Initial commit

.gitignore

+# Mac stuff
+.DS_Store*
+
+# Byte-compiled
+__pycache__/
+*.py[cod]
+
+# Jupyter Notebook
+.ipynb_checkpoints
+

SNFtools/SNF.R

+SNF <- function(Wall,K=20,t=20) {
+	
+	###This function is the main function of our software. The inputs are as follows:
+    # Wall : List of affinity matrices
+    # K : number of neighbors
+    # t : number of iterations for fusion
+    
+    ###The output is a unified similarity graph. It contains both complementary information and common structures from all individual network. 
+    ###You can do various applications on this graph, such as clustering(subtyping), classification, prediction.
+    
+    LW = length(Wall)
+    normalize <- function(X) X / rowSums(X)
+    # makes elements other than largest K zero
+    
+    
+    newW <- vector("list", LW)
+    nextW <- vector("list", LW)
+    ###First, normalize different networks to avoid scale problems.
+    for( i in 1: LW){
+      Wall[[i]] = normalize(Wall[[i]]);
+      Wall[[i]] = (Wall[[i]]+t(Wall[[i]]))/2;
+    }
+    
+    ### Calculate the local transition matrix S
+    for( i in 1: LW){
+      newW[[i]] = (.dominateset(Wall[[i]],K))
+    }
+    
+    # perform the diffusion for t iterations
+    for (i in 1:t) {
+      for(j in 1:LW){
+        sumWJ = matrix(0,dim(Wall[[j]])[1], dim(Wall[[j]])[2])
+        for(k in 1:LW){
+          if(k != j) {
+            sumWJ = sumWJ + Wall[[k]]
+          }
+        }
+          nextW[[j]] = newW[[j]] %*% (sumWJ/(LW-1)) %*% t(newW[[j]]);
+      }
+      ###Normalize each new obtained networks.
+      for(j in 1 : LW){
+      	      
+                Wall[[j]] = nextW[[j]] + diag(nrow(Wall[[j]]));
+                Wall[[j]] = (Wall[[j]] + t(Wall[[j]]))/2;
+                }
+   }
+    
+    # construct the combined affinity matrix by summing diffused matrices
+    W = matrix(0,nrow(Wall[[1]]), ncol(Wall[[1]]))
+    for(i in 1:LW){
+      W = W + Wall[[i]]
+    }
+    W = W/LW;
+    W = normalize(W);
+    # ensure affinity matrix is symmetrical
+    W = (W + t(W)+diag(nrow(W))) / 2;
+    
+    return(W)  
+  }

SNFtools/affinityMatrix.R

+affinityMatrix <- function(Diff,K=20,sigma=0.5) {
+###This function constructs similarity networks.
+  N = nrow(Diff)
+  
+  Diff = (Diff + t(Diff)) / 2
+  diag(Diff) = 0;
+  sortedColumns = as.matrix(t(apply(Diff,2,sort)))
+  finiteMean <- function(x) { mean(x[is.finite(x)]) }
+  means = apply(sortedColumns[,1:K+1],1,finiteMean)+.Machine$double.eps;
+  
+  avg <- function(x,y) ((x+y)/2)
+  Sig = outer(means,means,avg)/3*2 + Diff/3 + .Machine$double.eps;
+  Sig[Sig <= .Machine$double.eps] = .Machine$double.eps
+  densities = dnorm(Diff,0,sigma*Sig,log = FALSE)
+  
+  W = (densities + t(densities)) / 2
+  return(W)
+}

SNFtools/calNMI.R

+calNMI <- function(x, y) {
+##This function calculate the NMI between two clusters.	
+	
+	x = as.vector(x);
+	y = as.vector(y);  
+    return(max(0, .mutualInformation(x, y)/sqrt(.entropy(x) * .entropy(y)), na.rm=TRUE))
+}
+
+

SNFtools/chiDist2.R

+chiDist2 <- function(A,B){
+	###This function implements the Chi-Square distance between A and B
+  n = nrow(A)
+  m = nrow(B)
+   d = ncol(A)
+   stopifnot(d == ncol(B))
+   res = matrix(nrow = n, ncol = m)
+   sqA = A^2
+   sqB = B^2
+   twoAB = 2 * (A %*% t(B))
+   for (a_num in 1:n) {
+   	for (b_num in 1:m) {
+   		res[a_num, b_num] = sum((sqA[a_num, ] + sqB[b_num, ] - twoAB[a_num, b_num]) / (A[a_num, ] + B[b_num, ]))
+   		}
+   		}
+   		res = res / 2
+   		return(res)  
+}

