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netVisual_heatmap bug fix #339

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bac481c
netVisual_heatmap bug fix
adrdufour Apr 10, 2025
18070aa
return p-value
adrdufour May 9, 2025
7912197
Merge branch 'jinworks:main' into main
adrdufour Jul 21, 2025
a4c1799
change in rank net function
adrdufour Jul 21, 2025
d946f8a
fox colors
adrdufour Jul 21, 2025
a2fe04b
change chord_gene function for cluster group visualisation
adrdufour Jul 25, 2025
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131 changes: 48 additions & 83 deletions R/analysis.R
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@

#' Compute and visualize the contribution of each ligand-receptor pair in the overall signaling pathways
#'
#' @param object CellChat object
Expand Down Expand Up @@ -1029,14 +1028,14 @@ rankNet <- function(object, slot.name = "netP", measure = c("weight","count"), m
measure <- match.arg(measure)
mode <- match.arg(mode)
options(warn = -1)
object.names <- names(methods::slot(object, slot.name))
object1 <- methods::slot(object, slot.name)
object.names <- names(object1)
if (measure == "weight") {
ylabel = "Information flow"
} else if (measure == "count") {
ylabel = "Number of interactions"
}
if (mode == "single") {
object1 <- methods::slot(object, slot.name)
prob = object1$prob
prob[object1$pval > thresh] <- 0
if (measure == "count") {
Expand Down Expand Up @@ -1071,12 +1070,8 @@ rankNet <- function(object, slot.name = "netP", measure = c("weight","count"), m
pSum <- apply(prob, 3, sum)
pSum.original <- pSum
if (measure == "weight") {
pSum <- -1/log(pSum)
pSum <- log(1+pSum)
pSum[is.na(pSum)] <- 0
idx1 <- which(is.infinite(pSum) | pSum < 0)
values.assign <- seq(max(pSum)*1.1, max(pSum)*1.5, length.out = length(idx1))
position <- sort(pSum.original[idx1], index.return = TRUE)$ix
pSum[idx1] <- values.assign[match(1:length(idx1), position)]
} else if (measure == "count") {
pSum <- pSum.original
}
Expand All @@ -1087,12 +1082,7 @@ rankNet <- function(object, slot.name = "netP", measure = c("weight","count"), m
idx <- with(df, order(df$contribution))
df <- df[idx, ]
df$name <- factor(df$name, levels = as.character(df$name))
for (i in 1:length(pair.name)) {
df.t <- df[df$name == pair.name[i], "contribution"]
if (sum(df.t) == 0) {
df <- df[-which(df$name == pair.name[i]), ]
}
}
df <- df[ave(df$contribution, df$name, FUN = sum) != 0, ]

if (!is.null(signaling.type)) {
LR <- subset(object@DB$interaction, annotation %in% signaling.type)
Expand Down Expand Up @@ -1163,38 +1153,32 @@ rankNet <- function(object, slot.name = "netP", measure = c("weight","count"), m
}
prob.list[[i]] <- prob
pSum.original[[i]] <- apply(prob, 3, sum)
if (measure == "weight") {
pSum[[i]] <- -1/log(pSum.original[[i]])
}
# Step 1: Flatten both lists to a numeric vector
all_values <- unlist(pSum.original, use.names = FALSE)

# Step 2: Compute global min and max
global_min <- min(all_values, na.rm = TRUE)
global_max <- max(all_values, na.rm = TRUE)

