494 | 0.2502599 | 0.5431745 | kidney | 10 | 2.467050e-02 | 1.607821997 | 2 | 0.4408349 | 0.355724004 |
| intNMF3 | intNMF | kidney | 0.2741288 | 0.8344266 | 0.04722633 | 0.6897873 | 0.04910352 | 0.2132502 | 0.7765445 | 0.0139217 | 0.6652412 | 0.001561997 | kidney | 10 | 8.344266e-01 | 0.078611846 | 4 | 0.2741288 | 0.562045306 |
| JIVE3 | JIVE | kidney | 0.4912491 | 0.002390182 | 0.05585059 | 0.8306092 | 0.05585059 | 0.05585059 | 0.05585059 | 0.9652067 | 0.6703824 | 0.8306092 | kidney | 10 | 2.390182e-03 | 2.621568969 | 1 | 0.4912491 | 0.308698234 |
| tICA3 | tICA | kidney | 0.7082194 | 0.7082194 | 0.9803135 | 0.9803135 | 0.9803135 | 0.7082194 | 0.9803135 | 9.346661e-06 | 0.008954057 | 0.7082194 | kidney | 10 | 7.082194e-01 | 0.149832201 | 2 | 0.7082194 | 0.149832201 |
| scivae4 | scivae | liver | 0.9085322 | 0.8852581 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | liver | 10 | 8.852581e-01 | 0.052930075 | 0 | 0.9085322 | 0.041659698 |
| RGCCA4 | RGCCA | liver | 0.6531042 | 0.1285983 | 0.1352407 | 0.5760708 | 0.6531042 | 0.6983761 | 0.000640696 | 0.2450111 | 0.8488238 | 0.6983761 | liver | 10 | 1.285983e-01 | 0.890764684 | 1 | 0.6531042 | 0.185017501 |
| MCIA4 | MCIA | liver | 0.8014378 | 0.007538289 | 0.3522051 | 0.6584276 | 0.305833 | 0.005877229 | 0.1414228 | 0.365308 | 0.0424988 | 0.9747084 | liver | 10 | 7.538289e-03 | 2.122727189 | 3 | 0.8014378 | 0.096130175 |
| iCluster4 | iCluster | liver | 0.06324608 | 0.9816484 | 0.9816484 | 0.9952585 | 0.9952585 | 0.9816484 | 0.9952585 | 0.06324608 | 0.6784651 | 0.257104 | liver | 10 | 9.816484e-01 | 0.008044039 | 0 | 0.06324608 | 1.198966397 |
| intNMF4 | intNMF | liver | 0.6494593 | 0.6494593 | 0.1106518 | 0.6494593 | 0.6494593 | 0.6494593 | 0.6494593 | 0.1106518 | 0.1106518 | 0.699813 | liver | 10 | 6.494593e-01 | 0.187448055 | 0 | 0.6494593 | 0.187448055 |
| JIVE4 | JIVE | liver | 0.7431439 | 0.0009248186 | 0.6889358 | 0.7464017 | 0.6889358 | 0.2570553 | 0.01280808 | 0.6889358 | 0.6889358 | 0.7464017 | liver | 10 | 9.248186e-04 | 3.033943428 | 2 | 0.7431439 | 0.128927095 |
| tICA4 | tICA | liver | 0.9424653 | 0.454957 | 0.9424653 | 0.454957 | 0.454957 | 0.454957 | 0.454957 | 0.454957 | 0.6985288 | 0.9424653 | liver | 10 | 4.549570e-01 | 0.342029628 | 0 | 0.9424653 | 0.025734638 |
| scivae5 | scivae | melanoma | 0.1304159 | 0.07654574 | 0.1137157 | 0.3374772 | 0.1304159 | 0.07654574 | 0.758011 | 0.1137157 | 0.07654574 | 0.3979006 | melanoma | 10 | 7.654574e-02 | 1.116078975 | 0 | 0.1304159 | 0.884669577 |
| RGCCA5 | RGCCA | melanoma | 0.008763755 | 0.6675814 | 8.732694e-08 | 0.742436 | 0.5064252 | 0.5064252 | 0.6675814 | 0.03494139 | 0.07538833 | 0.6675814 | melanoma | 10 | 6.675814e-01 | 0.175495756 | 3 | 0.008763755 | 2.057309790 |