SNFtools/concordanceNetworkNMI.R

+
+concordanceNetworkNMI = function(Wall,C) {
+	# Given a list of affinity matrices, Wall, the number of clusters C, return a matrix containing the NMIs
+# between cluster assignments made with spectral clustering.
+
+
+#For example, if the input "wall" contains two networks, the output is a 3x3 matrix of NMIs. Note here NMI is a metric to measure the fitness of two clustering results. if NMI = 1, it indicates two clusters are identical; if NMI = 0, it indicates two #clusters are totally differently, independent. 
+# the output looks like this:
+#                                 fused network     network1        newtwork2
+#fused network                 1                       0.7                      0.55
+#network    1                   0.72                      1                         0.67
+#network 2        0.55        0.67         1
+
+#The output above means that, the fused network is more similar to network 1 than network 2.  And network 1 and network 2 has a similarity 0.67. 
+#sometimes, if the inputed networks have low similarities, which means they are contradicting each other, fusing them may not be a good idea.
+
+
+  # Calculate the fused network
+  # C is the number of clusters.
+  LW = length(Wall)
+   # Get the cluster labels for each of the networks
+  labels = lapply(Wall, function(x) spectralClustering(x, C))
+  # Calculate the NMI between each pair clusters
+  NMIs = matrix(NA, LW, LW)
+  for (i in 1:LW) {
+    for (j in 1:LW) {
+      NMIs[i, j] = calNMI(labels[[i]], labels[[j]]);
+    }
+  }
+  
+  return(NMIs)
+}

SNFtools/displayClusters.r

+displayClusters <- function(W, group) {
+  normalize <- function(X) X / rowSums(X)
+  ind = sort(as.vector(group),index.return=TRUE)
+  ind = ind$ix
+  diag(W) = 0
+  W = normalize(W);
+  W = W + t(W);
+  image(1:ncol(W),1:nrow(W),W[ind,ind],col = grey(100:0 / 100),xlab = 'Patients',ylab='Patients');
+}

SNFtools/dist2.R

+dist2 <- function(X,C) {
+  ndata = nrow(X)
+  ncentres = nrow(C)
+
+  sumsqX = rowSums(X^2)
+  sumsqC = rowSums(C^2)
+  
+  XC = 2 * (X %*% t(C))
+  
+  res = matrix(rep(sumsqX,times=ncentres),ndata,ncentres) + t(matrix(rep(sumsqC,times=ndata),ncentres,ndata)) - XC
+  res[res < 0] = 0
+  return(res)
+}

SNFtools/estimateNumberOfClustersGivenGraph.R

+estimateNumberOfClustersGivenGraph <- function(W, NUMC=2:5) {
+  
+  #   This function estimates the number of clusters given the two huristics
+  #   given in the supplementary materials of our nature method paper
+  #   W is the similarity graph
+  #   NUMC is a vector which contains the possible choices of number of
+  #   clusters.
+  #   