for (i in 1:length(comparison)) {
if (measure == "weight") {
pSum[[i]] <- log(1+pSum.original[[i]])
pSum[[i]][is.na(pSum[[i]])] <- 0
idx[[i]] <- which(is.infinite(pSum[[i]]) | pSum[[i]] < 0)
pSum.original.all <- c(pSum.original.all, pSum.original[[i]][idx[[i]]])
} else if (measure == "count") {
pSum[[i]] <- pSum.original[[i]] # the prob is already binarized in line 1136
}
pair.name[[i]] <- names(pSum.original[[i]])
object.names.comparison <- c(object.names.comparison, object.names[comparison[i]])
}
if (measure == "weight") {
values.assign <- seq(max(unlist(pSum))*1.1, max(unlist(pSum))*1.5, length.out = length(unlist(idx)))
position <- sort(pSum.original.all, index.return = TRUE)$ix
for (i in 1:length(comparison)) {
if (i == 1) {
pSum[[i]][idx[[i]]] <- values.assign[match(1:length(idx[[i]]), position)]
} else {
pSum[[i]][idx[[i]]] <- values.assign[match(length(unlist(idx[1:i-1]))+1:length(unlist(idx[1:i])), position)]
}
}
}



pair.name.all <- as.character(unique(unlist(pair.name)))
df <- list()
for (i in 1:length(comparison)) {
df[[i]] <- data.frame(name = pair.name.all, contribution = 0, contribution.scaled = 0, group = object.names[comparison[i]], row.names = pair.name.all)
df[[i]][pair.name[[i]],3] <- pSum[[i]]
df[[i]][pair.name[[i]],2] <- pSum.original[[i]]
}
df <- lapply(seq_along(comparison), function(i) {
d <- data.frame(name = pair.name.all, contribution = 0, contribution.scaled = 0, group = object.names[comparison[i]], row.names = pair.name.all)
d[pair.name[[i]], "contribution.scaled"] <- pSum[[i]]
d[pair.name[[i]], "contribution"] <- pSum.original[[i]]
d
})


# contribution.relative <- as.numeric(format(df[[length(comparison)]]$contribution/abs(df[[1]]$contribution), digits=1))
Expand All @@ -1214,23 +1198,13 @@ rankNet <- function(object, slot.name = "netP", measure = c("weight","count"), m
contribution.relative[[i]][is.na(contribution.relative[[i]])] <- 0
}
names(contribution.relative) <- paste0("contribution.relative.", 1:length(contribution.relative))
for (i in 1:length(comparison)) {
for (j in 1:length(contribution.relative)) {
df[[i]][[names(contribution.relative)[j]]] <- contribution.relative[[j]]
}
for (j in seq_along(contribution.relative)) {
df <- lapply(df, function(x) { x[[names(contribution.relative)[j]]] <- contribution.relative[[j]]; x })
}
df[[1]]$contribution.data2 <- df[[length(comparison)]]$contribution
if (length(comparison) == 2) {
idx <- with(df[[1]], order(-contribution.relative.1, contribution, -contribution.data2))
} else if (length(comparison) == 3) {
idx <- with(df[[1]], order(-contribution.relative.1, -contribution.relative.2,contribution, -contribution.data2))
} else if (length(comparison) == 4) {
idx <- with(df[[1]], order(-contribution.relative.1, -contribution.relative.2, -contribution.relative.3, contribution, -contribution.data2))
} else {
idx <- with(df[[1]], order(-contribution.relative.1, -contribution.relative.2, -contribution.relative.3, -contribution.relative.4, contribution, -contribution.data2))
}


order_args <- c(paste0("-contribution.relative.", seq_along(contribution.relative)), "contribution", "-contribution.data2")
order_expr <- paste(order_args, collapse = ", ")
idx <- eval(parse(text = paste0("with(df[[1]], order(", order_expr, "))")))

for (i in 1:length(comparison)) {
df[[i]] <- df[[i]][idx, ]
Expand All @@ -1242,13 +1216,9 @@ rankNet <- function(object, slot.name = "netP", measure = c("weight","count"), m
df$group <- factor(df$group, levels = object.names.comparison)

if (is.null(color.use)) {
color.use = ggPalette(length(comparison))
color.use <- ggPalette(length(levels(df$group)))
}

# https://stackoverflow.com/questions/49448497/coord-flip-changes-ordering-of-bars-within-groups-in-grouped-bar-plot
df$group <- factor(df$group, levels = rev(levels(df$group)))
color.use <- rev(color.use)

