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analysis.R
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analysis.R
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#' Compute and visualize the contribution of each ligand-receptor pair in the overall signaling pathways
#'
#' @param object CellChat object
#' @param signaling a signaling pathway name
#' @param signaling.name alternative signaling pathway name to show on the plot
#' @param sources.use a vector giving the index or the name of source cell groups
#' @param targets.use a vector giving the index or the name of target cell groups.
#' @param width the width of individual bar
#' @param vertex.receiver a numeric vector giving the index of the cell groups as targets in the first hierarchy plot
#' @param thresh threshold of the p-value for determining significant interaction
#' @param return.data whether return the data.frame consisting of the predicted L-R pairs and their contribution
#' @param x.rotation rotation of x-label
#' @param title the title of the plot
#' @param font.size font size of the text
#' @param font.size.title font size of the title
#' @importFrom dplyr select
#' @importFrom ggplot2 ggplot geom_bar aes coord_flip scale_x_discrete element_text theme ggtitle
#' @importFrom cowplot ggdraw draw_label plot_grid
#'
#' @return
#' @export
#'
#' @examples
netAnalysis_contribution <- function(object, signaling, signaling.name = NULL, sources.use = NULL, targets.use = NULL,
width = 0.1, vertex.receiver = NULL, thresh = 0.05, return.data = FALSE,
x.rotation = 0, title = "Contribution of each L-R pair",
font.size = 10, font.size.title = 10) {
pairLR <- searchPair(signaling = signaling, pairLR.use = object@LR$LRsig, key = "pathway_name", matching.exact = T, pair.only = T)
pair.name.use = select(object@DB$interaction[rownames(pairLR),],"interaction_name_2")
if (is.null(signaling.name)) {
signaling.name <- signaling
}
net <- object@net
pairLR.use.name <- dimnames(net$prob)[[3]]
pairLR.name <- intersect(rownames(pairLR), pairLR.use.name)
pairLR <- pairLR[pairLR.name, ]
prob <- net$prob
pval <- net$pval
prob[pval > thresh] <- 0
if (!is.null(sources.use)) {
if (is.character(sources.use)) {
if (all(sources.use %in% dimnames(prob)[[1]])) {
sources.use <- match(sources.use, dimnames(prob)[[1]])
} else {
stop("The input `sources.use` should be cell group names or a numerical vector!")
}
}
idx.t <- setdiff(1:nrow(prob), sources.use)
prob[idx.t, , ] <- 0
}
if (!is.null(targets.use)) {
if (is.character(targets.use)) {
if (all(targets.use %in% dimnames(prob)[[1]])) {
targets.use <- match(targets.use, dimnames(prob)[[2]])
} else {
stop("The input `targets.use` should be cell group names or a numerical vector!")
}
}
idx.t <- setdiff(1:nrow(prob), targets.use)
prob[ ,idx.t, ] <- 0
}
if (length(pairLR.name) > 1) {
pairLR.name.use <- pairLR.name[apply(prob[,,pairLR.name], 3, sum) != 0]
} else {
pairLR.name.use <- pairLR.name[sum(prob[,,pairLR.name]) != 0]
}
if (length(pairLR.name.use) == 0) {
stop(paste0('There is no significant communication of ', signaling.name))
} else {
pairLR <- pairLR[pairLR.name.use,]
}
prob <- prob[,,pairLR.name.use]
if (length(dim(prob)) == 2) {
prob <- replicate(1, prob, simplify="array")
dimnames(prob)[3] <- pairLR.name.use
}
prob <-(prob-min(prob))/(max(prob)-min(prob))
if (is.null(vertex.receiver)) {
pSum <- apply(prob, 3, sum)
pSum.max <- sum(prob)
pSum <- pSum/pSum.max
pSum[is.na(pSum)] <- 0
y.lim <- max(pSum)
pair.name <- unlist(dimnames(prob)[3])
pair.name <- factor(pair.name, levels = unique(pair.name))
if (!is.null(pairLR.name.use)) {
pair.name <- pair.name.use[as.character(pair.name),1]
pair.name <- factor(pair.name, levels = unique(pair.name))
}
mat <- pSum
df1 <- data.frame(name = pair.name, contribution = mat)
if(nrow(df1) < 10) {
df2 <- data.frame(name = as.character(1:(10-nrow(df1))), contribution = rep(0, 10-nrow(df1)))
