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ChIP_GSM_functions.R
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#******************************************************************
# ChIP-GSM functions
# Xi Chen,
# CBIL, ECE Department, Virginia Tech
# 09/29/2015
#
#*******************************************************************
loading_multi_TF_read_counts<-function(TF_path, hg19_RefSeq){
Num_TF=length(TF_path$TF_name)
Num_gene=length(hg19_RefSeq$gene_name)
segment_num=41 #postive and negative 10k of TSS, 500 bps per window
Y_Region_ID=matrix(nrow=Num_gene*segment_num, ncol=1)
Y_Segment_ID=matrix(nrow=Num_gene*segment_num, ncol=1)
Y_Input_tags=matrix(0, nrow=Num_gene*segment_num, ncol=Num_TF)
Y_Sample_tags=matrix(0, nrow=Num_gene*segment_num, ncol=Num_TF)
Normalize_T=5e6
Y_upper=250
for (j in 1:Num_TF){
print(sprintf('%s', TF_path$TF_name[j]))
Region_Sample_tags_full=matrix(0, nrow=length(hg19_RefSeq$gene_name), ncol=segment_num)
Region_Input_tags_full=matrix(0, nrow=length(hg19_RefSeq$gene_name), ncol=segment_num)
TF_sample_count<-as.matrix(read.table(file=sprintf("%s",TF_path$sample_file[j]), row.names=1, header = TRUE))
Gene_sample_list<-rownames(TF_sample_count)
index<-match(Gene_sample_list, hg19_RefSeq$gene_name)
for (i in 1:length(index)){
Region_Sample_tags_full[index[i],]=as.numeric(TF_sample_count[i,])
}
TF_input_count<-as.matrix(read.table(file=sprintf("%s",TF_path$input_file[j]), row.names=1, header = TRUE))
Gene_input_list<-rownames(TF_input_count)
index<-match(Gene_input_list, hg19_RefSeq$gene_name)
for (i in 1:length(index)){
Region_Input_tags_full[index[i],]=as.numeric(TF_input_count[i,])
}
for (k in 1:Num_gene){
for (s in 1:segment_num){
Y_Region_ID[(k-1)*segment_num+s,1]=hg19_RefSeq$gene_name[k]
Y_Segment_ID[(k-1)*segment_num+s,1]=s-21
Y_Input_tags[(k-1)*segment_num+s,j]=round(Region_Input_tags_full[k,s])
Y_Sample_tags[(k-1)*segment_num+s,j]=round(Region_Sample_tags_full[k,s])
}
}
Y_Sample_tags[which(Y_Sample_tags<0),j]=0
Y_Input_tags[which(Y_Input_tags<0),j]=0
Y_Sample_tags[,j]=floor(Normalize_T/(sum(Y_Sample_tags[,j]))*Y_Sample_tags[,j])
Y_Input_tags[,j]=floor(Normalize_T/(sum(Y_Input_tags[,j]))*Y_Input_tags[,j])
}
Y_Input_tags[which(Y_Input_tags>=Y_upper)]=Y_upper
Y_Sample_tags[which(Y_Sample_tags>=Y_upper)]=Y_upper
FD=(Y_Sample_tags+1)/(Y_Input_tags+1)
multi_TFs=list("Y_Region_ID"=Y_Region_ID,
"Y_Segment_ID"=Y_Segment_ID,
"Y_Sample_tags"=Y_Sample_tags,
"Y_Input_tags"=Y_Input_tags,
"FD"=FD)
return(multi_TFs)
}
#******************************************************************
#*******************************************************************
Candidate_cluster_searching<-function(multi_TFs, Num_gene, segment_num, Num_TF){
Sampling_window_flag=matrix(1, nrow=Num_gene*segment_num, ncol=Num_TF)
Sampling_window_flag[which(multi_TFs$FD<2, arr.ind=T)]=0
Sampling_window_flag[which(multi_TFs$Y_Sample_tags<50, arr.ind=T)]=0
Sampling_window_flag=Sampling_window_flag[which(rowSums(Sampling_window_flag)<=5),]
