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impacc_variants.R
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library(cluster)
pv_anova_rank = function(X,y){
X=as.matrix(X)
n=length(y)
n_y=levels(y)
R = t(apply(X, 1, rank))
TIES = apply(X, 1, table)
TIEE = sapply(TIES, function(x) sum(x^3-x))
STATISTIC = rowsums(sapply(n_y, function(w) rowsums(R[,y==w])^2/sum(y==w)))
STATISTICS <- ((12 * STATISTIC / (n * (n + 1)) - 3 * (n + 1)) /
(1 - TIEE/ (n^3 - n)))
PARAMETER <- nlevels(y) - 1L
PVAL <- sapply(STATISTICS, function(s) pchisq(s, PARAMETER, lower.tail = FALSE))
return(PVAL)
}
pv_multinom = function(X,y){
multinom_score = function(x,y){
fit=multinom(y ~ x)
pred = predict(fit, x, "probs")
if (nlevels(y)==2){
pred = cbind(1-pred,pred)
predd = pred[cbind(1:nrow(pred), y)]
}else{
predd = pred[cbind(1:nrow(pred), y)]
}
return(mean(predd))
}
return(sapply(c(1:nrow(X)), function(i) multinom_score(as.matrix(X)[i,],y)))
}
IMPACC_rankANOVA = function(d=NULL,
K=NULL,
adaptiveFeature = TRUE,
reps=100, ## number of iterations
pItem=0.25, ## minipatch size for observations
pFeature=0.1, ## minipatch size for features
innerLinkage="ward.D", ## internal HC linkage
distance="manhattan",## internal HC distance
h=0.95, ## cut internal tree at h quantile
E= 3, ## number of epochs in burn-in stage
qI=0.95, ## observations with greater than (qI) quantile of weights are classified to the set of high uncertain observations in adaptive sampling
qF=1, ## features with weights greater than (mean+qF*std) are classified to the set of high importance genes in adaptive sampling
alpha_I=0.5, ## learning rate for updating observation weights
alpha_F=0.5, ## learning rate for updating feature weights
pp=0.05, ## feature support threshold, a feature will be add to feature support if its p-value in ANOVA is smaller than pp
finalAlgorithm='hclust',
finalLinkage='ward.D',
early_stop=TRUE, ## whether perform early stop criteria
num_unchange = 5,
eps = 0.00001,
seed=NULL,
verbose=TRUE,
corUse="everything"){
###########
## The IMPACC function perform IMPACC (Interpretable MiniPatch Adaptive Consensus Clustering) with adaptive sampling scheme on both observations and features.
## It returns (1)consensus: final N by N consensus matrix; (2):feature_importance: feature importance scores
############
# adaptiveFeature = TRUE
# reps=100 ## number of iterations
# pItem=0.25 ## minipatch size for observations
# pFeature=0.1 ## minipatch size for features
# innerLinkage="ward.D" ## internal HC linkage
# distance="manhattan" ## internal HC distance
# h=0.95 ## cut internal tree at h quantile
# E= 3 ## number of epochs in burn-in stage
# qI=0.95 ## observations with greater than (qI) quantile of weights are classified to the set of high uncertain observations in adaptive sampling
# qF=1 ## features with weights greater than (mean+qF*std) are classified to the set of high importance genes in adaptive sampling
# alpha_I=0.5 ## learning rate for updating observation weights
# alpha_F=0.5 ## learning rate for updating feature weights
# pp=0.05 ## feature support threshold, a feature will be add to feature support if its p-value in ANOVA is smaller than pp
# finalAlgorithm='hclust'
# finalLinkage='ward.D'
# early_stop=TRUE ## whether perform early stop criteria
# seed=NULL
# verbose=TRUE
# corUse="everything"
######################
## check validity of data input
check(d)
d=data.frame(scalematrix(as.matrix(d)))
###############
if(is.null(seed)==TRUE){
seed=timeSeed = as.numeric(Sys.time())
}
set.seed(seed)
update_IMPACC = function(){
if(verbose){
message(paste(" i =",i))
}
### update observation weights
confusion = rowMeans(CoAsso*(1-CoAsso))
ww = (i+n_burnin)/subsample_o*confusion
wi <<- alpha_I*wi+(1-alpha_I)*(ww/sum(ww))
if (adaptiveFeature==TRUE){
wi_p<<- alpha_F*wi_p+(1-alpha_F)*feature_score
}else{
wi_p<<-NULL
}
## subsample
sample_x = sample_MiniPatch(d, pItem, pFeature,pi_item=pi_item[i],pi_feature=pi_feature[i],
weightsItem = wi, weightsFeature = wi_p,qI=qI,qF=qF)
subsample_o<<- subsample_o+colnames(d)%in%colnames(d)[sample_x$subcols]
subsample_f<<- subsample_f+rownames(d)%in%rownames(d)[sample_x$subrows]
############################
## clustering
########################
this_assignment = cluster_algo(submat = sample_x$submat,h = h,distance=distance,innerLinkage=innerLinkage)
#######################################
######### for feature importance
#######################################
if (adaptiveFeature==TRUE){
pvalu_rank = pv_anova_rank(as.matrix(sample_x$submat),as.factor(this_assignment))
pv_rank = quantile(na.omit(pvalu_rank),pp)
pvalue = pvalu_rank<=pv_rank
feature_support[rownames(sample_x$submat)[which(pvalue)]]<<- 1+ feature_support[rownames(sample_x$submat)[which(pvalue)]]
feature_score<<- feature_support/subsample_f
}else{
feature_support = feature_score <<- NULL
}
#######################################
##########################################
##mCount stores number of times a sample pair was sampled together.