df <- rbind(df1, df2)
} else {
df <- df1
}
df <- df[order(df$contribution, decreasing = TRUE), ]
# df$name <- factor(df$name, levels = unique(df$name))
df$name <- factor(df$name,levels=df$name[order(df$contribution, decreasing = TRUE)])
df1$name <- factor(df1$name,levels=df1$name[order(df1$contribution, decreasing = TRUE)])
gg <- ggplot(df, aes(x=name, y=contribution)) + geom_bar(stat="identity", width = 0.7) +
theme_classic() + theme(axis.text.y = element_text(angle = x.rotation, hjust = 1,size=font.size, colour = 'black'), axis.text=element_text(size=font.size),
axis.title.y = element_text(size= font.size), axis.text.x = element_blank(), axis.ticks = element_blank()) +
xlab("") + ylab("Relative contribution") + ylim(0,y.lim) + coord_flip() + theme(legend.position="none") +
scale_x_discrete(limits = rev(levels(df$name)), labels = c(rep("", max(0, 10-nlevels(df1$name))),rev(levels(df1$name))))
if (!is.null(title)) {
gg <- gg + ggtitle(title)+ theme(plot.title = element_text(hjust = 0.5, size = font.size.title))
}
gg
} else {
pair.name <- factor(unlist(dimnames(prob)[3]), levels = unique(unlist(dimnames(prob)[3])))
# show all the communications
pSum <- apply(prob, 3, sum)
pSum.max <- sum(prob)
pSum <- pSum/pSum.max
pSum[is.na(pSum)] <- 0
y.lim <- max(pSum)
df<- data.frame(name = pair.name, contribution = pSum)
gg <- ggplot(df, aes(x=name, y=contribution)) + geom_bar(stat="identity",width = 0.2) +
theme_classic() + theme(axis.text=element_text(size=10),axis.text.x = element_text(angle = x.rotation, hjust = 1,size=8),
axis.title.y = element_text(size=10)) +
xlab("") + ylab("Relative contribution") + ylim(0,y.lim)+ ggtitle("All")+ theme(plot.title = element_text(hjust = 0.5))#+
# show the communications in Hierarchy1
if (dim(prob)[3] > 1) {
pSum <- apply(prob[,vertex.receiver,], 3, sum)
} else {
pSum <- sum(prob[,vertex.receiver,])
}
pSum <- pSum/pSum.max
pSum[is.na(pSum)] <- 0
df<- data.frame(name = pair.name, contribution = pSum)
gg1 <- ggplot(df, aes(x=name, y=contribution)) + geom_bar(stat="identity",width = 0.2) +
theme_classic() + theme(axis.text=element_text(size=10),axis.text.x = element_text(angle = x.rotation, hjust = 1,size=8), axis.title.y = element_text(size=10)) +
xlab("") + ylab("Relative contribution") + ylim(0,y.lim)+ ggtitle("Hierarchy1") + theme(plot.title = element_text(hjust = 0.5))#+
#scale_x_discrete(limits = c(0,1))
# show the communications in Hierarchy2
if (dim(prob)[3] > 1) {
pSum <- apply(prob[,setdiff(1:dim(prob)[1],vertex.receiver),], 3, sum)
} else {
pSum <- sum(prob[,setdiff(1:dim(prob)[1],vertex.receiver),])
}
pSum <- pSum/pSum.max
pSum[is.na(pSum)] <- 0
df<- data.frame(name = pair.name, contribution = pSum)
gg2 <- ggplot(df, aes(x=name, y=contribution)) + geom_bar(stat="identity", width=0.9) +
theme_classic() + theme(axis.text=element_text(size=10),axis.text.x = element_text(angle = x.rotation, hjust = 1,size=8), axis.title.y = element_text(size=10)) +
xlab("") + ylab("Relative contribution") + ylim(0,y.lim)+ ggtitle("Hierarchy2")+ theme(plot.title = element_text(hjust = 0.5))#+
#scale_x_discrete(limits = c(0,1))
title <- cowplot::ggdraw() + cowplot::draw_label(paste0("Contribution of each signaling in ", signaling.name, " pathway"), fontface='bold', size = 10)
gg.combined <- cowplot::plot_grid(gg, gg1, gg2, nrow = 1)
gg.combined <- cowplot::plot_grid(title, gg.combined, ncol = 1, rel_heights=c(0.1, 1))
gg <- gg.combined
gg
}
if (return.data) {
df <- subset(df, contribution > 0)
return(list(LR.contribution = df, gg.obj = gg))
} else {
return(gg)
}
}
#' Compute the network centrality scores allowing identification of dominant senders, receivers, mediators and influencers in all inferred communication networks
#'
#' NB: This function was previously named as `netAnalysis_signalingRole`. The previous function `netVisual_signalingRole` is now named as `netAnalysis_signalingRole_network`.
