1. 报错如下
image.png
2. 解决如下：对比修改函数源代码
computeNetSimilarity <- function(object, slot.name = "netP", type = c("functional","structural"), k = NULL, thresh = NULL) {
+ type <- match.arg(type)
+ prob = methods::slot(object, slot.name)$prob
+ if (is.null(k)) {
+ if (dim(prob)[3] <= 25) {
+ k <- ceiling(sqrt(dim(prob)[3]))
+ } else {
+ k <- ceiling(sqrt(dim(prob)[3])) + 1
+ }
+ }
+ if (!is.null(thresh)) {
+ prob[prob < quantile(c(prob[prob != 0]), thresh)] <- 0
+ }
+ if (type == "functional") {
+ # compute the functional similarity
+ D_signalings <- matrix(0, nrow = dim(prob)[3], ncol = dim(prob)[3])
+ S2 <- D_signalings; S3 <- D_signalings;
+ for (i in 1:(dim(prob)[3]-1)) {
+ for (j in (i+1):dim(prob)[3]) {
+ Gi <- (prob[ , ,i] > 0)*1
+ Gj <- (prob[ , ,j] > 0)*1
+ S3[i,j] <- sum(Gi * Gj)/sum(Gi+Gj-Gi*Gj,na.rm=TRUE)
+ }
+ }
+ # define the similarity matrix
+ S3[is.na(S3)] <- 0; S3 <- S3 + t(S3); diag(S3) <- 1
+ # S_signalings <- S1 *S2
+ S_signalings <- S3
+ } else if (type == "structural") {
+ # compute the structure distance
+ D_signalings <- matrix(0, nrow = dim(prob)[3], ncol = dim(prob)[3])
+ for (i in 1:(dim(prob)[3]-1)) {
+ for (j in (i+1):dim(prob)[3]) {
+ Gi <- (prob[ , ,i] > 0)*1
+ Gj <- (prob[ , ,j] > 0)*1
+ D_signalings[i,j] <- computeNetD_structure(Gi,Gj)
+ }
+ }
+ # define the structure similarity matrix
+ D_signalings[is.infinite(D_signalings)] <- 0
+ D_signalings[is.na(D_signalings)] <- 0
+ D_signalings <- D_signalings + t(D_signalings)
+ S_signalings <- 1-D_signalings
+ }
+ # smooth the similarity matrix using SNN
+ SNN <- buildSNN(S_signalings, k = k, prune.SNN = 1/15)
+ Similarity <- as.matrix(S_signalings*SNN)
+ rownames(Similarity) <- dimnames(prob)[[3]]
+ colnames(Similarity) <- dimnames(prob)[[3]]
+ comparison <- "single"
+ comparison.name <- paste(comparison, collapse = "-")
+ if (!is.list(methods::slot(object, slot.name)$similarity[[type]]$matrix)) {
+ methods::slot(object, slot.name)$similarity[[type]]$matrix <- NULL
+ }
+ methods::slot(object, slot.name)$similarity[[type]]$matrix[[comparison.name]] <- Similarity
+ return(object)
+ }
> # 报错 找到pullrequest 修改后源代码运行 也不对 逐个函数运行
> cellchat <- computeNetSimilarity(cellchat, type = "functional")
> #' @param slot.name the slot name of object that is used to compute centrality measures of signaling networks
> #' @param type "functional","structural"
> #' @param comparison a numerical vector giving the datasets for comparison
> #' @param k the number of nearest neighbors
> #' @param thresh the fraction (0 to 0.25) of interactions to be trimmed before computing network similarity
> #' @importFrom methods slot
> #'
> #' @return
> #' @export
> #'
> computeNetSimilarityPairwise <- function(object, slot.name = "netP", type = c("functional","structural"), comparison = NULL, k = NULL, thresh = NULL) {