Sampling_window_flag=Sampling_window_flag[which(rowSums(Sampling_window_flag)>=2),]
Cluster_pattern<-unique(Sampling_window_flag)
Candidate_cluster_num=matrix(0, nrow=nrow(Cluster_pattern), ncol=1)
#index=which(row.match(data.frame(Sampling_window_flag), data.frame(Cluster_pattern[1,]))>0)
#index=which(apply(Sampling_window_flag, 1, identical, Cluster_pattern[1,]))
index=which(rowSums(abs(sweep(Sampling_window_flag, 2, Cluster_pattern[1,])))==0)
if (sum(index)>0){
Candidate_cluster_num[1,1]=length(index)
}
for (k in 2:nrow(Cluster_pattern)){
#index=which(row.match(data.frame(Sampling_window_flag), data.frame(Cluster_pattern[k,]))>0)
#index=which(apply(Sampling_window_flag, 1, identical, Cluster_pattern[k,]))
index=which(rowSums(abs(sweep(Sampling_window_flag, 2, Cluster_pattern[k,])))==0)
if (sum(index)>0){
Candidate_cluster_num[k,1]=length(index)
Sampling_window_flag[index,]=0
Sampling_window_flag=Sampling_window_flag[which(rowSums(Sampling_window_flag)>0),]
}
}
cluster_index=which(Candidate_cluster_num>50)
TF_index=which(colSums(Cluster_pattern[cluster_index,])>0)
Candidate_TFs=TF_path$TF_name[TF_index]
C_candidate_matrix=Cluster_pattern[cluster_index,TF_index]
C_candidate_matrix=rbind(C_candidate_matrix, 0)
Total_clusters=nrow(C_candidate_matrix)
Sampling_window_flag=matrix(1, nrow=Num_gene*segment_num, ncol=length(TF_index))
Sampling_window_ID=multi_TFs$Y_Region_ID
Sampling_window_index=multi_TFs$Y_Segment_ID
Sampling_window_Sample_tags=multi_TFs$Y_Sample_tags[,TF_index]
Sampling_window_Input_tags=multi_TFs$Y_Input_tags[,TF_index]
Sampling_window_FD=multi_TFs$FD[,TF_index]
Sampling_window_flag[which(Sampling_window_FD<2)]=0
Sampling_window_flag[which(Sampling_window_Sample_tags<10)]=0
TF_count_summary=colSums(Sampling_window_Sample_tags)
Cluster_mapping=matrix(0, nrow=Num_gene*segment_num, ncol=Total_clusters)
for (k in 1:nrow(Sampling_window_flag)){
Cluster_mapping[k,Total_clusters]=1
if (sum(Sampling_window_flag[k,])>=2){
for (s in 1:Total_clusters-1){
if (sum(abs(C_candidate_matrix[s,]-Sampling_window_flag[k,]*C_candidate_matrix[s,]))==0){
Cluster_mapping[k,s]=1
}
}
}
}
Sampling_input_data_frame=list('Sampling_window_ID'=Sampling_window_ID,
'Sampling_window_index'=Sampling_window_index,
'Sampling_window_Sample_tags'=Sampling_window_Sample_tags,
'Sampling_window_Input_tags'=Sampling_window_Input_tags,
'Sampling_window_FD'=Sampling_window_FD,
'Sampling_window_flag'=Sampling_window_flag,
'Cluster_mapping'=Cluster_mapping,
'C_candidate_matrix'=C_candidate_matrix,
'Candidate_TFs'=Candidate_TFs,
'TF_count_summary'=TF_count_summary)
return(Sampling_input_data_frame)
}
#*******************************************************************
#*******************************************************************
sampling_X_cluster_distance<-function(Sampling_input_data_frame, Binding_pattern, TX, Xmin, Nsigma, Xupper, Pdfx_PowerLaw, Binding_prior, Num_windows, Num_TFs){
X=matrix(0, nrow=Num_windows, ncol=Num_TFs)