#'
#' @param object CellChat object; If object = NULL, USER must provide `net`
#' @param slot.name the slot name of object that is used to compute centrality measures of signaling networks
#' @param net compute the centrality measures on a specific signaling network given by a 2 or 3 dimemsional array net
#' @param net.name a character vector giving the name of signaling networks
#' @param thresh threshold of the p-value for determining significant interaction
#' @importFrom future nbrOfWorkers
#' @importFrom methods slot
#' @importFrom future.apply future_sapply
#' @importFrom pbapply pbsapply
#'
#' @return
#' @export
#'
netAnalysis_computeCentrality <- function(object = NULL, slot.name = "netP", net = NULL, net.name = NULL, thresh = 0.05) {
if (is.null(net)) {
prob <- methods::slot(object, slot.name)$prob
pval <- methods::slot(object, slot.name)$pval
pval[prob == 0] <- 1
prob[pval >= thresh] <- 0
net = prob
}
if (is.null(net.name)) {
net.name <- dimnames(net)[[3]]
}
if (length(dim(net)) == 3) {
nrun <- dim(net)[3]
my.sapply <- ifelse(
test = future::nbrOfWorkers() == 1,
yes = pbapply::pbsapply,
no = future.apply::future_sapply
)
centr.all = my.sapply(
X = 1:nrun,
FUN = function(x) {
net0 <- net[ , , x]
return(computeCentralityLocal(net0))
},
simplify = FALSE
)
} else {
centr.all <- as.list(computeCentralityLocal(net))
}
names(centr.all) <- net.name
if (is.null(object)) {
return(centr.all)
} else {
slot(object, slot.name)$centr <- centr.all
return(object)
}
}
#' Compute Centrality measures for a signaling network
#'
#' @param net compute the centrality measures on a specific signaling network given by a 2 or 3 dimemsional array net
#' @importFrom igraph graph_from_adjacency_matrix strength hub_score authority_score eigen_centrality page_rank betweenness E
#' @importFrom sna flowbet infocent
#'
#' @return
computeCentralityLocal <- function(net) {
centr <- vector("list")
G <- igraph::graph_from_adjacency_matrix(net, mode = "directed", weighted = T)
centr$outdeg_unweighted <- rowSums(net > 0)
centr$indeg_unweighted <- colSums(net > 0)
centr$outdeg <- igraph::strength(G, mode="out")
centr$indeg <- igraph::strength(G, mode="in")
centr$hub <- igraph::hub_score(G)$vector
centr$authority <- igraph::authority_score(G)$vector # A node has high authority when it is linked by many other nodes that are linking many other nodes.
centr$eigen <- igraph::eigen_centrality(G)$vector # A measure of influence in the network that takes into account second-order connections
centr$page_rank <- igraph::page_rank(G)$vector
igraph::E(G)$weight <- 1/igraph::E(G)$weight
centr$betweenness <- igraph::betweenness(G)
#centr$flowbet <- try(sna::flowbet(net)) # a measure of its role as a gatekeeper for the flow of communication between any two cells; the total maximum flow (aggregated across all pairs of third parties) mediated by v.
#centr$info <- try(sna::infocent(net)) # actors with higher information centrality are predicted to have greater control over the flow of information within a network; highly information-central individuals tend to have a large number of short paths to many others within the social structure.
centr$flowbet <- tryCatch({
sna::flowbet(net)
}, error = function(e) {
as.vector(matrix(0, nrow = nrow(net), ncol = 1))
})
centr$info <- tryCatch({
sna::infocent(net, diag = T, rescale = T, cmode = "lower")
# sna::infocent(net, diag = T, rescale = T, cmode = "weak")
}, error = function(e) {
as.vector(matrix(0, nrow = nrow(net), ncol = 1))
})
return(centr)
}
#' Select the number of the patterns for running `identifyCommunicationPatterns`
#'
#' We infer the number of patterns based on two metrics that have been implemented in the NMF R package, including Cophenetic and Silhouette. Both metrics measure the stability for a particular number of patterns based on a hierarchical clustering of the consensus matrix. For a range of the number of patterns, a suitable number of patterns is the one at which Cophenetic and Silhouette values begin to drop suddenly.