mCount<<- connectivityMatrix(rep(1,length(sample_x$subcols)),
mCount,
sample_x$subcols)
##ml stores number of times a sample pair was sampled together.
ml<<- connectivityMatrix(this_assignment,
ml,
sample_x$subcols)
this_coA = ml / mCount
this_coA[mCount==0]=0
CoAsso <<- this_coA
}
## initialize parameters
n=ncol(d)
if (adaptiveFeature==TRUE){
pi_item= pi_feature <- seq(0.5,1,by=0.5/reps)
}else{
pi_item <- seq(0.5,1,by=0.5/reps)
pi_feature <- rep(1,reps)
}
############################
## calculate # of iterations needed for burn-in stage
############################
if (100%%(pItem*100)==0){
p_sample = rep(pItem,1/pItem)
}else{
p_sample = c(rep(pItem,floor(1/pItem)),1%%pItem)
}
if (100%%(pFeature*100)==0){
p_sample_row = rep(pFeature,1/pFeature)
}else{
p_sample_row = c(rep(pFeature,floor(1/pFeature)),1%%pFeature)
}
lcm = max(length(p_sample_row),length(p_sample))
num_partition = E*lcm ## number of partition needed for burn-in stage
conf_record = array(0,dim = c(reps+num_partition,n))
######################################
## BURN IN STAGE
##############################################
message('Burn-in stage')
sample_Burn = sampleBurin(d,num_partition,p_sample,p_sample_row) ## 'sampleBurin' is in funmini.R
n_burnin=length(sample_Burn)
subsample_o=rep(0,ncol(d))
subsample_f=rep(0,nrow(d))
if (adaptiveFeature==TRUE){
feature_support = rep(0,nrow(d)) ## feature support
names(feature_support)= rownames(d)
}
CoAsso=mCount=ml=matrix(c(0),ncol=n,nrow=n)
for (i in 1:length(sample_Burn)){
if(verbose){
message(paste(" i =",i))
}
sample_x = sample_Burn[[i]]
this_assignment = cluster_algo(submat = sample_x$submat,h = h,distance=distance,innerLinkage=innerLinkage)
## update records on minipatch sampling
subsample_o = subsample_o+colnames(d)%in%colnames(d)[sample_x$subcols]
subsample_f = subsample_f+rownames(d)%in%rownames(d)[sample_x$subrows]
if (adaptiveFeature==TRUE){
##########################################
######## for feature importance
###########################################
pvalu_rank = pv_anova_rank(as.matrix(sample_x$submat),as.factor(this_assignment))
pv_rank = quantile(na.omit(pvalu_rank),pp)
pvalue = pvalu_rank<=pv_rank
feature_support[rownames(sample_x$submat)[which(pvalue)]]=1+ feature_support[rownames(sample_x$submat)[which(pvalue)]]
feature_score=feature_support/(subsample_f)
feature_score[subsample_f==0]=0
}else{
feature_support = feature_score<-NULL
}
###################################
mCount <- connectivityMatrix(rep(1,length(sample_x$subcols)),
mCount,
sample_x$subcols)
ml <- connectivityMatrix(this_assignment,
ml,
sample_x$subcols)
CoAsso = ml / mCount
CoAsso[mCount==0]=0
}
wi=rep(1/n,n)
wi_p=rep(1/nrow(d),nrow(d))
# records = list('CoAsso'=CoAsso, 'mCount'=mCount,'ml'=ml,
# 'wi'=wi,'wi_p'=wi_p,'subsample_o'=subsample_o,'subsample_f'=subsample_f,
# 'feature_support'=feature_support,'feature_score'=feature_score)
############################################
## adaptive stage
#####################################################
##message("adaptive stage")
if (early_stop == F){
message('No early stopping')
for (i in seq(1,reps)){
update_IMPACC()
}
}else{
conf_q=NULL
continue=T
i=1
while (continue==T & i<reps){
update_IMPACC()
conf_q=c(conf_q,quantile(rowMeans(CoAsso*(1-CoAsso)),0.9)) ## take 90% quantile of the confusion
if (i>num_unchange){
## find change of quantile
cm = sapply(c(i:(i-(num_unchange-1))), function(x) abs(conf_q[x]-conf_q[x-1]))