Gaussion_pdf=matrix(0, nrow=2*Xupper+1, ncol=1)
for (k in -Xupper:Xupper){
Gaussion_pdf[k+Xupper+1,1]=dnorm(k, mean=0, sd=sqrt(Nsigma))
}
Gaussion_pdf=Gaussion_pdf/sum(Gaussion_pdf)
for (t in 1:Num_TFs){
PX=matrix(0, nrow=Num_windows, ncol=1)
# assign Xmin tags to each binding region
binding_index= which(Binding_pattern[,t]>0)
Number_bindings=length(binding_index)
X[binding_index, t]=Xmin
# determine the weight for each binding region
for (k in 1:Number_bindings){
mean1=Sampling_input_data_frame$Sampling_window_Sample_tags[binding_index[k], t]
Y_min=max(c(floor(mean1-3*sqrt(Nsigma)), Xmin))
Y_max=min(c(floor(mean1+3*sqrt(Nsigma)), Xupper))
if (Y_min<Y_max){
Pdfx_Gaussion=Gaussion_pdf[c((Xupper+Y_min-mean1):(Xupper+Y_max-mean1))]
Pdfx_Gaussion=Pdfx_Gaussion/sum(Pdfx_Gaussion)
Pdfx=Pdfx_Gaussion*Pdfx_PowerLaw[t,c((Y_min-Xmin+1):(Y_max-Xmin+1))]
Pdfx=Pdfx/sum(Pdfx)
X_temp = sample(c(Y_min:Y_max), 1, prob=Pdfx)
}else{
X_temp=Y_min#Y_min=Ymax, only one choice
}
PX[binding_index[k], 1] = Pdfx_PowerLaw[t,X_temp-Xmin+1]*dnorm(X_temp, mean=mean1, sd=sqrt(Nsigma))*Binding_prior[binding_index[k],t]
}
# assign tags to each region according to the weight defined in PX
PX_tag=PX[binding_index,1]
PX_tag=PX_tag/sum(PX_tag)
Tag_location= sample(c(1:Number_bindings), TX[t]-Xmin*Number_bindings, replace = TRUE, prob=PX_tag)
X_add<-hist(Tag_location, breaks=c(0.5:(Number_bindings+0.5)), plot = FALSE)
# X_add=floor(X_add/100)
X[binding_index, t]=X[binding_index, t]+X_add$counts
}
X[which(X>Xupper)]=Xupper
return(X)
}
#*******************************************************************
#*******************************************************************
sampling_I_cluster_distance<-function(Sampling_input_data_frame, Binding_pattern, TI, Nsigma, Xupper, Pdfi_Gamma, Background_prior, Num_windows, Num_TFs){
I=matrix(0, nrow=Num_windows, ncol=Num_TFs)
Gaussion_pdf=matrix(0, nrow=2*Xupper+1, ncol=1)
for (k in -Xupper:Xupper){
Gaussion_pdf[k+Xupper+1,1]=dnorm(k, mean=0, sd=sqrt(Nsigma))
}
Gaussion_pdf=Gaussion_pdf/sum(Gaussion_pdf)
for (t in 1:Num_TFs){
PI=matrix(0, nrow=Num_windows, ncol=1)
# assign Xmin tags to each binding region
None_binding_index = which(Binding_pattern[,t]==0)
Num_none_binding = length(None_binding_index)
# determine the weight for each binding region
for (k in 1:Num_none_binding){
mean1=Sampling_input_data_frame$Sampling_window_Sample_tags[None_binding_index[k], t]
Y_min=max(c(floor(mean1-3*sqrt(Nsigma)), 1))
Y_max=min(c(floor(mean1+3*sqrt(Nsigma)), Xupper))
if (Y_min < Y_max){
Pdfi_Gaussion=Gaussion_pdf[c((Xupper+Y_min-mean1):(Xupper+Y_max-mean1))]
Pdfi_Gaussion=Pdfi_Gaussion/sum(Pdfi_Gaussion)
Pdfi=Pdfi_Gaussion*Pdfi_Gamma[t, c(Y_min:Y_max)]
Pdfi=Pdfi/sum(Pdfi)
I_temp = sample(c(Y_min:Y_max),1,prob=Pdfi)
}else{
I_temp=Y_min
}
PI[None_binding_index[k]] = Pdfi_Gamma[t, I_temp]*dnorm(I_temp, mean=mean1, sd=sqrt(Nsigma))*Background_prior[None_binding_index[k], t]
}