| MCIA5 | MCIA | melanoma | 0.09323865 | 0.05891835 | 0.0002436218 | 0.804762 | 0.0008816326 | 0.03279005 | 0.7119304 | 0.2168784 | 0.03386561 | 0.8038506 | melanoma | 10 | 5.891835e-02 | 1.229749441 | 4 | 0.09323865 | 1.030404016 |
| iCluster5 | iCluster | melanoma | 0.0009345873 | 3.75896e-06 | 0.5990119 | 0.7279161 | 0.7279161 | 0.890351 | 0.5990119 | 0.5990119 | 0.3708371 | 0.08171892 | melanoma | 10 | 3.758960e-06 | 5.424932342 | 2 | 0.0009345873 | 3.029380130 |
| intNMF5 | intNMF | melanoma | 0.1465673 | 0.6719592 | 0.1465673 | 0.005668112 | 0.1465673 | 0.3740275 | 0.4502339 | 0.07138578 | 9.996027e-06 | 0.1736893 | melanoma | 10 | 6.719592e-01 | 0.172657124 | 2 | 0.1465673 | 0.833962855 |
| JIVE5 | JIVE | melanoma | 0.08815332 | 0.5182627 | 0.3125852 | 8.295589e-06 | 0.4521105 | 0.1132352 | 0.002069068 | 0.4005567 | 0.8126514 | 0.5420346 | melanoma | 10 | 5.182627e-01 | 0.285450062 | 2 | 0.08815332 | 1.054761324 |
| tICA5 | tICA | melanoma | 0.1481763 | 0.1250969 | 1.085854e-05 | 0.4590133 | 0.08744965 | 0.4994696 | 0.1250969 | 0.09207737 | 0.09207737 | 0.003079848 | melanoma | 10 | 1.250969e-01 | 0.902753468 | 2 | 0.1481763 | 0.829221333 |
| scivae6 | scivae | ovarian | 0.121396 | 0.02921971 | 0.1367591 | 0.02921971 | 0.05835234 | 0.02921971 | 0.02921971 | 0.7325978 | 0.3023191 | 0.1006367 | ovarian | 10 | 2.921971e-02 | 1.534324112 | 4 | 0.121396 | 0.915795632 |
| RGCCA6 | RGCCA | ovarian | 0.9687845 | 0.3328547 | 0.9687845 | 0.9687845 | 0.3328547 | 0.9687845 | 0.4094383 | 0.9687845 | 0.9687845 | 0.9687845 | ovarian | 10 | 3.328547e-01 | 0.477745367 | 0 | 0.9687845 | 0.013772812 |
| MCIA6 | MCIA | ovarian | 0.9963681 | 0.9963681 | 0.4856017 | 0.8644176 | 0.9963681 | 0.9963681 | 0.9963681 | 0.4856017 | 0.4856017 | 0.9630502 | ovarian | 10 | 9.963681e-01 | 0.001580192 | 0 | 0.9963681 | 0.001580192 |
| iCluster6 | iCluster | ovarian | 0.9684332 | 0.9684332 | 0.9684332 | 0.9684332 | 0.8815489 | 0.9684332 | 0.8815489 | 0.9684332 | 0.8815489 | 0.9684332 | ovarian | 10 | 9.684332e-01 | 0.013930335 | 0 | 0.9684332 | 0.013930335 |
| intNMF6 | intNMF | ovarian | 0.9620995 | 0.9620995 | 0.4585659 | 0.9620995 | 0.9620995 | 0.9620995 | 0.4585659 | 0.4585659 | 0.4585659 | 0.9620995 | ovarian | 10 | 9.620995e-01 | 0.016780000 | 0 | 0.9620995 | 0.016780000 |
| JIVE6 | JIVE | ovarian | 0.859074 | 0.859074 | 0.3109906 | 0.859074 | 0.4184484 | 0.3109906 | 0.859074 | 0.770928 | 0.3109906 | 0.859074 | ovarian | 10 | 8.590740e-01 | 0.065969410 | 0 | 0.859074 | 0.065969410 |
| tICA6 | tICA | ovarian | 0.9062334 | 0.9062334 | 0.9062334 | 0.4188126 | 0.9062334 | 0.9062334 | 0.9062334 | 0.9062334 | 0.9062334 | 0.4188126 | ovarian | 10 | 9.062334e-01 | 0.042759923 | 0 | 0.9062334 | 0.042759923 |