if (max(cm)<eps){
continue=F
message(paste0('Stop at iteration ',i+num_partition))
}else{
continue=T
}
}
i=i+1
}
}
#heatmap(CoAsso)
labels = IMPACC_cluster(CoAsso,K=K,finalAlgorithm=finalAlgorithm,finalLinkage=finalLinkage)
return(list(ConsensusMatrix = CoAsso,
labels=as.character(labels),
feature_importance=feature_score,
nIter = i))
}
#############################
## multinomial regression
############################
IMPACC_multinomial = function(d=NULL,
K=NULL,
adaptiveFeature = TRUE,
reps=100, ## number of iterations
pItem=0.25, ## minipatch size for observations
pFeature=0.1, ## minipatch size for features
innerLinkage="ward.D", ## internal HC linkage
distance="manhattan",## internal HC distance
h=0.95, ## cut internal tree at h quantile
E= 3, ## number of epochs in burn-in stage
qI=0.95, ## observations with greater than (qI) quantile of weights are classified to the set of high uncertain observations in adaptive sampling
qF=1, ## features with weights greater than (mean+qF*std) are classified to the set of high importance genes in adaptive sampling
alpha_I=0.5, ## learning rate for updating observation weights
alpha_F=0.5, ## learning rate for updating feature weights
pp=0.05, ## feature support threshold, a feature will be add to feature support if its p-value in ANOVA is smaller than pp
finalAlgorithm='hclust',
finalLinkage='ward.D',
early_stop=TRUE, ## whether perform early stop criteria
num_unchange = 5,
eps = 0.00001,
seed=NULL,
verbose=TRUE,
corUse="everything"){
###########
## The IMPACC function perform IMPACC (Interpretable MiniPatch Adaptive Consensus Clustering) with adaptive sampling scheme on both observations and features.
## It returns (1)consensus: final N by N consensus matrix; (2):feature_importance: feature importance scores
######################
## check validity of data input
check(d)
d=data.frame(scalematrix(as.matrix(d)))
###############
if(is.null(seed)==TRUE){
seed=timeSeed = as.numeric(Sys.time())
}
set.seed(seed)
update_IMPACC = function(){
if(verbose){
message(paste(" i =",i))
}
### update observation weights
confusion = rowMeans(CoAsso*(1-CoAsso))
ww = (i+n_burnin)/subsample_o*confusion
wi <<- alpha_I*wi+(1-alpha_I)*(ww/sum(ww))
if (adaptiveFeature==TRUE){
wi_p<<- alpha_F*wi_p+(1-alpha_F)*feature_score
}else{
wi_p<<-NULL
}
## subsample
sample_x = sample_MiniPatch(d, pItem, pFeature,pi_item=pi_item[i],pi_feature=pi_feature[i],
weightsItem = wi, weightsFeature = wi_p,qI=qI,qF=qF)
subsample_o<<- subsample_o+colnames(d)%in%colnames(d)[sample_x$subcols]
subsample_f<<- subsample_f+rownames(d)%in%rownames(d)[sample_x$subrows]
############################
## clustering
########################
this_assignment = cluster_algo(submat = sample_x$submat,h = h,distance=distance,innerLinkage=innerLinkage)
#######################################
######### for feature importance
#######################################
if (adaptiveFeature==TRUE){
prob_all = pv_multinom(as.matrix(sample_x$submat),as.factor(this_assignment))
cutoff = quantile(na.omit(prob_all),1-pp)
pvalue = prob_all>=cutoff
feature_support[rownames(sample_x$submat)[which(pvalue)]]<<- 1+ feature_support[rownames(sample_x$submat)[which(pvalue)]]
feature_score<<- feature_support/subsample_f
}else{
feature_support = feature_score <<- NULL
}
#######################################
##########################################
##mCount stores number of times a sample pair was sampled together.