# assign tags to each region according to the weight defined in PI
PI_tag=PI[None_binding_index, 1]
PI_tag=PI_tag/sum(PI_tag)
Tag_location= sample(c(1:Num_none_binding), TI[t], replace=TRUE, prob=PI_tag)
I_add<-hist(Tag_location, breaks=c(0.5:(Num_none_binding+0.5)), plot = FALSE)
I[None_binding_index, t]=I[None_binding_index, t]+I_add$counts
}
I[which(I>Xupper)]=Xupper
return(I)
}
#*******************************************************************
#*******************************************************************
sampling_Nsigma_cluster<-function(Sampling_input_data_frame, X, I, alpha_N, beta_N, Num_windows, Num_TFs){
alpha_new=alpha_N+1/2
beta_new=beta_N+sum((Sampling_input_data_frame$Sampling_window_Sample_tags-X-I)^2)/(2*Num_windows*Num_TFs)
Nsigma = 1/rgamma(1, shape=alpha_new, scale=1/beta_new)
#Nsigma = beta_new/rchisq(1, 2*alpha_new)
return(Nsigma)
}
#********************************************************************
#********************************************************************
sampling_binding_cluster_distance<-function(Sampling_input_data_frame, Binding_pattern_old, X, Xmin, Xupper, I, Nsigma,
Pdfx_PowerLaw, Pdfi_Gamma, Binding_prior, Background_prior, Num_windows, Num_TFs, Num_clusters){
Binding_pattern=matrix(0, nrow=Num_windows, ncol=Num_TFs)
Cluster_pattern=matrix(0, nrow=Num_windows, ncol=Num_clusters)
Gaussion_pdf=matrix(0, nrow=2*Xupper+1, ncol=1)
for (k in -Xupper:Xupper){
Gaussion_pdf[k+Xupper+1,1]=dnorm(k, mean=0, sd=sqrt(Nsigma))
}
Gaussion_pdf=Gaussion_pdf/sum(Gaussion_pdf)
for (k in 1:Num_windows){
candiate_cluster_index=which(Sampling_input_data_frame$Cluster_mapping[k,]>0)
if (length(candiate_cluster_index)==1){#only have zero binding, then, no sampling is needed
#current region has no binding with sample/input > 2
Binding_pattern[k,]=0
Cluster_pattern[k,Num_clusters]=1
}
else{
PX_new=matrix(0, nrow=1, ncol=Num_TFs)
PI_new=matrix(0, nrow=1, ncol=Num_TFs)
all_possible_binding_vector=colSums(Sampling_input_data_frame$C_candidate_matrix[candiate_cluster_index,])
all_possible_binding_vector[which(all_possible_binding_vector>0)]=1
for (t in 1:Num_TFs){
if (Binding_pattern_old[k,t]==0 && all_possible_binding_vector[t]==1){
# sample X
mean_x=Sampling_input_data_frame$Sampling_window_Sample_tags[k,t]
Y_min=max(c(floor(mean_x-3*sqrt(Nsigma)), Xmin))
Y_max=min(c(floor(mean_x+3*sqrt(Nsigma)), Xupper))
if (Y_min<Y_max){
Pdfx_Gaussion= Gaussion_pdf[c((Xupper+Y_min-mean_x):(Xupper+Y_max-mean_x))]
Pdfx_Gaussion=Pdfx_Gaussion/sum(Pdfx_Gaussion)
Pdfx=Pdfx_Gaussion*Pdfx_PowerLaw[t,c((Y_min-Xmin+1):(Y_max-Xmin+1))]
Pdfx=Pdfx/sum(Pdfx)
X_temp = sample(c(Y_min:Y_max),1,prob=Pdfx)
}else{
X_temp=Y_min
}
PX = Pdfx_PowerLaw[t, X_temp-Xmin+1]*dnorm(X_temp, mean=mean_x, sd=sqrt(Nsigma))*Binding_prior[k,t]
if (I[k,t]==0){
PI = Pdfi_Gamma[t, I[k,t]+1]*dnorm(I[k,t], mean=mean_x, sd=sqrt(Nsigma))*Background_prior[k,t]
}else{
PI = Pdfi_Gamma[t, I[k,t]]*dnorm(I[k,t], mean=mean_x, sd=sqrt(Nsigma))*Background_prior[k,t]
}
PX_new[1,t] = PX/(PI+PX)