| scivae7 | scivae | sarcoma | 0.8784908 | 0.9881741 | 0.4992487 | 0.4634695 | 0.2841599 | 0.4634695 | 0.4634695 | 0.4992487 | 0.4634695 | 0.7462966 | sarcoma | 10 | 9.881741e-01 | 0.005166512 | 0 | 0.8784908 | 0.056262799 |
| RGCCA7 | RGCCA | sarcoma | 0.3003338 | 0.009348357 | 0.003162501 | 0.419127 | 0.3228952 | 0.7294782 | 0.5759544 | 0.8200843 | 0.1237538 | 0.09980813 | sarcoma | 10 | 9.348357e-03 | 2.029264694 | 2 | 0.3003338 | 0.522395733 |
| MCIA7 | MCIA | sarcoma | 0.257462 | 0.0004413724 | 0.01089201 | 0.3778846 | 0.6572043 | 0.4851062 | 0.6572043 | 0.257462 | 0.4723466 | 0.3778846 | sarcoma | 10 | 4.413724e-04 | 3.355194794 | 2 | 0.257462 | 0.589286887 |
| iCluster7 | iCluster | sarcoma | 0.3697605 | 0.1276649 | 0.01745401 | 0.8435923 | 0.3697605 | 0.1805633 | 0.007330939 | 0.007330939 | 0.1849386 | 0.3697605 | sarcoma | 10 | 1.276649e-01 | 0.893928475 | 3 | 0.3697605 | 0.432079510 |
| intNMF7 | intNMF | sarcoma | 0.1542796 | 0.123546 | 0.002268153 | 0.123546 | 0.6661077 | 0.0148154 | 0.8502676 | 0.686137 | 0.001449921 | 0.5219471 | sarcoma | 10 | 1.235460e-01 | 0.908171248 | 3 | 0.1542796 | 0.811691426 |
| JIVE7 | JIVE | sarcoma | 0.1218231 | 0.1218231 | 0.003316721 | 0.2426025 | 0.6901569 | 0.8813984 | 0.6901569 | 0.8512053 | 0.1937397 | 0.2426025 | sarcoma | 10 | 1.218231e-01 | 0.914270337 | 1 | 0.1218231 | 0.914270337 |
| tICA7 | tICA | sarcoma | 0.8819494 | 0.09647813 | 0.2658254 | 0.001811467 | 0.4997008 | 0.2658254 | 0.3081159 | 0.7623773 | 0.2658254 | 0.05267573 | sarcoma | 10 | 9.647813e-02 | 1.015571108 | 1 | 0.8819494 | 0.054556313 |
In [106]:
survival_analysis$cancer.1 <- unlist(survival_analysis$cancer.1)
In [99]:
survival_analysis
| methods | cancer | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | cancer.1 | facs | padj | log10padj | num_sig_facs | combinedvals | log10pvals | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <fct> | <fct> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <named list> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <list> | <dbl> | |
| scivae | scivae | aml | 0.03498773 | 0.4034696 | 0.851101 | 0.01864446 | 0.01864446 | 0.851101 | 0.001500313 | 0.03498773 | 0.851101 | 0.5272676 | aml | 10 | 4.034696e-01 | 0.394189196 | 5 | 0.03498773 | 1.456084214 |
| RGCCA | RGCCA | aml | 0.577179 | 0.01854128 | 0.7869179 | 0.009525792 | 0.7869179 | 0.3677111 | 0.7869179 | 0.678091 | 0.9857022 | 0.9414523 | aml | 10 | 1.854128e-02 | 1.731860249 | 2 | 0.577179 | 0.238689464 |
| MCIA | MCIA | aml | 0.09845619 | 0.01660443 | 0.2290268 | 0.4954931 | 0.4954931 | 0.01660443 | 0.4643065 | 0.9984336 | 0.2290268 | 0.8920132 | aml | 10 | 1.660443e-02 | 1.779775923 | 2 | 0.09845619 | 1.006756953 |