#'
#' @param object CellChat object
#' @param slot.name the slot name of object that is used to compute centrality measures of signaling networks
#' @param pattern "outgoing" or "incoming"
#' @param k.range a range of the number of patterns
#' @param title.name title of plot
#' @param do.facet whether use facet plot showing the two measures
#' @param nrun number of runs when performing NMF
#' @param seed.use seed when performing NMF
#' @importFrom methods slot
# #' @importFrom NMF nmfEstimateRank
#' @import NMF
# #' @importFrom ggplot2 scale_color_brewer
#' @import ggplot2
#' @return a ggplot object
#' @export
#'
#' @examples
selectK <- function(object, slot.name = "netP", pattern = c("outgoing","incoming"), title.name = NULL, do.facet = TRUE, k.range = seq(2,10), nrun = 30, seed.use = 10) {
pattern <- match.arg(pattern)
prob <- methods::slot(object, slot.name)$prob
if (pattern == "outgoing") {
data_sender <- apply(prob, c(1,3), sum)
data_sender = sweep(data_sender, 2L, apply(data_sender, 2, function(x) max(x, na.rm = TRUE)), '/', check.margin = FALSE)
data0 = as.matrix(data_sender)
} else if (pattern == "incoming") {
data_receiver <- apply(prob, c(2,3), sum)
data_receiver = sweep(data_receiver, 2L, apply(data_receiver, 2, function(x) max(x, na.rm = TRUE)), '/', check.margin = FALSE)
data0 = as.matrix(data_receiver)
}
options(warn = -1)
data <- data0
data <- data[rowSums(data)!=0,]
if (is.null(title.name)) {
title.name <- paste0(pattern, " signaling \n")
# title.name <- paste0(pattern, " signaling \n (nrun = ", nrun, ", seed = ", seed.use, ")")
}
res <- NMF::nmfEstimateRank(data, range = k.range, method = 'lee', nrun=nrun, seed = seed.use)
df1 <- data.frame(k = res$measures$rank, score = res$measures$cophenetic, Measure = "Cophenetic")
df2 <- data.frame(k = res$measures$rank, score = res$measures$silhouette.consensus, Measure = "Silhouette")
# df3 <- data.frame(k = res$measures$rank, score = res$measures$dispersion, Measure = "Dispersion")
df <- rbind(df1, df2)
#df <- rbind(df1, df2, df3)
gg <- ggplot(df, aes(x = k, y = score, group = Measure, color = Measure)) + geom_line(size=1) +
geom_point() +
theme_classic() + labs(x = 'Number of patterns', y='Measure score') +
labs(title = title.name) + theme(plot.title = element_text(size = 10, face = "bold", hjust = 0.5)) +
theme(legend.position = "right") + theme(text = element_text(size = 10)) + scale_x_discrete(limits = (unique(df$k))) +
scale_color_brewer(palette="Set2") + guides(color=guide_legend("Measure type"))
if (do.facet) {
gg <- gg + facet_wrap(~ Measure, scales='free')
}
gg
return(gg)
}
#' Identification of major signals for specific cell groups and general communication patterns
#'
#' @param object CellChat object
#' @param slot.name the slot name of object that is used to compute centrality measures of signaling networks
#' @param pattern "outgoing" or "incoming"
#' @param k the number of patterns
#' @param k.range a range of the number of patterns
#' @param heatmap.show whether showing heatmap
#' @param color.use the character vector defining the color of each cell group
#' @param color.heatmap a color name in brewer.pal
#' @param title.legend the title of legend in heatmap
#' @param width width of heatmap
#' @param height height of heatmap
#' @param font.size fontsize in heatmap
#' @importFrom methods slot
#' @importFrom NMF nmfEstimateRank nmf
#' @importFrom grDevices colorRampPalette
#' @importFrom RColorBrewer brewer.pal
#' @importFrom ComplexHeatmap Heatmap HeatmapAnnotation draw
#' @importFrom stats setNames
#' @importFrom grid grid.grabExpr grid.newpage pushViewport grid.draw unit gpar viewport popViewport
#'
#' @return
#' @export
#'
#' @examples
identifyCommunicationPatterns <- function(object, slot.name = "netP", pattern = c("outgoing","incoming"), k = NULL, k.range = seq(2,10), heatmap.show = TRUE,
color.use = NULL, color.heatmap = "Spectral", title.legend = "Contributions",
width = 4, height = 6, font.size = 8) {
pattern <- match.arg(pattern)
prob <- methods::slot(object, slot.name)$prob
if (pattern == "outgoing") {
data_sender <- apply(prob, c(1,3), sum)
data_sender = sweep(data_sender, 2L, apply(data_sender, 2, function(x) max(x, na.rm = TRUE)), '/', check.margin = FALSE)
data0 = as.matrix(data_sender)
} else if (pattern == "incoming") {
data_receiver <- apply(prob, c(2,3), sum)
data_receiver = sweep(data_receiver, 2L, apply(data_receiver, 2, function(x) max(x, na.rm = TRUE)), '/', check.margin = FALSE)
data0 = as.matrix(data_receiver)
}
options(warn = -1)
data <- data0
data <- data[rowSums(data)!=0,]
if (is.null(k)) {
stop("Please run the function `selectK` for selecting a suitable k!")