PX_new[1,t] = max(c(min(c(0.98, PX_new[1,t])), 0.02))
PI_new[1,t] = 1-PX_new[1,t]
}else if(Binding_pattern_old[k,t]==1 && all_possible_binding_vector[t]==0){
#sample I
mean_i=Sampling_input_data_frame$Sampling_window_Sample_tags[k,t]
Y_min = max(c(floor(mean_i-3*sqrt(Nsigma)), 1))
Y_max = min(c(floor(mean_i+3*sqrt(Nsigma)), Xupper))
if (Y_min<Y_max){
Pdfi_Gaussion = Gaussion_pdf[c((Xupper+Y_min-mean_i):(Xupper+Y_max-mean_i))]
Pdfi_Gaussion = Pdfi_Gaussion/sum(Pdfi_Gaussion)
Pdfi = Pdfi_Gaussion*Pdfi_Gamma[t, c(Y_min:Y_max)]
Pdfi = Pdfi/(sum(Pdfi))
I_temp = sample(c(Y_min:Y_max),1,prob=Pdfi)
}else{
I_temp=Y_min
}
PI = Pdfi_Gamma[t, I_temp]*dnorm(I_temp, mean=mean_i, sd=sqrt(Nsigma))*Background_prior[k,t]
PX = Pdfx_PowerLaw[t, X[k,t]-Xmin+1]*dnorm(X[k,t], mean=mean_i, sd=sqrt(Nsigma))*Binding_prior[k,t]
PX_new[1,t] = PX/(PI+PX)
PX_new[1,t] = max(c(min(c(0.98, PX_new[1,t])), 0.02))
PI_new[1,t] = 1-PX_new[1,t]
}else {
#all_possible_binding_vector(1,t)==0
PX_new[1,t]=0.5
PI_new[1,t]=0.5
}
}
P_candidate_cluster=matrix(1,nrow=1, ncol=length(candiate_cluster_index))
for (c in 1:length(candiate_cluster_index)){
for (t in 1:Num_TFs){
P_candidate_cluster[1,c]=P_candidate_cluster[1,c]*(PX_new[1,t]^Sampling_input_data_frame$C_candidate_matrix[candiate_cluster_index[c],t])*(PI_new[1,t]^(1-Sampling_input_data_frame$C_candidate_matrix[candiate_cluster_index[c],t]))
}
}
P_candidate_cluster=P_candidate_cluster/sum(P_candidate_cluster)
cluster_sample_index = sample(length(candiate_cluster_index),1,prob=P_candidate_cluster)
Binding_pattern[k,]=Sampling_input_data_frame$C_candidate_matrix[candiate_cluster_index[cluster_sample_index],]
Cluster_pattern[k,candiate_cluster_index[cluster_sample_index]]=1
}
}
return(list('Binding_pattern'=Binding_pattern, 'Cluster_pattern'=Cluster_pattern))
}
#********************************************************
#********************************************************
sampling_TX_TI_cluster<-function(Sampling_input_data_frame, Fold_change_factor, alpha_I, beta_I, Binding_pattern, Num_TFs){
TX=matrix(0, nrow=Num_TFs, ncol=1)
TI=matrix(0, nrow=Num_TFs, ncol=1)
for (t in 1:Num_TFs){
binding_index=which(Binding_pattern[,t]>0)
Background_sampling_weight = rgamma(Num_windows, shape=alpha_I[t], scale=beta_I[t])
Forground_sampling_weight = Background_sampling_weight
Forground_sampling_weight[binding_index]=Fold_change_factor*Forground_sampling_weight[binding_index]
Forground_sampling_weight=Forground_sampling_weight/(sum(Forground_sampling_weight))
TX[t]=floor(sum(Forground_sampling_weight[binding_index])*Sampling_input_data_frame$TF_count_summary[t]);
TI[t]=Sampling_input_data_frame$TF_count_summary[t]-TX[t];
}
return(list('TX'=TX, 'TI'=TI))
}
#**********************************************************
#*********************************************************
sampling_lambda_distance<-function(Sampling_input_data_frame, Binding_pattern_old, alpha_lambda, beta_lambda,
Num_windows, Num_TFs, window_size, promoter_size){
