#' @name getNEMO

#' @title Get subtypes from NEMO

#' @description This function wraps the NEMO (Neighborhood based multi-omics clustering) algorithm and provides standard output for `getMoHeatmap()` and `getConsensusMOIC()`.

#' @param data List of matrices.

#' @param N.clust Number of clusters.

#' @param num.neighbors The number of neighbors to use for each omic.

#' @param type Data type corresponding to the list of matrics, which can be gaussian, binomial or possion.

#' @return A list with the following components:

#'

#'         \code{fit}       an object returned by \link[NEMO]{nemo.clustering}.

#'

#'         \code{clust.res} a data.frame storing sample ID and corresponding clusters.

#'

#'         \code{mo.method} a string value indicating the method used for multi-omics integrative clustering.

#' @import SNFtool

#' @export

#' @references Rappoport N, Shamir R (2019). NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics, 35(18):3348-3356.

#' @examples # There is no example and please refer to vignette.

getNEMO <- function(data          = NULL,

                    N.clust       = NULL,

                    type          = rep("gaussian", length(data)),

                    num.neighbors = NA) {

  # check data

  n_dat <- length(data)

  if(n_dat > 6){

    stop('current verision of MOVICS can support up to 6 datasets.')

  }

  if(n_dat < 2){

    stop('current verision of MOVICS needs at least 2 omics data.')

  }

  useless.argument <- type

  fit <- nemo.clustering(omics.list    = data,

                         num.clusters  = N.clust,

                         num.neighbors = num.neighbors)

  clustres <- data.frame(samID = colnames(data[[1]]),

                         clust = as.numeric(fit),

                         row.names = colnames(data[[1]]),

                         stringsAsFactors = FALSE)

  #clustres <- clustres[order(clustres$clust),]

  return(list(fit = fit, clust.res = clustres, mo.method = "NEMO"))

}

#' @title affinityMatrix

#' @name affinityMatrix

#' @param Diff Distance Matrix.

#' @param K Number of nearest neighbors.

#' @param sigma Variance for local model.

#' @author Bo Wang, Aziz Mezlini, Feyyaz Demir, Marc Fiume, Zhuowen Tu, Michael Brudno, Benjamin Haibe-Kains, Anna Goldenberg

#' @references Wang B, Mezlini AM, Demir F, et al (2014). Similarity network fusion for aggregating data types on a genomic scale. Nat Methods, 11(3):333-337.

#' @keywords internal

#' @return affinityMatrix

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)

}

#' @title dist2

#' @name dist2

#' @param X A data matrix where each row is a different data point.

#' @param C A data matrix where each row is a different data point. If this matrix is the same as X, pairwise distances for all data points are computed.

#' @author Bo Wang, Aziz Mezlini, Feyyaz Demir, Marc Fiume, Zhuowen Tu, Michael Brudno, Benjamin Haibe-Kains, Anna Goldenberg

#' @references Wang B, Mezlini AM, Demir F, et al (2014). Similarity network fusion for aggregating data types on a genomic scale. Nat Methods, 11(3):333-337.

#' @keywords internal

#' @return dist2

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)

}

#' @title Spectral clustering

#' @name spectralClustering

#' @keywords internal

#' @author Bo Wang, Aziz Mezlini, Feyyaz Demir, Marc Fiume, Zhuowen Tu, Michael Brudno, Benjamin Haibe-Kains, Anna Goldenberg

#' @references Wang B, Mezlini AM, Demir F, et al (2014). Similarity network fusion for aggregating data types on a genomic scale. Nat Methods, 11(3):333-337.

#' @return spectralClustering

spectralClustering = SNFtool::spectralClustering

#' @title NEMO num clusters

#' @name nemo.num.clusters

#' @description Estimates the number of clusters in an affinity graph.

#' @param W the affinity graph.

#' @param NUMC possible values for the number of clusters. Defaults to 2:15.

#' @return the estimated number of clusters in the graph.

#' @author Nimrod Rappoport

#' @references Rappoport N, Shamir R (2019). NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics, 35(18):3348-3356.

#' @keywords internal

nemo.num.clusters <- function(W, NUMC=2:15) {

  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)

    degs[degs == 0] = .Machine$double.eps

    D = diag(degs)

    L = D - W

    Di = diag(1/sqrt(degs))

    L = Di %*% L %*% Di

    print(dim(L))

    eigs = eigen(L)

    eigs_order = sort(eigs$values, index.return = TRUE)$ix

    eigs$values = eigs$values[eigs_order]

    eigs$vectors = eigs$vectors[, eigs_order]

    eigengap = abs(diff(eigs$values))

    eigengap = (1:length(eigengap)) * eigengap

    t1 <- sort(eigengap[NUMC], decreasing = TRUE, index.return = TRUE)$ix

    return(NUMC[t1[1]])

  }

}

#' @title NEMO affinity graph

#' @name nemo.affinity.graph

#' @description Constructs a single affinity graph measuring similarity across different omics.