}
outs_NMF <- NMF::nmf(data, rank = k, method = 'lee', seed = 'nndsvd')
W <- scaleMat(outs_NMF@fit@W, 'r1')
H <- scaleMat(outs_NMF@fit@H, 'c1')
colnames(W) <- paste0("Pattern ", seq(1,ncol(W))); rownames(H) <- paste0("Pattern ", seq(1,nrow(H)));
if (heatmap.show) {
net <- W
if (is.null(color.use)) {
color.use <- scPalette(length(rownames(net)))
}
color.heatmap = grDevices::colorRampPalette(rev(RColorBrewer::brewer.pal(n = 9, name = color.heatmap)))(255)
df<- data.frame(group = rownames(net)); rownames(df) <- rownames(net)
cell.cols.assigned <- setNames(color.use, unique(as.character(df$group)))
row_annotation <- HeatmapAnnotation(df = df, col = list(group = cell.cols.assigned),which = "row",
show_legend = FALSE, show_annotation_name = FALSE,
simple_anno_size = grid::unit(0.2, "cm"))
ht1 = Heatmap(net, col = color.heatmap, na_col = "white", name = "Contribution",
left_annotation = row_annotation,
cluster_rows = T,cluster_columns = F,clustering_method_rows = "average",
row_names_side = "left",row_names_rot = 0,row_names_gp = gpar(fontsize = font.size),column_names_gp = gpar(fontsize = font.size),
width = unit(width, "cm"), height = unit(height, "cm"),
show_heatmap_legend = F,
column_title = "Cell patterns",column_title_gp = gpar(fontsize = 10)
)
net <- t(H)
ht2 = Heatmap(net, col = color.heatmap, na_col = "white", name = "Contribution",
cluster_rows = T,cluster_columns = F,clustering_method_rows = "average",
row_names_side = "left",row_names_rot = 0,row_names_gp = gpar(fontsize = font.size),column_names_gp = gpar(fontsize = font.size),
width = unit(width, "cm"), height = unit(height, "cm"),
column_title = "Communication patterns",column_title_gp = gpar(fontsize = 10),
heatmap_legend_param = list(title = title.legend, title_gp = gpar(fontsize = 8, fontface = "plain"),title_position = "leftcenter-rot",
border = NA, at = c(round(min(net, na.rm = T), digits = 1), round(max(net, na.rm = T), digits = 1)),
legend_height = unit(20, "mm"),labels_gp = gpar(fontsize = 6),grid_width = unit(2, "mm"))
)
gb_ht1 = grid.grabExpr(draw(ht1))
gb_ht2 = grid.grabExpr(draw(ht2))
#grid.newpage()
pushViewport(viewport(x = 0.1, y = 0.1, width = 0.2, height = 0.5, just = c("left", "bottom")))
grid.draw(gb_ht1)
popViewport()
pushViewport(viewport(x = 0.6, y = 0.1, width = 0.2, height = 0.5, just = c("left", "bottom")))
grid.draw(gb_ht2)
popViewport()
}
data_W <- as.data.frame(as.table(W)); colnames(data_W) <- c("CellGroup","Pattern","Contribution")
data_H <- as.data.frame(as.table(H)); colnames(data_H) <- c("Pattern","Signaling","Contribution")
res.pattern = list("cell" = data_W, "signaling" = data_H)
methods::slot(object, slot.name)$pattern[[pattern]] <- list(data = data0, pattern = res.pattern)
return(object)
}
#' Compute signaling network similarity for any pair of signaling networks
#'
#' @param object CellChat object
#' @param slot.name the slot name of object that is used to compute centrality measures of signaling networks
#' @param type "functional","structural"
#' @param k the number of nearest neighbors
#' @param thresh the fraction (0 to 0.25) of interactions to be trimmed before computing network similarity
#' @importFrom methods slot
#'
#' @return
#' @export
#'
#' @examples
computeNetSimilarity <- function(object, slot.name = "netP", type = c("functional","structural"), k = NULL, thresh = NULL) {
type <- match.arg(type)
prob = methods::slot(object, slot.name)$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)
}
#' Compute signaling network similarity for any pair of datasets
#'
#' @param object A merged CellChat 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
#' @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)
}
#' Manifold learning of the signaling networks based on their similarity
#'
#' @param object CellChat 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 pathway.remove a range of the number of patterns
#' @param umap.method UMAP implementation to run.