Binding_prior=matrix(0, nrow=Num_windows, ncol=Num_TFs)
alpha_new=matrix(0, nrow=Num_TFs, ncol=1)
beta_new=matrix(0, nrow=Num_TFs, ncol=1)
Normalization_C=matrix(0, nrow=Num_TFs, ncol=1)
lambda_new=matrix(0, nrow=Num_TFs, ncol=1)
weight=matrix(0, nrow=Num_TFs, ncol=41)
for (t in 1:Num_TFs){
alpha_new[t]=alpha_lambda+sum(Binding_pattern_old[,t])
beta_new[t]=beta_lambda+sum(((abs(Sampling_input_data_frame$Sampling_window_index)+0.5)*Binding_pattern_old[,t]))
#lambda_new[t]=rgamma(1, shape = alpha_new[t], scale=1/beta_new[t])
#lambda_new[t]=beta_new[t]/rchisq(1, 2*alpha_new[t])
lambda_new[t]=1/(sum(((abs(Sampling_input_data_frame$Sampling_window_index)+0.5)*Binding_pattern_old[,t]))/sum(Binding_pattern_old[,t]))
Normalization_C[t]=sum(lambda_new[t]*exp(-lambda_new[t]*c(0.5:20.5)))
Binding_prior[,t]=(promoter_size/window_size)/Normalization_C[t]*lambda_new[t]*exp(-lambda_new[t]*(abs(Sampling_input_data_frame$Sampling_window_index)+0.5))
#for (w in 1:41){
# weight[t, w]=sum(Sampling_input_data_frame$Sampling_window_Sample_tags[which(Sampling_input_data_frame$Sampling_window_index==(w-21)), t])
#}
#weight[t,]=41*weight[t,]/sum(weight[t,])
#for (w in 1:41){
# index=which(Sampling_input_data_frame$Sampling_window_index==(w-21))
# Binding_prior[index,t]=weight[t,w]
#}
}
return(Binding_prior)
}
#*********************************************************
#*********************************************************
sampling_lambda_distance_constant<-function(Sampling_input_data_frame, Binding_pattern_old, alpha_lambda, beta_lambda,
Num_windows, Num_TFs, window_size, promoter_size){
Binding_prior=matrix(0, nrow=Num_windows, ncol=Num_TFs)
#alpha_new=matrix(0, nrow=Num_TFs, ncol=1)
#beta_new=matrix(0, nrow=Num_TFs, ncol=1)
#Normalization_C=matrix(0, nrow=Num_TFs, ncol=1)
#lambda_new=matrix(0, nrow=Num_TFs, ncol=1)
weight=matrix(0, nrow=Num_TFs, ncol=41)
for (t in 1:Num_TFs){
#alpha_new[t]=alpha_lambda+sum(Binding_pattern_old[,t])
#beta_new[t]=beta_lambda+sum(((abs(Sampling_input_data_frame$Sampling_window_index)+0.5)*Binding_pattern_old[,t]))
#lambda_new[t]=rgamma(1, shape = alpha_new[t], scale=1/beta_new[t])
#lambda_new[t]=1/(sum(((abs(Sampling_input_data_frame$Sampling_window_index)+0.5)*Binding_pattern_old[,t]))/sum(Binding_pattern_old[,t]))
#Normalization_C[t]=sum(lambda_new[t]*exp(-lambda_new[t]*c(0.5:20.5)))
#Binding_prior[,t]=(promoter_size/window_size)/Normalization_C[t]*lambda_new[t]*exp(-lambda_new[t]*(abs(Sampling_input_data_frame$Sampling_window_index)+0.5))
for (w in 1:41){
weight[t, w]=sum(Sampling_input_data_frame$Sampling_window_Sample_tags[which(Sampling_input_data_frame$Sampling_window_index==(w-21)), t])
}
weight[t,]=41*weight[t,]/sum(weight[t,])
for (w in 1:41){
index=which(Sampling_input_data_frame$Sampling_window_index==(w-21))
Binding_prior[index,t]=weight[t,w]
}
}
Binding_prior=matrix(1, nrow=Num_windows, ncol=Num_TFs)
return(Binding_prior)
}
#***********************************************************