+  #   
+  #   K1 is the estimated best number of clusters according to eigen-gaps
+  #   K12 is the estimated SECOND best number of clusters according to eigen-gaps
+  #   
+  #   K2 is the estimated number of clusters according to rotation cost
+  #   K22 is the estimated SECOND number of clusters according to rotation cost
+  #   
+  #   an example would be [K1, K2, K12,K22] = Estimate_Number_of_Clusters_given_graph(W,
+  #                                                                                   [2:5]);
+  #   
+  #   Note that this function can only give an estimate of the number of
+  #   clusters. How to determine the "OPTIMAL" number of clusters, is still an
+  #   open question so far. 
+  
+  if (min(NUMC) == 1) {
+    warning('Note that we always assume there are more than one cluster.');
+    NUMC = NUMC[NUMC > 1]  
+  }
+  
+  W = (W + t(W))/2
+  diag(W) = 0
+  
+  if (length(NUMC) > 0) {
+    degs = rowSums(W)
+
+    
+    # compute unnormalized Laplacian
+    
+    degs[degs == 0] = .Machine$double.eps    
+    D = diag(degs)    
+    L = D - W
+    Di = diag(1 / sqrt(degs))
+    L = Di %*% L %*% Di
+    
+    # compute the eigenvectors corresponding to the k smallest
+    # eigs$valuess
+    eigs = eigen(L)
+    eigs_order = sort(eigs$values, index.return=T)$ix
+    eigs$values = eigs$values[eigs_order]
+    eigs$vectors = eigs$vectors[, eigs_order]
+    eigengap = abs(diff(eigs$values))
+    eigengap = eigengap * (1 - eigs$values[1:length(eigs$values) - 1] ) / (1 - eigs$values[2:length(eigs$values)])
+
+    quality = list()
+    for (c_index in 1:length(NUMC)) {
+      ck = NUMC[c_index]
+      UU = eigs$vectors[, 1:ck]
+      # MC edit:
+      # check for all-0 rows
+      tmpidx <- rowSums(UU==0) == ncol(UU)
+      # substitute with small values
+      UU[tmpidx,] <- .Machine$double.eps
+      # end MC edit
+      EigenvectorsDiscrete <- .discretisation(UU)[[1]]
+      EigenVectors = EigenvectorsDiscrete^2
+      
+      # MATLAB: sort(EigenVectors,2, 'descend');
+      temp1 <- EigenVectors[do.call(order, lapply(1:ncol(EigenVectors), function(i) EigenVectors[, i])), ]
+      temp1 <- t(apply(temp1, 1, sort, TRUE))  
+      
+      quality[[c_index]] = (1 - eigs$values[ck + 1]) / (1 - eigs$values[ck]) * 
+        sum( sum( diag(1 / (temp1[, 1] + .Machine$double.eps) ) %*% temp1[, 1:max(2, ck-1)] ))
+    }
+    
+    t1 <- sort(eigengap[NUMC], decreasing=TRUE, index.return=T)$ix
+    K1 = NUMC[t1[1]]
+    K12 = NUMC[t1[2]]
+    t2 <- sort(unlist(quality), index.return=TRUE)$ix
+    K2 <- NUMC[t2[1]]
+    K22 <- NUMC[t2[2]]    
+  }
+  
+  return (list(K1, K12, K2, K22))
+}