| iCluster | iCluster | aml | 0.9446362 | 0.07925052 | 0.6462015 | 0.01479379 | 0.4282431 | 0.3432563 | 0.8009971 | 0.4282431 | 0.5179414 | 0.5687697 | aml | 10 | 7.925052e-02 | 1.100997853 | 1 | 0.9446362 | 0.024735401 |
| intNMF | intNMF | aml | 0.5612005 | 0.5612005 | 0.5732093 | 0.04581529 | 0.1068234 | 0.08743822 | 0.04581529 | 0.1602273 | 0.04581529 | 0.9081898 | aml | 10 | 5.612005e-01 | 0.250881932 | 3 | 0.5612005 | 0.250881932 |
| JIVE | JIVE | aml | 0.1653313 | 0.9969533 | 0.002351374 | 0.4220073 | 0.8604182 | 0.7539287 | 0.1653313 | 0.8604182 | 0.3424456 | 0.8604182 | aml | 10 | 9.969533e-01 | 0.001325206 | 1 | 0.1653313 | 0.781644862 |
| tICA | tICA | aml | 0.8514052 | 0.8514052 | 0.8514052 | 0.01541895 | 0.8514052 | 0.04319786 | 0.8514052 | 0.5320929 | 0.1294422 | 0.1493582 | aml | 10 | 8.514052e-01 | 0.069863692 | 2 | 0.8514052 | 0.069863692 |
| scivae1 | scivae | breast | 0.4850375 | 0.5367704 | 0.4850375 | 0.5367704 | 0.9817527 | 0.8851041 | 0.4850375 | 0.5367704 | 0.8851041 | 0.677522 | breast | 10 | 5.367704e-01 | 0.270211439 | 0 | 0.4850375 | 0.314224699 |
| RGCCA1 | RGCCA | breast | 0.7228516 | 0.08150212 | 0.08150212 | 0.7228516 | 0.3663289 | 0.001529672 | 0.06566508 | 0.7228516 | 0.7228516 | 0.7228516 | breast | 10 | 8.150212e-02 | 1.088831105 | 1 | 0.7228516 | 0.140950868 |
| MCIA1 | MCIA | breast | 0.6446539 | 0.05390532 | 0.6446539 | 0.6117583 | 0.4955347 | 0.05390532 | 0.05629511 | 0.05880803 | 0.02298899 | 0.6117583 | breast | 10 | 5.390532e-02 | 1.268368350 | 1 | 0.6446539 | 0.190673377 |
| iCluster1 | iCluster | breast | 0.9950847 | 0.4977844 | 0.9334352 | 0.7299385 | 0.4977844 | 0.3958043 | 0.05888213 | 0.0863816 | 0.0863816 | 0.05888213 | breast | 10 | 4.977844e-01 | 0.302958733 | 0 | 0.9950847 | 0.002139965 |
| intNMF1 | intNMF | breast | 0.05860942 | 0.3086518 | 0.3221858 | 0.2857767 | 0.9150312 | 0.0009279364 | 0.2857767 | 0.3086518 | 0.3974081 | 0.3221858 | breast | 10 | 3.086518e-01 | 0.510531172 | 1 | 0.05860942 | 1.232032575 |
| JIVE1 | JIVE | breast | 0.8161499 | 0.07765908 | 0.8150449 | 0.1793233 | 0.8168842 | 0.003104433 | 0.07765908 | 0.07765908 | 0.7909869 | 0.07765908 | breast | 10 | 7.765908e-02 | 1.109807770 | 1 | 0.8161499 | 0.088230051 |
| tICA1 | tICA | breast | 0.0930841 | 0.003750981 | 0.5772502 | 0.003750981 | 0.01029604 | 0.4745634 | 0.8910863 | 0.8910863 | 0.8910863 | 0.1747179 | breast | 10 | 3.750981e-03 | 2.425855082 | 3 | 0.0930841 | 1.031124512 |
| scivae2 | scivae | colon | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.87352 | 0.8800942 | 0.87352 | 0.87352 | colon | 10 | 8.735200e-01 | 0.058727143 | 0 | 0.87352 | 0.058727143 |
| RGCCA2 | RGCCA | colon | 0.6845027 | 0.6845027 | 0.4534483 | 0.2783878 | 0.4696353 | 0.6845027 | 0.6845027 | 0.6845027 | 0.6845027 | 0.9316657 | colon | 10 | 6.845027e-01 | 0.164624850 | 0 | 0.6845027 | 0.164624850 |