# perform statistical analysis
# if (do.stat) {
# pvalues <- c()
Expand All @@ -1272,30 +1242,30 @@ rankNet <- function(object, slot.name = "netP", measure = c("weight","count"), m
# df$pvalues <- pvalues
# }
if (do.stat & length(comparison) == 2) {
for (i in 1:length(pair.name.all)) {
if (nrow(prob.list[[j]]) != nrow(prob.list[[1]])) {
if (paired.test) {
stop("Paired test is not applicable to datasets with different cellular compositions! Please set `do.stat = FALSE` or `paired.test = FALSE`! \n")
}
if (nrow(prob.list[[1]]) != nrow(prob.list[[2]])) {
if (paired.test) {
stop("Paired test is not applicable to datasets with different cellular compositions! Please set `do.stat = FALSE` or `paired.test = FALSE`! \n")
}
}
prob.values <- matrix(0, nrow = nrow(prob.list[[1]]) * nrow(prob.list[[1]]), ncol = length(comparison))
for (j in 1:length(comparison)) {
if (pair.name.all[i] %in% pair.name[[j]]) {
prob.values[, j] <- as.vector(prob.list[[j]][ , , pair.name.all[i]])
for (i in 1:length(pair.name.all)) {
prob.values <- matrix(0, nrow = nrow(prob.list[[1]]) * nrow(prob.list[[1]]), ncol = length(comparison))
for (j in 1:length(comparison)) {
if (pair.name.all[i] %in% pair.name[[j]]) {
prob.values[, j] <- as.vector(prob.list[[j]][ , , pair.name.all[i]])
} else {
prob.values[, j] <- NA
}
}
prob.values <- prob.values[rowSums(prob.values, na.rm = TRUE) != 0, , drop = FALSE]
if (nrow(prob.values) >3 & sum(is.na(prob.values)) == 0) {
pvalues <- wilcox.test(prob.values[ ,1], prob.values[ ,2], paired = paired.test)$p.value
} else {
prob.values[, j] <- NA
pvalues <- 0
}
pvalues[is.na(pvalues)] <- 0
df$pvalues[df$name == pair.name.all[i]] <- pvalues
}
prob.values <- prob.values[rowSums(prob.values, na.rm = TRUE) != 0, , drop = FALSE]
if (nrow(prob.values) >3 & sum(is.na(prob.values)) == 0) {
pvalues <- wilcox.test(prob.values[ ,1], prob.values[ ,2], paired = paired.test)$p.value
} else {
pvalues <- 0
}
pvalues[is.na(pvalues)] <- 0
df$pvalues[df$name == pair.name.all[i]] <- pvalues
}
}


if (length(comparison) == 2) {
Expand All @@ -1309,12 +1279,7 @@ rankNet <- function(object, slot.name = "netP", measure = c("weight","count"), m
colors.text = NULL
}

for (i in 1:length(pair.name.all)) {
df.t <- df[df$name == pair.name.all[i], "contribution"]
if (sum(df.t) == 0) {
df <- df[-which(df$name == pair.name.all[i]), ]
}
}
df <- df[ave(df$contribution, df$name, FUN = sum) != 0, ]

if ((slot.name == "netP") && (!is.null(signaling))) {
df <- subset(df, name %in% signaling)
Expand Down Expand Up @@ -1372,7 +1337,7 @@ rankNet <- function(object, slot.name = "netP", measure = c("weight","count"), m
}

gg <- gg + theme(axis.text=element_text(size=font.size), axis.title.y = element_text(size=font.size))
gg <- gg + scale_fill_manual(name = "", values = color.use)
gg <- gg + scale_fill_manual(name = "", values = setNames(color.use, rev(levels(df$group))))
gg <- gg + guides(fill = guide_legend(reverse = TRUE))
gg <- gg + theme(axis.text.x = element_text(angle = x.angle, hjust=x.hjust),
axis.text.y = element_text(angle = y.angle, hjust=y.hjust))
Expand Down
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