SNFtools/groupPredict.r

+groupPredict <- function(train,test,groups,K=20,alpha=0.5,t=20,method=1){
+
+###This function is used to predict the subtype of new patients.
+#train and test have the same number of view and the same number of columns
+# group is the label for the train data
+# K, alpha, t are the prameters for SNF. 
+#K is the number of neighbors
+#alpha is the hyperparameter used in constructing similarity network
+# t is the number of iterations
+#method is a indicator of which method to use to predict the label. method = 0 means to use local and global consistency; method = 1 means to use label propagation.
+Wi= vector("list", length=length(train));
+
+for (i in 1:length(train)){
+view= standardNormalization(rbind(train[[i]],test[[i]]));
+Dist1 = dist2(view, view);
+Wi[[i]] = affinityMatrix(Dist1, K, alpha);
+}
+
+W = SNF(Wi,K,t);
+Y0=matrix(0,nrow(view), max(groups));
+for (i in 1:length(groups)) Y0[i,groups[i]]=1;
+Y=.csPrediction(W,Y0,method);
+newgroups=rep(0,nrow(view));
+for (i in 1:nrow(Y)) newgroups[i]=which(Y[i,]==max(Y[i,]));
+
+return (newgroups);
+}

SNFtools/internal.R

+.csPrediction <- function(W,Y0,method){
+  ###This function implements the label propagation to predict the label(subtype) for new patients.	
+  ### note method is an indicator of which semi-supervised method to use
+  # method == 0 indicates to use the local and global consistency method
+  # method >0 indicates to use label propagation method.
+  
+  alpha=0.9;
+  P= W/rowSums(W)
+  if(method==0){
+    Y= (1-alpha)* solve( diag(dim(P)[1])- alpha*P)%*%Y0;
+  } else {
+    NLabel=which(rowSums(Y0)==0)[1]-1;
+    Y=Y0;
+    for (i in 1:1000){
+      Y=P%*%Y;
+      Y[1:NLabel,]=Y0[1:NLabel,];
+    }
+  }
+  return(Y);
+}
+
+.discretisation <- function(eigenVectors) {
+  
+  normalize <- function(x) x / sqrt(sum(x^2))
+  eigenVectors = t(apply(eigenVectors,1,normalize))
+  
+  n = nrow(eigenVectors)
+  k = ncol(eigenVectors)
+  
+  R = matrix(0,k,k)
+  R[,1] = t(eigenVectors[round(n/2),])
+  
+  mini <- function(x) {
+    i = which(x == min(x))
+    return(i[1])
+  }
+  
+  c = matrix(0,n,1)
+  for (j in 2:k) {
+    c = c + abs(eigenVectors %*% matrix(R[,j-1],k,1))
+    i = mini(c)
+    R[,j] = t(eigenVectors[i,])
+  }
+  
+  lastObjectiveValue = 0
+  for (i in 1:20) {
+    eigenDiscrete = .discretisationEigenVectorData(eigenVectors %*% R)
+    
+    svde = svd(t(eigenDiscrete) %*% eigenVectors)
+    U = svde[['u']]
+    V = svde[['v']]
+    S = svde[['d']]
+    
+    NcutValue = 2 * (n-sum(S))
+    if(abs(NcutValue - lastObjectiveValue) < .Machine$double.eps) 
+      break
+    
+    lastObjectiveValue = NcutValue
+    R = V %*% t(U)
+    
+  }
+  
+  return(list(discrete=eigenDiscrete,continuous =eigenVectors))
+}
+
+.discretisationEigenVectorData <- function(eigenVector) {
+  
+  Y = matrix(0,nrow(eigenVector),ncol(eigenVector))
+  maxi <- function(x) {
+    i = which(x == max(x))
+    return(i[1])
+  }
+  j = apply(eigenVector,1,maxi)
+  Y[cbind(1:nrow(eigenVector),j)] = 1
+  
+  return(Y)
+  
+}
+
+.dominateset <- function(xx,KK=20) {
+  ###This function outputs the top KK neighbors.	
+  
+  zero <- function(x) {
+    s = sort(x, index.return=TRUE)
+    x[s$ix[1:(length(x)-KK)]] = 0
+    return(x)
+  }
+  normalize <- function(X) X / rowSums(X)
+  A = matrix(0,nrow(xx),ncol(xx));
+  for(i in 1:nrow(xx)){
+    A[i,] = zero(xx[i,]);
+    
+  }
+  
+  
+  return(normalize(A))
+}
+
+# Calculate the mutual information between vectors x and y.
+.mutualInformation <- function(x, y) {
+  classx <- unique(x)
+  classy <- unique(y)
+  nx <- length(x)
+  ncx <- length(classx)
+  ncy <- length(classy)
+  
+  probxy <- matrix(NA, ncx, ncy)
+  for (i in 1:ncx) {
+    for (j in 1:ncy) {
+      probxy[i, j] <- sum((x == classx[i]) & (y == classy[j])) / nx
+    }
+  }
+  
+  probx <- matrix(rowSums(probxy), ncx, ncy)
+  proby <- matrix(colSums(probxy), ncx, ncy, byrow=TRUE)
+  result <- sum(probxy * log(probxy / (probx * proby), 2), na.rm=TRUE)
+  return(result)
+}
+
+# Calculate the entropy of vector x.
+.entropy <- function(x) {
+  class <- unique(x)
+  nx <- length(x)
+  nc <- length(class)
+  
+  prob <- rep.int(NA, nc)
+  for (i in 1:nc) {
+    prob[i] <- sum(x == class[i])/nx
+  }
+  
+  result <- -sum(prob * log(prob, 2))
+  return(result)
+}
+
+.repmat = function(X,m,n){
+  ##R equivalent of repmat (matlab)
+  if (is.null(dim(X))) {
+    mx = length(X)
+    nx = 1
+  } else {
+    mx = dim(X)[1]
+    nx = dim(X)[2]
+  }
+  matrix(t(matrix(X,mx,nx*n)),mx*m,nx*n,byrow=T)
+}
\ No newline at end of file