mCount<<- connectivityMatrix(rep(1,length(sample_x$subcols)),
mCount,
sample_x$subcols)
##ml stores number of times a sample pair was sampled together.
ml<<- connectivityMatrix(this_assignment,
ml,
sample_x$subcols)
this_coA = ml / mCount
this_coA[mCount==0]=0
CoAsso <<- this_coA
}
## initialize parameters
n=ncol(d)
if (adaptiveFeature==TRUE){
pi_item= pi_feature <- seq(0.5,1,by=0.5/reps)
}else{
pi_item <- seq(0.5,1,by=0.5/reps)
pi_feature <- rep(1,reps)
}
############################
## calculate # of iterations needed for burn-in stage
############################
if (100%%(pItem*100)==0){
p_sample = rep(pItem,1/pItem)
}else{
p_sample = c(rep(pItem,floor(1/pItem)),1%%pItem)
}
if (100%%(pFeature*100)==0){
p_sample_row = rep(pFeature,1/pFeature)
}else{
p_sample_row = c(rep(pFeature,floor(1/pFeature)),1%%pFeature)
}
lcm = max(length(p_sample_row),length(p_sample))
num_partition = E*lcm ## number of partition needed for burn-in stage
conf_record = array(0,dim = c(reps+num_partition,n))
######################################
## BURN IN STAGE
##############################################
message('Burn-in stage')
sample_Burn = sampleBurin(d,num_partition,p_sample,p_sample_row) ## 'sampleBurin' is in funmini.R
n_burnin=length(sample_Burn)
subsample_o=rep(0,ncol(d))
subsample_f=rep(0,nrow(d))
if (adaptiveFeature==TRUE){
feature_support = rep(0,nrow(d)) ## feature support
names(feature_support)= rownames(d)
}
CoAsso=mCount=ml=matrix(c(0),ncol=n,nrow=n)
for (i in 1:length(sample_Burn)){
if(verbose){
message(paste(" i =",i))
}
sample_x = sample_Burn[[i]]
this_assignment = cluster_algo(submat = sample_x$submat,h = h,distance=distance,innerLinkage=innerLinkage)
## update records on minipatch sampling
subsample_o = subsample_o+colnames(d)%in%colnames(d)[sample_x$subcols]
subsample_f = subsample_f+rownames(d)%in%rownames(d)[sample_x$subrows]
if (adaptiveFeature==TRUE){
##########################################
######## for feature importance
###########################################
prob_all = pv_multinom(as.matrix(sample_x$submat),as.factor(this_assignment))
cutoff = quantile(na.omit(prob_all),1-pp)
pvalue = prob_all>=cutoff
feature_support[rownames(sample_x$submat)[which(pvalue)]]=1+ feature_support[rownames(sample_x$submat)[which(pvalue)]]
feature_score=feature_support/(subsample_f)
feature_score[subsample_f==0]=0
}else{
feature_support = feature_score<-NULL
}
###################################
mCount <- connectivityMatrix(rep(1,length(sample_x$subcols)),
mCount,
sample_x$subcols)
ml <- connectivityMatrix(this_assignment,
ml,
sample_x$subcols)
CoAsso = ml / mCount
CoAsso[mCount==0]=0
}
wi=rep(1/n,n)
wi_p=rep(1/nrow(d),nrow(d))
# records = list('CoAsso'=CoAsso, 'mCount'=mCount,'ml'=ml,
# 'wi'=wi,'wi_p'=wi_p,'subsample_o'=subsample_o,'subsample_f'=subsample_f,
# 'feature_support'=feature_support,'feature_score'=feature_score)
############################################
## adaptive stage
#####################################################
##message("adaptive stage")
if (early_stop == F){
message('No early stopping')
for (i in seq(1,reps)){
update_IMPACC()
}
}else{
conf_q=NULL
continue=T
i=1
while (continue==T & i<reps){
update_IMPACC()
conf_q=c(conf_q,quantile(rowMeans(CoAsso*(1-CoAsso)),0.9)) ## take 90% quantile of the confusion
if (i>num_unchange){