| MCIA2 | MCIA | colon | 0.7268303 | 0.7268303 | 0.3866666 | 0.2567604 | 0.4170189 | 0.8204053 | 0.7268303 | 0.7268303 | 0.7268303 | 0.0183408 | colon | 10 | 7.268303e-01 | 0.138566987 | 1 | 0.7268303 | 0.138566987 |
| iCluster2 | iCluster | colon | 0.9430608 | 0.9430608 | 0.8879562 | 0.9430608 | 0.9430608 | 0.8879562 | 0.9229776 | 0.9430608 | 0.9430608 | 0.9430608 | colon | 10 | 9.430608e-01 | 0.025460286 | 0 | 0.9430608 | 0.025460286 |
| intNMF2 | intNMF | colon | 0.9728918 | 0.790393 | 0.7384476 | 0.7384476 | 0.9728918 | 0.790393 | 0.5887348 | 0.790393 | 0.9728918 | 0.790393 | colon | 10 | 7.903930e-01 | 0.102156916 | 0 | 0.9728918 | 0.011935475 |
| JIVE2 | JIVE | colon | 0.7750902 | 0.7750902 | 0.6810251 | 0.6810251 | 0.6810251 | 0.7750902 | 0.6810251 | 0.6810251 | 0.6810251 | 0.6810251 | colon | 10 | 7.750902e-01 | 0.110647748 | 0 | 0.7750902 | 0.110647748 |
| tICA2 | tICA | colon | 0.6067935 | 0.7504688 | 0.3362704 | 0.6067935 | 0.6067935 | 0.6067935 | 0.6067935 | 0.6067935 | 0.8593003 | 0.5142843 | colon | 10 | 7.504688e-01 | 0.124667362 | 0 | 0.6067935 | 0.216959071 |
| scivae3 | scivae | kidney | 0.4374853 | 0.1212966 | 0.6681088 | 0.9648711 | 0.2752951 | 0.4374853 | 0.4374853 | 0.1212966 | 0.4374853 | 0.2752951 | kidney | 10 | 1.212966e-01 | 0.916151255 | 0 | 0.4374853 | 0.359036493 |
| RGCCA3 | RGCCA | kidney | 0.5537313 | 8.713777e-05 | 0.5537313 | 0.02758201 | 0.5366432 | 0.5868637 | 0.5868637 | 0.5366432 | 0.3509294 | 0.5868637 | kidney | 10 | 8.713777e-05 | 4.059793547 | 2 | 0.5537313 | 0.256700919 |
| MCIA3 | MCIA | kidney | 0.6770211 | 0.5281441 | 2.149008e-05 | 0.796189 | 0.796189 | 0.001663955 | 0.1363642 | 0.796189 | 0.1363642 | 0.796189 | kidney | 10 | 5.281441e-01 | 0.277247607 | 2 | 0.6770211 | 0.169397807 |
| iCluster3 | iCluster | kidney | 0.4408349 | 0.0246705 | 0.5431745 | 0.0246705 | 0.323935 | 0.4408349 | 0.4408349 | 0.8185494 | 0.2502599 | 0.5431745 | kidney | 10 | 2.467050e-02 | 1.607821997 | 2 | 0.4408349 | 0.355724004 |
| intNMF3 | intNMF | kidney | 0.2741288 | 0.8344266 | 0.04722633 | 0.6897873 | 0.04910352 | 0.2132502 | 0.7765445 | 0.0139217 | 0.6652412 | 0.001561997 | kidney | 10 | 8.344266e-01 | 0.078611846 | 4 | 0.2741288 | 0.562045306 |
| JIVE3 | JIVE | kidney | 0.4912491 | 0.002390182 | 0.05585059 | 0.8306092 | 0.05585059 | 0.05585059 | 0.05585059 | 0.9652067 | 0.6703824 | 0.8306092 | kidney | 10 | 2.390182e-03 | 2.621568969 | 1 | 0.4912491 | 0.308698234 |
| tICA3 | tICA | kidney | 0.7082194 | 0.7082194 | 0.9803135 | 0.9803135 | 0.9803135 | 0.7082194 | 0.9803135 | 9.346661e-06 | 0.008954057 | 0.7082194 | kidney | 10 | 7.082194e-01 | 0.149832201 | 2 | 0.7082194 | 0.149832201 |