+ type <- match.arg(type)
+ if (is.null(comparison)) {
+ comparison <- 1:length(unique(object@meta$datasets))
+ }
+ cat("Compute signaling network similarity for datasets", as.character(comparison), '\n')
+ comparison.name <- paste(comparison, collapse = "-")
+ net <- list()
+ signalingAll <- c()
+ object.net.nameAll <- c()
+ # 1:length(setdiff(names(methods::slot(object, slot.name)), "similarity"))
+ for (i in 1:length(comparison)) {
+ object.net <- methods::slot(object, slot.name)[[comparison[i]]]
+ object.net.name <- names(methods::slot(object, slot.name))[comparison[i]]
+ object.net.nameAll <- c(object.net.nameAll, object.net.name)
+ net[[i]] = object.net$prob
+ signalingAll <- c(signalingAll, paste0(dimnames(net[[i]])[[3]], "--", object.net.name))
+ # signalingAll <- c(signalingAll, dimnames(net[[i]])[[3]])
+ }
+ names(net) <- object.net.nameAll
+ net.dim <- sapply(net, dim)[3,]
+ nnet <- sum(net.dim)
+ position <- cumsum(net.dim); position <- c(0,position)
+ if (is.null(k)) {
+ if (nnet <= 25) {
+ k <- ceiling(sqrt(nnet))
+ } else {
+ k <- ceiling(sqrt(nnet)) + 1
+ }
+ }
+ if (!is.null(thresh)) {
+ for (i in 1:length(net)) {
+ neti <- net[[i]]
+ neti[neti < quantile(c(neti[neti != 0]), thresh)] <- 0
+ net[[i]] <- neti
+ }
+ }
+ if (type == "functional") {
+ # compute the functional similarity
+ S3 <- matrix(0, nrow = nnet, ncol = nnet)
+ for (i in 1:nnet) {
+ for (j in 1:nnet) {
+ idx.i <- which(position - i >= 0)[1]
+ idx.j <- which(position - j >= 0)[1]
+ net.i <- net[[idx.i-1]]
+ net.j <- net[[idx.j-1]]
+ Gi <- (net.i[ , ,i-position[idx.i-1]] > 0)*1
+ Gj <- (net.j[ , ,j-position[idx.j-1]] > 0)*1
+ S3[i,j] <- sum(Gi * Gj)/sum(Gi+Gj-Gi*Gj,na.rm=TRUE)
+ }
+ }
+ # define the similarity matrix
+ S3[is.na(S3)] <- 0; diag(S3) <- 1
+ S_signalings <- S3
+ } else if (type == "structural") {
+ # compute the structure distance
+ D_signalings <- matrix(0, nrow = nnet, ncol = nnet)
+ for (i in 1:nnet) {
+ for (j in 1:nnet) {
+ idx.i <- which(position - i >= 0)[1]
+ idx.j <- which(position - j >= 0)[1]
+ net.i <- net[[idx.i-1]]
+ net.j <- net[[idx.j-1]]
+ Gi <- (net.i[ , ,i-position[idx.i-1]] > 0)*1
+ Gj <- (net.j[ , ,j-position[idx.j-1]] > 0)*1
+ D_signalings[i,j] <- computeNetD_structure(Gi,Gj)
+ }
+ }
+ # define the structure similarity matrix
+ D_signalings[is.infinite(D_signalings)] <- 0
+ D_signalings[is.na(D_signalings)] <- 0
+ S_signalings <- 1-D_signalings
+ }
+ # smooth the similarity matrix using SNN
+ SNN <- buildSNN(S_signalings, k = k, prune.SNN = 1/15)
+ Similarity <- as.matrix(S_signalings*SNN)
+ rownames(Similarity) <- signalingAll
+ colnames(Similarity) <- rownames(Similarity)
+ if (!is.list(methods::slot(object, slot.name)$similarity[[type]]$matrix)) {