## find change of quantile
cm = sapply(c(i:(i-(num_unchange-1))), function(x) abs(conf_q[x]-conf_q[x-1]))
if (max(cm)<eps){
continue=F
message(paste0('Stop at iteration ',i+num_partition))
}else{
continue=T
}
}
i=i+1
}
}
#heatmap(CoAsso)
labels = IMPACC_cluster(CoAsso,K=K,finalAlgorithm=finalAlgorithm,finalLinkage=finalLinkage)
return(list(ConsensusMatrix = CoAsso,
labels=as.character(labels),
feature_importance=feature_score,
nIter = i))
}
## cut tree by siloutte score
IMPACC_silh = function(d=NULL,
K=NULL,
adaptiveFeature = TRUE,
reps=100, ## number of iterations
pItem=0.25, ## minipatch size for observations
pFeature=0.1, ## minipatch size for features
innerLinkage="ward.D", ## internal HC linkage
distance="manhattan",## internal HC distance
h=0.95, ## cut internal tree at h quantile
E= 3, ## number of epochs in burn-in stage
qI=0.95, ## observations with greater than (qI) quantile of weights are classified to the set of high uncertain observations in adaptive sampling
qF=1, ## features with weights greater than (mean+qF*std) are classified to the set of high importance genes in adaptive sampling
alpha_I=0.5, ## learning rate for updating observation weights
alpha_F=0.5, ## learning rate for updating feature weights
pp=0.05, ## feature support threshold, a feature will be add to feature support if its p-value in ANOVA is smaller than pp
finalAlgorithm='hclust',
finalLinkage='ward.D',
early_stop=TRUE, ## whether perform early stop criteria
num_unchange = 5,
eps = 0.00001,
seed=NULL,
verbose=TRUE,
corUse="everything"){
######################
## check validity of data input
check(d)
d=data.frame(scalematrix(as.matrix(d)))
###############
if(is.null(seed)==TRUE){
seed=timeSeed = as.numeric(Sys.time())
}
set.seed(seed)
update_IMPACC = function(){
if(verbose){
message(paste(" i =",i))
}
### update observation weights
confusion = rowMeans(CoAsso*(1-CoAsso))
ww = (i+n_burnin)/subsample_o*confusion
wi <<- alpha_I*wi+(1-alpha_I)*(ww/sum(ww))
if (adaptiveFeature==TRUE){
wi_p<<- alpha_F*wi_p+(1-alpha_F)*feature_score
}else{
wi_p<<-NULL
}
## subsample
sample_x = sample_MiniPatch(d, pItem, pFeature,pi_item=pi_item[i],pi_feature=pi_feature[i],
weightsItem = wi, weightsFeature = wi_p,qI=qI,qF=qF)
subsample_o<<- subsample_o+colnames(d)%in%colnames(d)[sample_x$subcols]
subsample_f<<- subsample_f+rownames(d)%in%rownames(d)[sample_x$subrows]
############################
## clustering
########################
this_assignment = cluster_algo_silh(submat = sample_x$submat,h = h,distance=distance,innerLinkage=innerLinkage)
#######################################
######### for feature importance
#######################################
if (adaptiveFeature==TRUE){
pvalu = pv_anova(as.matrix(sample_x$submat),as.factor(this_assignment))
pv = quantile(na.omit(pvalu),pp)
pvalue = pvalu<=pv
feature_support[rownames(sample_x$submat)[which(pvalue)]]<<- 1+ feature_support[rownames(sample_x$submat)[which(pvalue)]]
feature_score<<- feature_support/subsample_f
}else{
feature_support = feature_score <<- NULL
}
#######################################
##########################################
##mCount stores number of times a sample pair was sampled together.
mCount<<- connectivityMatrix(rep(1,length(sample_x$subcols)),
mCount,
sample_x$subcols)
##ml stores number of times a sample pair was sampled together.