| scivae4 | scivae | liver | 0.9085322 | 0.8852581 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | 0.8730199 | liver | 10 | 8.852581e-01 | 0.052930075 | 0 | 0.9085322 | 0.041659698 |
| RGCCA4 | RGCCA | liver | 0.6531042 | 0.1285983 | 0.1352407 | 0.5760708 | 0.6531042 | 0.6983761 | 0.000640696 | 0.2450111 | 0.8488238 | 0.6983761 | liver | 10 | 1.285983e-01 | 0.890764684 | 1 | 0.6531042 | 0.185017501 |
| MCIA4 | MCIA | liver | 0.8014378 | 0.007538289 | 0.3522051 | 0.6584276 | 0.305833 | 0.005877229 | 0.1414228 | 0.365308 | 0.0424988 | 0.9747084 | liver | 10 | 7.538289e-03 | 2.122727189 | 3 | 0.8014378 | 0.096130175 |
| iCluster4 | iCluster | liver | 0.06324608 | 0.9816484 | 0.9816484 | 0.9952585 | 0.9952585 | 0.9816484 | 0.9952585 | 0.06324608 | 0.6784651 | 0.257104 | liver | 10 | 9.816484e-01 | 0.008044039 | 0 | 0.06324608 | 1.198966397 |
| intNMF4 | intNMF | liver | 0.6494593 | 0.6494593 | 0.1106518 | 0.6494593 | 0.6494593 | 0.6494593 | 0.6494593 | 0.1106518 | 0.1106518 | 0.699813 | liver | 10 | 6.494593e-01 | 0.187448055 | 0 | 0.6494593 | 0.187448055 |
| JIVE4 | JIVE | liver | 0.7431439 | 0.0009248186 | 0.6889358 | 0.7464017 | 0.6889358 | 0.2570553 | 0.01280808 | 0.6889358 | 0.6889358 | 0.7464017 | liver | 10 | 9.248186e-04 | 3.033943428 | 2 | 0.7431439 | 0.128927095 |
| tICA4 | tICA | liver | 0.9424653 | 0.454957 | 0.9424653 | 0.454957 | 0.454957 | 0.454957 | 0.454957 | 0.454957 | 0.6985288 | 0.9424653 | liver | 10 | 4.549570e-01 | 0.342029628 | 0 | 0.9424653 | 0.025734638 |
| scivae5 | scivae | melanoma | 0.1304159 | 0.07654574 | 0.1137157 | 0.3374772 | 0.1304159 | 0.07654574 | 0.758011 | 0.1137157 | 0.07654574 | 0.3979006 | melanoma | 10 | 7.654574e-02 | 1.116078975 | 0 | 0.1304159 | 0.884669577 |
| RGCCA5 | RGCCA | melanoma | 0.008763755 | 0.6675814 | 8.732694e-08 | 0.742436 | 0.5064252 | 0.5064252 | 0.6675814 | 0.03494139 | 0.07538833 | 0.6675814 | melanoma | 10 | 6.675814e-01 | 0.175495756 | 3 | 0.008763755 | 2.057309790 |
| MCIA5 | MCIA | melanoma | 0.09323865 | 0.05891835 | 0.0002436218 | 0.804762 | 0.0008816326 | 0.03279005 | 0.7119304 | 0.2168784 | 0.03386561 | 0.8038506 | melanoma | 10 | 5.891835e-02 | 1.229749441 | 4 | 0.09323865 | 1.030404016 |
| iCluster5 | iCluster | melanoma | 0.0009345873 | 3.75896e-06 | 0.5990119 | 0.7279161 | 0.7279161 | 0.890351 | 0.5990119 | 0.5990119 | 0.3708371 | 0.08171892 | melanoma | 10 | 3.758960e-06 | 5.424932342 | 2 | 0.0009345873 | 3.029380130 |
| intNMF5 | intNMF | melanoma | 0.1465673 | 0.6719592 | 0.1465673 | 0.005668112 | 0.1465673 | 0.3740275 | 0.4502339 | 0.07138578 | 9.996027e-06 | 0.1736893 | melanoma | 10 | 6.719592e-01 | 0.172657124 | 2 | 0.1465673 | 0.833962855 |