#'
#' Can be umap-learn: Run the python umap-learn package; uwot: Runs umap via the uwot R package; If umap.method = "uwot", please make sure you have installed the 'uwot' (https://github.com/jlmelville/uwot)
#'
#' @param n_neighbors the number of nearest neighbors in running umap
#' @param min_dist This controls how tightly the embedding is allowed compress points together.
#' Larger values ensure embedded points are moreevenly distributed, while smaller values allow the
#' algorithm to optimise more accurately with regard to local structure. Sensible values are in the range 0.001 to 0.5.
#' @param ... Parameters passing to umap
#' @importFrom methods slot
#' @return
#' @export
#'
#' @examples
netEmbedding <- function(object, slot.name = "netP", type = c("functional","structural"), comparison = NULL, pathway.remove = NULL,
umap.method = c("umap-learn", "uwot"), n_neighbors = NULL,min_dist = 0.3,...) {
umap.method <- match.arg(umap.method)
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]]$matrix[[comparison.name]]
if (is.null(pathway.remove)) {
pathway.remove <- rownames(Similarity)[which(colSums(Similarity) == 1)]
}
if (length(pathway.remove) > 0) {
pathway.remove.idx <- which(rownames(Similarity) %in% pathway.remove)
Similarity <- Similarity[-pathway.remove.idx, -pathway.remove.idx]
}
if (is.null(n_neighbors)) {
n_neighbors <- ceiling(sqrt(dim(Similarity)[1])) + 1
}
options(warn = -1)
# dimension reduction
if (umap.method == "umap-learn") {
Y <- runUMAP(Similarity, min_dist = min_dist, n_neighbors = n_neighbors,...)
} else if (umap.method == "uwot") {
Y <- uwot::umap(Similarity, min_dist = min_dist, n_neighbors = n_neighbors,...)
colnames(Y) <- paste0('UMAP', 1:ncol(Y))
rownames(Y) <- colnames(Similarity)
}
if (!is.list(methods::slot(object, slot.name)$similarity[[type]]$dr)) {
methods::slot(object, slot.name)$similarity[[type]]$dr <- NULL
}
methods::slot(object, slot.name)$similarity[[type]]$dr[[comparison.name]] <- Y
return(object)
}
#' Classification learning of the signaling networks
#'
#' @param object CellChat object
#' @param slot.name the slot name of object that is used to compute centrality measures of signaling networks
#' @param type "functional","structural"
#' @param comparison a numerical vector giving the datasets for comparison. No need to define for a single dataset. Default are all datasets when object is a merged object
#' @param k the number of signaling groups when running kmeans
#' @param methods the methods for clustering: "kmeans" or "spectral"
#' @param do.plot whether showing the eigenspectrum for inferring number of clusters; Default will save the plot
#' @param fig.id add a unique figure id when saving the plot
#' @param do.parallel whether doing parallel when inferring the number of signaling groups when running kmeans
#' @param nCores number of workers when doing parallel
#' @param k.eigen the number of eigenvalues used when doing spectral clustering
#' @importFrom methods slot
#' @importFrom future nbrOfWorkers plan
#' @importFrom future.apply future_sapply
#' @importFrom pbapply pbsapply
#' @return
#' @export
#'
#' @examples
netClustering <- function(object, slot.name = "netP", type = c("functional","structural"), comparison = NULL, k = NULL, methods = "kmeans", do.plot = TRUE, fig.id = NULL, do.parallel = TRUE, nCores = 4, k.eigen = NULL) {
type <- match.arg(type)
if (object@options$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]]
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]]$group[[comparison.name]] <- clusters
return(object)
}
#' Build SNN matrix
# #' Adapted from swne (https://github.com/yanwu2014/swne)
#' @param data.use Features x samples matrix to use to build the SNN
#' @param k Defines k for the k-nearest neighbor algorithm
#' @param k.scale Granularity option for k.param
#' @param prune.SNN Sets the cutoff for acceptable Jaccard distances when
#' computing the neighborhood overlap for the SNN construction.