ml<<- connectivityMatrix(this_assignment,
ml,
sample_x$subcols)
this_coA = ml / mCount
this_coA[mCount==0]=0
CoAsso <<- this_coA
}
## initialize parameters
n=ncol(d)
if (adaptiveFeature==TRUE){
pi_item= pi_feature <- seq(0.5,1,by=0.5/reps)
}else{
pi_item <- seq(0.5,1,by=0.5/reps)
pi_feature <- rep(1,reps)
}
############################
## calculate # of iterations needed for burn-in stage
############################
if (100%%(pItem*100)==0){
p_sample = rep(pItem,1/pItem)
}else{
p_sample = c(rep(pItem,floor(1/pItem)),1%%pItem)
}
if (100%%(pFeature*100)==0){
p_sample_row = rep(pFeature,1/pFeature)
}else{
p_sample_row = c(rep(pFeature,floor(1/pFeature)),1%%pFeature)
}
lcm = max(length(p_sample_row),length(p_sample))
num_partition = E*lcm ## number of partition needed for burn-in stage
conf_record = array(0,dim = c(reps+num_partition,n))
######################################
## BURN IN STAGE
##############################################
message('Burn-in stage')
sample_Burn = sampleBurin(d,num_partition,p_sample,p_sample_row) ## 'sampleBurin' is in funmini.R
n_burnin=length(sample_Burn)
subsample_o=rep(0,ncol(d))
subsample_f=rep(0,nrow(d))
if (adaptiveFeature==TRUE){
feature_support = rep(0,nrow(d)) ## feature support
names(feature_support)= rownames(d)
}
CoAsso=mCount=ml=matrix(c(0),ncol=n,nrow=n)
for (i in 1:length(sample_Burn)){
if(verbose){
message(paste(" i =",i))
}
sample_x = sample_Burn[[i]]
this_assignment = cluster_algo_silh(submat = sample_x$submat,h = h,distance=distance,innerLinkage=innerLinkage)
## update records on minipatch sampling
subsample_o = subsample_o+colnames(d)%in%colnames(d)[sample_x$subcols]
subsample_f = subsample_f+rownames(d)%in%rownames(d)[sample_x$subrows]
if (adaptiveFeature==TRUE){
##########################################
######## for feature importance
###########################################
pvalu = pv_anova(as.matrix(sample_x$submat),as.factor(this_assignment))
pv = quantile(na.omit(pvalu),pp)
pvalue = pvalu<=pv
feature_support[rownames(sample_x$submat)[which(pvalue)]]=1+ feature_support[rownames(sample_x$submat)[which(pvalue)]]
feature_score=feature_support/(subsample_f)
feature_score[subsample_f==0]=0
}else{
feature_support = feature_score<-NULL
}
###################################
mCount <- connectivityMatrix(rep(1,length(sample_x$subcols)),
mCount,
sample_x$subcols)
ml <- connectivityMatrix(this_assignment,
ml,
sample_x$subcols)
CoAsso = ml / mCount
CoAsso[mCount==0]=0
}
wi=rep(1/n,n)
wi_p=rep(1/nrow(d),nrow(d))
# records = list('CoAsso'=CoAsso, 'mCount'=mCount,'ml'=ml,
# 'wi'=wi,'wi_p'=wi_p,'subsample_o'=subsample_o,'subsample_f'=subsample_f,
# 'feature_support'=feature_support,'feature_score'=feature_score)
############################################
## adaptive stage
#####################################################
##message("adaptive stage")
if (early_stop == F){
message('No early stopping')
for (i in seq(1,reps)){
update_IMPACC()
}
}else{
conf_q=NULL
continue=T
i=1
while (continue==T & i<reps){
update_IMPACC()
conf_q=c(conf_q,quantile(rowMeans(CoAsso*(1-CoAsso)),0.9)) ## take 90% quantile of the confusion
if (i>num_unchange){
## find change of quantile
cm = sapply(c(i:(i-(num_unchange-1))), function(x) abs(conf_q[x]-conf_q[x-1]))
if (max(cm)<eps){
continue=F
message(paste0('Stop at iteration ',i+num_partition))
}else{
continue=T
}
}
i=i+1
}
}
#heatmap(CoAsso)
labels = IMPACC_cluster(CoAsso,K=K,finalAlgorithm=finalAlgorithm,finalLinkage=finalLinkage)
return(list(ConsensusMatrix = CoAsso,
labels=as.character(labels),
feature_importance=feature_score,
nIter = i))
}
### SPECIFY CLUSTERING MODELS
cluster_algo_silh = function(submat,h,distance,innerLinkage){
acceptable.distance <- c( "euclidean", "maximum", "manhattan", "canberra", "binary","minkowski",
"pearson", "spearman" )
## check distance function
if ( inherits( distance, "character" ) ) {
if ( ! distance %in% acceptable.distance & ( class(try(get(distance),silent=T))!="function") ){
stop("unsupported distance.")}
if(distance=="pearson" | distance=="spearman"){
dis <- as.dist( 1-cor(submat,method=distance,use=corUse ))