+ methods::slot(object, slot.name)$similarity[[type]]$matrix <- NULL
+ }
+ # methods::slot(object, slot.name)$similarity[[type]]$matrix <- Similarity
+ methods::slot(object, slot.name)$similarity[[type]]$matrix[[comparison.name]] <- Similarity
+ return(object)
+ }
> #' @param slot.name the slot name of object that is used to compute centrality measures of signaling networks
> #' @param type "functional","structural"
> #' @param comparison a numerical vector giving the datasets for comparison. No need to define for a single dataset. Default are all datasets when object is a merged object
> #' @param k the number of nearest neighbors in running umap
> #' @param pathway.remove a range of the number of patterns
> #' @importFrom methods slot
> #' @return
> #' @export
> #'
> #' @examples
> netEmbedding <- function(object, slot.name = "netP", type = c("functional","structural"), comparison = NULL, pathway.remove = NULL, k = NULL) {
+ if (object@options$mode == "single") {
+ comparison <- "single"
+ cat("Manifold learning of the signaling networks for a single dataset", '\n')
+ } else if (object@options$mode == "merged") {
+ if (is.null(comparison)) {
+ comparison <- 1:length(unique(object@meta$datasets))
+ }
+ cat("Manifold learning of the signaling networks for datasets", as.character(comparison), '\n')
+ }
+ comparison.name <- paste(comparison, collapse = "-")
+ Similarity <- methods::slot(object, slot.name)$similarity[[type]]$matrix[[comparison.name]]
+ if (is.null(pathway.remove)) {
+ pathway.remove <- rownames(Similarity)[which(colSums(Similarity) == 1)]
+ }
+ if (length(pathway.remove) > 0) {
+ pathway.remove.idx <- which(rownames(Similarity) %in% pathway.remove)
+ Similarity <- Similarity[-pathway.remove.idx, -pathway.remove.idx]
+ }
+ if (is.null(k)) {
+ k <- ceiling(sqrt(dim(Similarity)[1])) + 1
+ }
+ options(warn = -1)
+ # dimension reduction
+ Y <- runUMAP(Similarity, min.dist = 0.3, n.neighbors = k)
+ if (!is.list(methods::slot(object, slot.name)$similarity[[type]]$dr)) {
+ methods::slot(object, slot.name)$similarity[[type]]$dr <- NULL
+ }
+ methods::slot(object, slot.name)$similarity[[type]]$dr[[comparison.name]] <- Y
+ return(object)
+ }
> cellchat <- netEmbedding(cellchat, type = "functional")
Manifold learning of the signaling networks for a single dataset
C:\Users\zzu\AppData\Local\R-MINI~1\envs\R-RETI~1\lib\site-packages\umap\umap_.py:133: UserWarning: A large number of your vertices were disconnected from the manifold.
Disconnection_distance = 1 has removed 142 edges.
It has fully disconnected 3 vertices.
You might consider using find_disconnected_points() to find and remove these points from your data.
Use umap.utils.disconnected_vertices() to identify them.