| JIVE5 | JIVE | melanoma | 0.08815332 | 0.5182627 | 0.3125852 | 8.295589e-06 | 0.4521105 | 0.1132352 | 0.002069068 | 0.4005567 | 0.8126514 | 0.5420346 | melanoma | 10 | 5.182627e-01 | 0.285450062 | 2 | 0.08815332 | 1.054761324 |
| tICA5 | tICA | melanoma | 0.1481763 | 0.1250969 | 1.085854e-05 | 0.4590133 | 0.08744965 | 0.4994696 | 0.1250969 | 0.09207737 | 0.09207737 | 0.003079848 | melanoma | 10 | 1.250969e-01 | 0.902753468 | 2 | 0.1481763 | 0.829221333 |
| scivae6 | scivae | ovarian | 0.121396 | 0.02921971 | 0.1367591 | 0.02921971 | 0.05835234 | 0.02921971 | 0.02921971 | 0.7325978 | 0.3023191 | 0.1006367 | ovarian | 10 | 2.921971e-02 | 1.534324112 | 4 | 0.121396 | 0.915795632 |
| RGCCA6 | RGCCA | ovarian | 0.9687845 | 0.3328547 | 0.9687845 | 0.9687845 | 0.3328547 | 0.9687845 | 0.4094383 | 0.9687845 | 0.9687845 | 0.9687845 | ovarian | 10 | 3.328547e-01 | 0.477745367 | 0 | 0.9687845 | 0.013772812 |
| MCIA6 | MCIA | ovarian | 0.9963681 | 0.9963681 | 0.4856017 | 0.8644176 | 0.9963681 | 0.9963681 | 0.9963681 | 0.4856017 | 0.4856017 | 0.9630502 | ovarian | 10 | 9.963681e-01 | 0.001580192 | 0 | 0.9963681 | 0.001580192 |
| iCluster6 | iCluster | ovarian | 0.9684332 | 0.9684332 | 0.9684332 | 0.9684332 | 0.8815489 | 0.9684332 | 0.8815489 | 0.9684332 | 0.8815489 | 0.9684332 | ovarian | 10 | 9.684332e-01 | 0.013930335 | 0 | 0.9684332 | 0.013930335 |
| intNMF6 | intNMF | ovarian | 0.9620995 | 0.9620995 | 0.4585659 | 0.9620995 | 0.9620995 | 0.9620995 | 0.4585659 | 0.4585659 | 0.4585659 | 0.9620995 | ovarian | 10 | 9.620995e-01 | 0.016780000 | 0 | 0.9620995 | 0.016780000 |
| JIVE6 | JIVE | ovarian | 0.859074 | 0.859074 | 0.3109906 | 0.859074 | 0.4184484 | 0.3109906 | 0.859074 | 0.770928 | 0.3109906 | 0.859074 | ovarian | 10 | 8.590740e-01 | 0.065969410 | 0 | 0.859074 | 0.065969410 |
| tICA6 | tICA | ovarian | 0.9062334 | 0.9062334 | 0.9062334 | 0.4188126 | 0.9062334 | 0.9062334 | 0.9062334 | 0.9062334 | 0.9062334 | 0.4188126 | ovarian | 10 | 9.062334e-01 | 0.042759923 | 0 | 0.9062334 | 0.042759923 |
| scivae7 | scivae | sarcoma | 0.8784908 | 0.9881741 | 0.4992487 | 0.4634695 | 0.2841599 | 0.4634695 | 0.4634695 | 0.4992487 | 0.4634695 | 0.7462966 | sarcoma | 10 | 9.881741e-01 | 0.005166512 | 0 | 0.8784908 | 0.056262799 |
| RGCCA7 | RGCCA | sarcoma | 0.3003338 | 0.009348357 | 0.003162501 | 0.419127 | 0.3228952 | 0.7294782 | 0.5759544 | 0.8200843 | 0.1237538 | 0.09980813 | sarcoma | 10 | 9.348357e-03 | 2.029264694 | 2 | 0.3003338 | 0.522395733 |
| MCIA7 | MCIA | sarcoma | 0.257462 | 0.0004413724 | 0.01089201 | 0.3778846 | 0.6572043 | 0.4851062 | 0.6572043 | 0.257462 | 0.4723466 | 0.3778846 | sarcoma | 10 | 4.413724e-04 | 3.355194794 | 2 | 0.257462 | 0.589286887 |