}else if( class(try(get(distance),silent=T))=="function"){
dis <- get(distance)(t(submat))
}else{
dis = dist(t(submat),method = distance)
}
}else{stop("unsupported distance specified.")}
this_cluster = hclust(dis, method=innerLinkage)
silh=rep(0,9)
for (k in c(2:10)){
ct = cutree(this_cluster,k)
ss <- silhouette(ct,dis)
silh[k-1] = mean(ss[,3])
}
kk =c(2:10)[which.max(silh)]
this_assignment = cutree(this_cluster,kk)
return(this_assignment=this_assignment)
}
cluster_algo_oracle = function(submat,h,distance,innerLinkage,K){
acceptable.distance <- c( "euclidean", "maximum", "manhattan", "canberra", "binary","minkowski",
"pearson", "spearman" )
## check distance function
if ( inherits( distance, "character" ) ) {
if ( ! distance %in% acceptable.distance & ( class(try(get(distance),silent=T))!="function") ){
stop("unsupported distance.")}
if(distance=="pearson" | distance=="spearman"){
dis <- as.dist( 1-cor(submat,method=distance,use=corUse ))
}else if( class(try(get(distance),silent=T))=="function"){
dis <- get(distance)(t(submat))
}else{
dis = dist(t(submat),method = distance)
}
}else{stop("unsupported distance specified.")}
this_cluster = hclust(dis, method=innerLinkage)
this_assignment = cutree(this_cluster,K)
return(this_assignment=this_assignment)
}
IMPACC_oracle = function(d=NULL,
K=NULL,
adaptiveFeature = TRUE,
reps=100, ## number of iterations
pItem=0.25, ## minipatch size for observations
pFeature=0.1, ## minipatch size for features
innerLinkage="ward.D", ## internal HC linkage
distance="manhattan",## internal HC distance
h=0.95, ## cut internal tree at h quantile
E= 3, ## number of epochs in burn-in stage
qI=0.95, ## observations with greater than (qI) quantile of weights are classified to the set of high uncertain observations in adaptive sampling
qF=1, ## features with weights greater than (mean+qF*std) are classified to the set of high importance genes in adaptive sampling
alpha_I=0.5, ## learning rate for updating observation weights
alpha_F=0.5, ## learning rate for updating feature weights
pp=0.05, ## feature support threshold, a feature will be add to feature support if its p-value in ANOVA is smaller than pp
finalAlgorithm='hclust',
finalLinkage='ward.D',
early_stop=TRUE, ## whether perform early stop criteria
num_unchange = 5,
eps = 0.00001,
seed=NULL,
verbose=TRUE,
corUse="everything"){
######################
## check validity of data input
check(d)
d=data.frame(scalematrix(as.matrix(d)))
###############
if(is.null(seed)==TRUE){
seed=timeSeed = as.numeric(Sys.time())
}
set.seed(seed)
update_IMPACC = function(){
if(verbose){
message(paste(" i =",i))
}
### update observation weights
confusion = rowMeans(CoAsso*(1-CoAsso))
ww = (i+n_burnin)/subsample_o*confusion
wi <<- alpha_I*wi+(1-alpha_I)*(ww/sum(ww))
if (adaptiveFeature==TRUE){
wi_p<<- alpha_F*wi_p+(1-alpha_F)*feature_score
}else{
wi_p<<-NULL
}
## subsample
sample_x = sample_MiniPatch(d, pItem, pFeature,pi_item=pi_item[i],pi_feature=pi_feature[i],
weightsItem = wi, weightsFeature = wi_p,qI=qI,qF=qF)
subsample_o<<- subsample_o+colnames(d)%in%colnames(d)[sample_x$subcols]
subsample_f<<- subsample_f+rownames(d)%in%rownames(d)[sample_x$subrows]
############################
## clustering
########################
this_assignment = cluster_algo_oracle(submat = sample_x$submat,h = h,distance=distance,innerLinkage=innerLinkage,K=K)
#######################################
######### for feature importance
#######################################
if (adaptiveFeature==TRUE){
pvalu = pv_anova(as.matrix(sample_x$submat),as.factor(this_assignment))
pv = quantile(na.omit(pvalu),pp)
pvalue = pvalu<=pv
feature_support[rownames(sample_x$submat)[which(pvalue)]]<<- 1+ feature_support[rownames(sample_x$submat)[which(pvalue)]]
feature_score<<- feature_support/subsample_f
}else{
feature_support = feature_score <<- NULL
}
#######################################
##########################################
##mCount stores number of times a sample pair was sampled together.
mCount<<- connectivityMatrix(rep(1,length(sample_x$subcols)),
mCount,
sample_x$subcols)
##ml stores number of times a sample pair was sampled together.