#'
#' @return Returns similarity matrix in sparse matrix format
#'
#' @importFrom FNN get.knn
#' @importFrom Matrix sparseMatrix
#' @export
#'
buildSNN <- function(data.use, k = 10, k.scale = 10, prune.SNN = 1/15) {
n.cells <- ncol(data.use)
if (n.cells < k) {
stop("k cannot be greater than the number of samples")
}
## find the k-nearest neighbors for each single cell
my.knn <- FNN::get.knn(t(as.matrix(data.use)), k = min(k.scale * k, n.cells - 1))
nn.ranked <- cbind(1:n.cells, my.knn$nn.index[, 1:(k - 1)])
nn.large <- my.knn$nn.index
w <- ComputeSNN(nn.ranked, prune.SNN)
colnames(w) <- rownames(w) <- colnames(data.use)
Matrix::diag(w) <- 1
return(w)
}
#' Compute the eigengap of a given matrix for inferring the number of clusters
#'
#' @param CM consensus matrix
#' @param tau truncated consensus matrix
#' @param tol tolerance
#' @return
#' @import ggplot2
#' @export
computeEigengap <- function(CM, tau = NULL, tol = 0.01){
# compute the drop tolerance, enforcing parsimony of components
K.init <- computeLaplacian(CM, tol = tol)$n_zeros
if (is.null(tau)) {
if (K.init <= 5) {
tau = 0.3
} else if (K.init <= 10){
tau = 0.4
} else {
tau = 0.5
}
}
# truncate the ensemble consensus matrix
CM[CM <= tau] <- 0;
# normalize and make symmetric
CM <- (CM + t(CM))/2
eigs <- computeLaplacian(CM, tol = tol)
# compute the largest eigengap
gaps <- diff(eigs$val)
upper_bound <- which(gaps == max(gaps))
# compute the number of zero eigenvalues
lower_bound <- eigs$n_zeros
df <- data.frame(nCluster = 1:min(c(30,length(eigs$val))), eigenVal = eigs$val[1:min(c(30,length(eigs$val)))])
g <- ggplot(df, aes(x = nCluster, y = eigenVal)) + geom_point(size = 1) +
geom_point(aes(x= upper_bound, y= eigs$val[upper_bound]), colour="red", size = 3, pch = 1) + theme(legend.position="none")
title.name <- paste0('Inferred number of clusters: ', upper_bound,'; Min number: ', lower_bound)
g <- g + labs(title = title.name) + theme_bw() + scale_x_continuous(breaks=seq(0,30,5)) +
theme(plot.title = element_text(size = 10, face = "bold", hjust = 0.5)) +
theme(text = element_text(size = 10)) + labs(x = 'Number of clusters', y = 'Eigenvalue of graph Laplacian')+
theme(axis.text.x = element_text(size = 8), axis.text.y = element_text(size = 8))
# ggsave(filename= paste0("estimationNumCluster_eigenspectrum",sample.int(100,1),".pdf"), plot=g, width = 3.5, height = 3, units = 'in', dpi = 300)
return(list(upper_bound = upper_bound,
lower_bound = lower_bound,
eigs = eigs,
gg.obj = g))
}
#' Compute eigenvalues of associated Laplacian matrix of a given matrix
#'
#' @param CM consensus matrix
#' @param tol tolerance
#' @return
#' @importFrom RSpectra eigs_sym
#' @importFrom Matrix colSums
#' @export
computeLaplacian <- function(CM, tol = 0.01) {
# Normalized Laplacian:
Dsq <- sqrt(Matrix::colSums(CM))
L <- -Matrix::t(CM / Dsq) / Dsq
Matrix::diag(L) <- 1 + Matrix::diag(L)
numEigs <- min(100,nrow(CM))
res <- RSpectra::eigs_sym(L, k = numEigs, which = "SM", opt = list(tol = 1e-4))
eigs <- abs(Re(res$values))
n_zeros <- sum(eigs <= tol)
return(list(val = sort(eigs), n_zeros = n_zeros))
}
#' Rank the similarity of the shared signaling pathways based on their joint manifold learning
#'
#' @param object CellChat object
#' @param slot.name the slot name of object that is used to compute centrality measures of signaling networks
#' @param type "functional","structural"
#' @param comparison1 a numerical vector giving the datasets for comparison. This should be the same as `comparison` in `computeNetSimilarityPairwise`
#' @param comparison2 a numerical vector with two elements giving the datasets for comparison.
#'
#' If there are more than 2 datasets defined in `comparison1`, `comparison2` can be defined to indicate which two datasets used for computing the distance.
#' e.g., comparison2 = c(1,3) indicates the first and third datasets defined in `comparison1` will be used for comparison.