#' @param raw.data A list of the data to be clustered, where each an entry is a matrix of features x samples.

#' @param k The number of neighbors to use for each omic. It can either be a number, a list of numbers

#' or NA. If it is a number, this is the number of neighbors used for all omics. If this is a list,

#' the number of neighbors are taken for each omic from that list. If it is NA, each omic chooses the

#' number of neighbors to be the number of samples divided by NUM.NEIGHBORS.RATIO.

#' @return A single matrix measuring similarity between the samples across all omics.

#' @author Nimrod Rappoport

#' @references Rappoport N, Shamir R (2019). NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics, 35(18):3348-3356.

#' @keywords internal

nemo.affinity.graph <- function(raw.data, k = NA, NUM.NEIGHBORS.RATIO = 6) {

  if (is.na(k)) {

    k = as.numeric(lapply(1:length(raw.data), function(i) round(ncol(raw.data[[i]]) / NUM.NEIGHBORS.RATIO)))

  } else if (length(k) == 1) {

    k = rep(k, length(raw.data))

  }

  sim.data = lapply(1:length(raw.data), function(i) {affinityMatrix(dist2(as.matrix(t(raw.data[[i]])),

                                                                          as.matrix(t(raw.data[[i]]))), k[i], 0.5)})

  affinity.per.omic = lapply(1:length(raw.data), function(i) {

    sim.datum = sim.data[[i]]

    non.sym.knn = apply(sim.datum, 1, function(sim.row) {

      returned.row = sim.row

      threshold = sort(sim.row, decreasing = TRUE)[k[i]]

      returned.row[sim.row < threshold] = 0

      row.sum = sum(returned.row)

      returned.row[sim.row >= threshold] = returned.row[sim.row >= threshold] / row.sum

      return(returned.row)

    })

    sym.knn = non.sym.knn + t(non.sym.knn)

    return(sym.knn)

  })

  patient.names = Reduce(union, lapply(raw.data, colnames))

  num.patients = length(patient.names)

  returned.affinity.matrix = matrix(0, ncol = num.patients, nrow=num.patients)

  rownames(returned.affinity.matrix) = patient.names

  colnames(returned.affinity.matrix) = patient.names

  shared.omic.count = matrix(0, ncol = num.patients, nrow=num.patients)

  rownames(shared.omic.count) = patient.names

  colnames(shared.omic.count) = patient.names

  for (j in 1:length(raw.data)) {

    curr.omic.patients = colnames(raw.data[[j]])

    returned.affinity.matrix[curr.omic.patients, curr.omic.patients] = returned.affinity.matrix[curr.omic.patients, curr.omic.patients] + affinity.per.omic[[j]][curr.omic.patients, curr.omic.patients]

    shared.omic.count[curr.omic.patients, curr.omic.patients] = shared.omic.count[curr.omic.patients, curr.omic.patients] + 1

  }

  final.ret = returned.affinity.matrix / shared.omic.count

  lower.tri.ret = final.ret[lower.tri(final.ret)]

  final.ret[shared.omic.count == 0] = mean(lower.tri.ret[!is.na(lower.tri.ret)])

  return(final.ret)

}

#' @title NEMO clustering

#' @name nemo.clustering

#' @description Performs multi-omic clustering on a datset using the NEMO algorithm.

#' Uses nemo.num.clusters to estimate the number of clusters.

#' @param omics.list A list of the data to be clustered, where each an entry is a matrix of features x samples.

#' @param k The number of neighbors to use for each omic. It can either be a number, a list of numbers

#' or NA. If it is a number, this is the number of neighbors used for all omics. If this is a list,

#' the number of neighbors are taken for each omic from that list. If it is NA, each omic chooses the

#' number of neighbors to be the number of samples divided by NUM.NEIGHBORS.RATIO.

#' @return A single matrix measuring similarity between the samples across all omics.

#' @author Nimrod Rappoport

#' @references Rappoport N, Shamir R (2019). NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics, 35(18):3348-3356.

#' @keywords internal

nemo.clustering <- function(omics.list, num.clusters = NULL, num.neighbors = NA) {

  if (is.null(num.clusters)) {

    num.clusters = NA

  }

  graph = nemo.affinity.graph(omics.list, k = num.neighbors)

  if (is.na(num.clusters)) {

    num.clusters = nemo.num.clusters(graph)

  }

  clustering = spectralClustering(graph, num.clusters)

  names(clustering) = colnames(graph)

  return(clustering)

}