ml<<- connectivityMatrix(this_assignment,
ml,
sample_x$subcols)
this_coA = ml / mCount
this_coA[mCount==0]=0
CoAsso <<- this_coA
}
## initialize parameters
n=ncol(d)
if (adaptiveFeature==TRUE){
pi_item= pi_feature <- seq(0.5,1,by=0.5/reps)
}else{
pi_item <- seq(0.5,1,by=0.5/reps)
pi_feature <- rep(1,reps)
}
############################
## calculate # of iterations needed for burn-in stage
############################
if (100%%(pItem*100)==0){
p_sample = rep(pItem,1/pItem)
}else{
p_sample = c(rep(pItem,floor(1/pItem)),1%%pItem)
}
if (100%%(pFeature*100)==0){
p_sample_row = rep(pFeature,1/pFeature)
}else{
p_sample_row = c(rep(pFeature,floor(1/pFeature)),1%%pFeature)
}
lcm = max(length(p_sample_row),length(p_sample))
num_partition = E*lcm ## number of partition needed for burn-in stage
conf_record = array(0,dim = c(reps+num_partition,n))
######################################
## BURN IN STAGE
##############################################
message('Burn-in stage')
sample_Burn = sampleBurin(d,num_partition,p_sample,p_sample_row) ## 'sampleBurin' is in funmini.R
n_burnin=length(sample_Burn)
subsample_o=rep(0,ncol(d))
subsample_f=rep(0,nrow(d))
if (adaptiveFeature==TRUE){
feature_support = rep(0,nrow(d)) ## feature support
names(feature_support)= rownames(d)
}
CoAsso=mCount=ml=matrix(c(0),ncol=n,nrow=n)
for (i in 1:length(sample_Burn)){
if(verbose){
message(paste(" i =",i))
}
sample_x = sample_Burn[[i]]
this_assignment = cluster_algo_oracle(submat = sample_x$submat,h = h,distance=distance,innerLinkage=innerLinkage,K=K)
## update records on minipatch sampling
subsample_o = subsample_o+colnames(d)%in%colnames(d)[sample_x$subcols]
subsample_f = subsample_f+rownames(d)%in%rownames(d)[sample_x$subrows]
if (adaptiveFeature==TRUE){
##########################################
######## for feature importance
###########################################
pvalu = pv_anova(as.matrix(sample_x$submat),as.factor(this_assignment))
pv = quantile(na.omit(pvalu),pp)
pvalue = pvalu<=pv
feature_support[rownames(sample_x$submat)[which(pvalue)]]=1+ feature_support[rownames(sample_x$submat)[which(pvalue)]]
feature_score=feature_support/(subsample_f)
feature_score[subsample_f==0]=0
}else{
feature_support = feature_score<-NULL
}
###################################
mCount <- connectivityMatrix(rep(1,length(sample_x$subcols)),
mCount,
sample_x$subcols)
ml <- connectivityMatrix(this_assignment,
ml,
sample_x$subcols)
CoAsso = ml / mCount
CoAsso[mCount==0]=0
}
wi=rep(1/n,n)
wi_p=rep(1/nrow(d),nrow(d))
# records = list('CoAsso'=CoAsso, 'mCount'=mCount,'ml'=ml,
# 'wi'=wi,'wi_p'=wi_p,'subsample_o'=subsample_o,'subsample_f'=subsample_f,
# 'feature_support'=feature_support,'feature_score'=feature_score)
############################################
## adaptive stage
#####################################################
##message("adaptive stage")
if (early_stop == F){
message('No early stopping')
for (i in seq(1,reps)){
update_IMPACC()
}
}else{
conf_q=NULL
continue=T
i=1
while (continue==T & i<reps){
update_IMPACC()
conf_q=c(conf_q,quantile(rowMeans(CoAsso*(1-CoAsso)),0.9)) ## take 90% quantile of the confusion
if (i>num_unchange){
## find change of quantile
cm = sapply(c(i:(i-(num_unchange-1))), function(x) abs(conf_q[x]-conf_q[x-1]))
if (max(cm)<eps){
continue=F
message(paste0('Stop at iteration ',i+num_partition))
}else{
continue=T
}
}
i=i+1
}
}
#heatmap(CoAsso)
labels = IMPACC_cluster(CoAsso,K=K,finalAlgorithm=finalAlgorithm,finalLinkage=finalLinkage)
return(list(ConsensusMatrix = CoAsso,
labels=as.character(labels),
feature_importance=feature_score,
nIter = i))
}