SNFtools/snf_integration_tuning.R

+## This code is written by Alessandro Zandona' <zandona@fbk.eu>.
+
+## This program is free software: you can redistribute it and/or modify
+## it under the terms of the GNU General Public License as published by
+## the Free Software Foundation, either version 3 of the License, or
+## (at your option) any later version.
+## This program is distributed in the hope that it will be useful,
+## but WITHOUT ANY WARRANTY; without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+## GNU General Public License for more details.
+## You should have received a copy of the GNU General Public License
+## along with this program.  If not, see <http://www.gnu.org/licenses/>.
+# Requires R >= 3.2.3
+snf_cv <- function(W, lab, K=5, N=10, clm, infocl){
+  median_NMI <- list()
+  nSamp <- dim(W)[1]
+  SNFNMI_all <- list()
+  set.seed(N)
+  cv_folds <- generateCVRuns(lab, ntimes = N, nfold = K, stratified = TRUE)
+  for (nfold in 1:N){
+    #cv_folds <- cvFolds(nSamp, K=5, type="random")
+    cv_folds_current <- cv_folds[[nfold]]
+    SNFNMI_K <- list()
+    for(l in 1:K){
+      # subset of X by indexes selected with k-fold CV
+      idx_k <- cv_folds_current[[l]]
+      W_k <- W[idx_k,idx_k]
+      # extract the respective labels
+      lab_k <- lab[idx_k]
+      if (clm=="spectral"){
+        if (infocl){
+          nclust <- length(unique(lab))
+          # predict clusters for subset k of X
+          group_k <- spectralClustering(W_k, nclust)
+        }else{
+          nclust <- estimateNumberOfClustersGivenGraph(W_k)[[1]]
+          group_k <- spectralClustering(W_k, nclust)
+        }
+      } else if (clm=="fastgreedy") {
+        W_sc_k <- W_k/max(W_k)
+        g_k <- graph.adjacency(W_sc_k, weighted = TRUE, diag=FALSE, mode='undirected')
+        m_k  <- cluster_fast_greedy(g_k)
+        if (infocl){
+          nclust <- length(unique(lab))
+          group_k <- cutree(as.hclust(m_k), nclust)
+        } else {
+          group_k <- m_k$membership  
+        }
+      }
+      # NMI score
+      SNFNMI_K[l] <-  calNMI(group_k, lab_k)
+    }
+    
+    # median of NMI score for current CV repetition
+    median_NMI[nfold] <- median(unlist(SNFNMI_K))
+  }
+  # median of NMI score over all CV repetitions
+  median_NMI_allrep <- median(unlist(median_NMI))
+  return(med_NMI=median_NMI_allrep)
+}
+
+snf_tuning <- function(dist1, dist2, lab, clm, infocl){
+  # Pairwise distance between samples of two different datasets
+  dist_tab1 <- dist1
+  dist_tab2 <- dist2
+
+  # min and max K values
+  minK <- 10
+  maxK <- 30
+  stepK <- 1
+  K_values <- seq(minK,maxK,stepK)
+  
+  # min and max alpha values
+  min_alpha <- 0.3
+  max_alpha <- 0.8
+  step_alpha <- 0.05
+  alpha_values <- seq(min_alpha,max_alpha,step_alpha)
+  
+  registerDoParallel(cores=2)
+  # for each combination of K and alpha, compute NMI score median over 10x5-CV
+  NMI_tun <- foreach(K=K_values) %dopar% {foreach(alpha=alpha_values) %dopar% 
+  {       W1_tun <-  affinityMatrix(dist_tab1, K=K, alpha);
+          W2_tun <-  affinityMatrix(dist_tab2, K=K, alpha); 
+          W_K <- SNF(list(W1_tun, W2_tun), K=K); 
+          med_NMI <- snf_cv(W_K, lab, clm=clm, infocl=infocl)}
+  }
+  
+  # K values
+  nK <- length(seq(minK,maxK,stepK))
+  # alpha values
+  nalpha <- length(seq(min_alpha,max_alpha,step_alpha))
+  
+  idx_max_alpha_fk <- list()
+  max_nmi_fk <- list()
+  tab_median_NMI <- matrix(,nrow=nK, ncol=nalpha)
+  # Set rownames
+  knames <- vector("character")
+  ik <- 1
+  for (i in seq(10,30,1)){
+    knames[ik] <- paste('K',i,sep='_')
+    ik <- ik+1
+  }
+  rownames(tab_median_NMI) <- knames
+  
+  # Set colnames
+  anames <- vector("character")
+  ia <- 1
+  for (i in seq(0.3,0.8,0.05)){
+    anames[ia] <- paste('alpha',i,sep='_')
+    ia <- ia+1
+  }
+  colnames(tab_median_NMI) <- anames
+  
+  for (elk in c(1:nK)){
+    
+    # K fixed, find max NMI over all alpha values
+    max_nmi_fk[elk] <- max(unlist(NMI_tun[[elk]]))
+    tab_median_NMI[elk,] <- unlist(NMI_tun[[elk]])
+    }
+  # Find K corresponding to max NMI
+  best_K_idx <- which.max(unlist(max_nmi_fk))[1]
+  best_K <- K_values[best_K_idx]
+  # Find alpha corresponding to max NMI (and previously found K)
+  best_alpha_idx <- which.max(NMI_tun[[best_K_idx]])
+  best_alpha <- alpha_values[best_alpha_idx]
+  return(list(best_K=best_K, best_alpha=best_alpha, tab_median_NMI=tab_median_NMI))
+}

SNFtools/spectralClustering.r

+spectralClustering <- function(affinity, K, type=3) {
+	
+  ###This function implements the famous spectral clustering algorithms. There are three variants. The default one is the third type. 
+  ###THe inputs are as follows:
+  
+      #affinity: the similarity matrix;
+      #K: the number of clusters
+      # type: indicators of variants of spectral clustering 
+	
+  d = rowSums(affinity)
+  d[d == 0] = .Machine$double.eps
+  D = diag(d)