#' @param x.rotation rotation of x-labels
#' @param title main title of the plot
#' @param bar.w the width of bar plot
#' @param color.use defining the color
#' @param font.size font size
#' @import ggplot2
#' @importFrom methods slot
#' @return
#' @export
#'
#' @examples
rankSimilarity <- function(object, slot.name = "netP", type = c("functional","structural"), comparison1 = NULL, comparison2 = c(1,2),
x.rotation = 90, title = NULL, color.use = NULL, bar.w = NULL, font.size = 8) {
type <- match.arg(type)
if (is.null(comparison1)) {
comparison1 <- 1:length(unique(object@meta$datasets))
}
comparison.name <- paste(comparison1, collapse = "-")
cat("Compute the distance of signaling networks between datasets", as.character(comparison1[comparison2]), '\n')
comparison2.name <- names(methods::slot(object, slot.name))[comparison1[comparison2]]
# net <- list()
# for (i in 1:length(comparison2)) {
# net[[i]] = methods::slot(object, slot.name)[[comparison1[comparison2[i]]]]$prob
# }
#net.dim <- sapply(net, dim)[3,]
#position <- cumsum(net.dim); position <- c(0,position)
# if (is.null(pathway.remove)) {
# similarity <- methods::slot(object, slot.name)$similarity[[type]]$matrix[[comparison.name]]
# pathway.remove <- rownames(similarity)[which(colSums(similarity) == 1)]
# pathway.remove.idx <- which(rownames(similarity) %in% pathway.remove)
# }
# if (length(pathway.remove.idx) > 0) {
# for (i in 1:length(pathway.remove.idx)) {
# idx <- which(position - pathway.remove.idx[i] > 0)
# if (!is.null(idx)) {
# position[idx[1]] <- position[idx[1]] - 1
# if (idx[1] == 2) {
# position[3] <- position[3] - 1
# }
# }
# }
# }
Y <- methods::slot(object, slot.name)$similarity[[type]]$dr[[comparison.name]]
group <- sub(".*--", "", rownames(Y))
data1 <- Y[group %in% comparison2.name[1], ]
data2 <- Y[group %in% comparison2.name[2], ]
rownames(data1) <- sub("--.*", "", rownames(data1))
rownames(data2) <- sub("--.*", "", rownames(data2))
pathway.show = as.character(intersect(rownames(data1), rownames(data2)))
data1 <- data1[pathway.show, ]
data2 <- data2[pathway.show, ]
euc.dist <- function(x1, x2) sqrt(sum((x1 - x2) ^ 2))
dist <- NULL
for(i in 1:nrow(data1)) dist[i] <- euc.dist(data1[i,],data2[i,])
df <- data.frame(name = pathway.show, dist = dist, row.names = pathway.show)
df <- df[order(df$dist), , drop = F]
df$name <- factor(df$name, levels = as.character(df$name))
gg <- ggplot(df, aes(x=name, y=dist)) + geom_bar(stat="identity",width = bar.w) +
theme_classic() + theme(text=element_text(size=font.size),axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.y = element_text(size=font.size)) +
xlab("") + ylab("Pathway distance") + coord_flip()#+
if (!is.null(title)) {
gg <- gg + ggtitle(title)+ theme(plot.title = element_text(hjust = 0.5))
}
if (!is.null(color.use)) {
gg <- gg + scale_fill_manual(values = ggplot2::alpha(color.use, alpha = 1), drop = FALSE, na.value = "white")
gg <- gg + scale_colour_manual(values = color.use, drop = FALSE, na.value = "white")
}
return(gg)
}
#' Rank signaling networks based on the information flow or the number of interactions
#'
#' This function can also be used to rank signaling from certain cell groups to other cell groups
#'
#' @param object CellChat object
#' @param slot.name the slot name of object that is used to compute centrality measures of signaling networks
#' @param measure "weight" or "count". "weight": comparing the total interaction weights (strength); "count": comparing the number of interactions;
#' @param mode "single","comparison"
#' @param comparison a numerical vector giving the datasets for comparison; a single value means ranking for only one dataset and two values means ranking comparison for two datasets
#' @param color.use defining the color for each cell group
#' @param stacked whether plot the stacked bar plot
#' @param sources.use a vector giving the index or the name of source cell groups
#' @param targets.use a vector giving the index or the name of target cell groups.
#' @param signaling a vector giving the signaling pathway to show
#' @param pairLR a vector giving the names of L-R pairs to show (e.g, pairLR = c("IL1A_IL1R1_IL1RAP","IL1B_IL1R1_IL1RAP"))
#' @param signaling.type a char giving the types of signaling from the three categories c("Secreted Signaling", "ECM-Receptor", "Cell-Cell Contact")