| iCluster7 | iCluster | sarcoma | 0.3697605 | 0.1276649 | 0.01745401 | 0.8435923 | 0.3697605 | 0.1805633 | 0.007330939 | 0.007330939 | 0.1849386 | 0.3697605 | sarcoma | 10 | 1.276649e-01 | 0.893928475 | 3 | 0.3697605 | 0.432079510 |
| intNMF7 | intNMF | sarcoma | 0.1542796 | 0.123546 | 0.002268153 | 0.123546 | 0.6661077 | 0.0148154 | 0.8502676 | 0.686137 | 0.001449921 | 0.5219471 | sarcoma | 10 | 1.235460e-01 | 0.908171248 | 3 | 0.1542796 | 0.811691426 |
| JIVE7 | JIVE | sarcoma | 0.1218231 | 0.1218231 | 0.003316721 | 0.2426025 | 0.6901569 | 0.8813984 | 0.6901569 | 0.8512053 | 0.1937397 | 0.2426025 | sarcoma | 10 | 1.218231e-01 | 0.914270337 | 1 | 0.1218231 | 0.914270337 |
| tICA7 | tICA | sarcoma | 0.8819494 | 0.09647813 | 0.2658254 | 0.001811467 | 0.4997008 | 0.2658254 | 0.3081159 | 0.7623773 | 0.2658254 | 0.05267573 | sarcoma | 10 | 9.647813e-02 | 1.015571108 | 1 | 0.8819494 | 0.054556313 |
In [100]:
survival_analysis$V1 <- unlist(survival_analysis$V1)
survival_analysis$V2 <- unlist(survival_analysis$V2)
survival_analysis$V3 <- unlist(survival_analysis$V3)
survival_analysis$V4 <- unlist(survival_analysis$V4)
survival_analysis$V5 <- unlist(survival_analysis$V5)
survival_analysis$V6 <- unlist(survival_analysis$V6)
survival_analysis$V7 <- unlist(survival_analysis$V7)
survival_analysis$V8 <- unlist(survival_analysis$V8)
survival_analysis$V9 <- unlist(survival_analysis$V9)
survival_analysis$V10 <- unlist(survival_analysis$V10)
In [104]:
survival_analysis$combinedvals <- unlist(survival_analysis$combinedvals)
In [105]:
write.table(survival_analysis, paste0(results_folder, "survival.txt"),
sep="\t", row.names=FALSE)
In [91]:
#tiff("BioEnr_go_fig1.tiff", units="in", width=8.95, height=6.05, res=300)
min_nonZero = min(survival_analysis[, "log10padj"])
max_nonZero = max(survival_analysis[, "log10padj"])
g <- ggplot(survival_analysis,
aes(x=num_sig_facs, y=log10pvals)) +
geom_point(aes(colour = methods, shape = cancer), size=10, alpha=.6, position=position_jitter(h=0, w=0.25))+
theme_bw() +
scale_color_manual(values=c('#FF6E28', '#C8961E', '#FF0000', '#0000FF', '#A0A0A0', '#FF00FF', '#48D1CC', '#00FF00')) +
labs(title="Survival significance",
x="# metagenes (factors) enriched in survival") +
theme(plot.title = element_text(size=14,face="bold"),
axis.text = element_text(size=11),
axis.title = element_text(size=13),
legend.text=element_text(size=10)) +
ylab("-log10(p value)") +
scale_shape_manual(values=c(16, 17, 15, 3, 7, 8, 23, 18)) +
labs(colour = "Methods",shape="Cancer") +
guides(color = guide_legend(order = 1),shape = guide_legend(order = 2),size = guide_legend(order = 3)) +
scale_x_discrete(limits=0:5, labels = c(0:5));
g
#dev.off()
ggsave(paste0(results_folder, "survival_sig.pdf"),dpi=300)
ggsave(paste0(results_folder, "survival_sig.png"),dpi=300)
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