f"A large number of your vertices were disconnected from the manifold.\n"
> #' @param nCores number of workers when doing parallel
> #' @param k.eigen the number of eigenvalues used when doing spectral clustering
> #' @importFrom methods slot
> #' @importFrom future nbrOfWorkers plan
> #' @importFrom future.apply future_sapply
> #' @importFrom pbapply pbsapply
> #' @return
> #' @export
> #'
> #' @examples
> netClustering <- function(object, slot.name = "netP", type = c("functional","structural"), comparison = NULL, k = NULL, methods = "kmeans", do.plot = TRUE, fig.id = NULL, do.parallel = TRUE, nCores = 4, k.eigen = NULL) {
+ type <- match.arg(type)
+ if (object@options$mode == "single") {
+ comparison <- "single"
+ cat("Classification learning of the signaling networks for a single dataset", '\n')
+ } else if (object@options$mode == "merged") {
+ if (is.null(comparison)) {
+ comparison <- 1:length(unique(object@meta$datasets))
+ }
+ cat("Classification learning of the signaling networks for datasets", as.character(comparison), '\n')
+ }
+ comparison.name <- paste(comparison, collapse = "-")
+
+ Y <- methods::slot(object, slot.name)$similarity[[type]]$dr[[comparison.name]]
+ Y[is.na(Y)] <- 0
+ data.use <- Y
+ if (methods == "kmeans") {
+ if (!is.null(k)) {
+ clusters = kmeans(data.use,k,nstart=10)$cluster
+ } else {
+ N <- nrow(data.use)
+ kRange <- seq(2,min(N-1, 10),by = 1)
+ if (do.parallel) {
+ future::plan("multiprocess", workers = nCores)
+ options(future.globals.maxSize = 1000 * 1024^2)
+ }
+ my.sapply <- ifelse(
+ test = future::nbrOfWorkers() == 1,
+ yes = pbapply::pbsapply,
+ no = future.apply::future_sapply
+ )
+ results = my.sapply(
+ X = 1:length(kRange),
+ FUN = function(x) {
+ idents <- kmeans(data.use,kRange[x],nstart=10)$cluster
+ clusIndex <- idents
+ #adjMat0 <- as.numeric(outer(clusIndex, clusIndex, FUN = "==")) - outer(1:N, 1:N, "==")
+ adjMat0 <- Matrix::Matrix(as.numeric(outer(clusIndex, clusIndex, FUN = "==")), nrow = N, ncol = N)
+ return(list(adjMat = adjMat0, ncluster = length(unique(idents))))
+ },
+ simplify = FALSE
+ )
+ adjMat <- lapply(results, "[[", 1)
+ CM <- Reduce('+', adjMat)/length(kRange)
+ res <- computeEigengap(as.matrix(CM))
+ numCluster <- res$upper_bound
+ clusters = kmeans(data.use,numCluster,nstart=10)$cluster
+ if (do.plot) {
+ gg <- res$gg.obj
+ ggsave(filename= paste0("estimationNumCluster_",fig.id,"_",type,"_dataset_",comparison.name,".pdf"), plot=gg, width = 3.5, height = 3, units = 'in', dpi = 300)
+ }
+ }
+ } else if (methods == "spectral") {
+ A <- as.matrix(data.use)
+ D <- apply(A, 1, sum)
+ L <- diag(D)-A # unnormalized version
+ L <- diag(D^-0.5)%*%L%*% diag(D^-0.5) # normalized version
+ evL <- eigen(L,symmetric=TRUE) # evL$values is decreasing sorted when symmetric=TRUE
+ # pick the first k first k eigenvectors (corresponding k smallest) as data points in spectral space
+ plot(rev(evL$values)[1:30])
+ Z <- evL$vectors[,(ncol(evL$vectors)-k.eigen+1):ncol(evL$vectors)]
+ clusters = kmeans(Z,k,nstart=20)$cluster
+ }
+ if (!is.list(methods::slot(object, slot.name)$similarity[[type]]$group)) {
+ methods::slot(object, slot.name)$similarity[[type]]$group <- NULL
+ }
+ methods::slot(object, slot.name)$similarity[[type]]$group[[comparison.name]] <- clusters
+ return(object)
+ }
> #> Manifold learning of the signaling networks for a single dataset
> cellchat <- netClustering(cellchat, type = "functional")
Classification learning of the signaling networks for a single dataset
> #> Classification learning of the signaling networks for a single dataset
> # Visualization in 2D-space
> netVisual_embedding(cellchat, type = "functional", label.size = 3.5)
> cellchat <- computeNetSimilarity(cellchat, type = "structural")
> cellchat <- netEmbedding(cellchat, type = "structural")
Manifold learning of the signaling networks for a single dataset
> #> Manifold learning of the signaling networks for a single dataset
> cellchat <- netClustering(cellchat, type = "structural")
Classification learning of the signaling networks for a single dataset
> #> Classification learning of the signaling networks for a single dataset
> # Visualization in 2D-space
> netVisual_embedding(cellchat, type = "structural", label.size = 3.5)
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正确出图如下
image.png