diff --git a/R/analysis.R b/R/analysis.R
index a455f61..e00f94c 100644
--- a/R/analysis.R
+++ b/R/analysis.R
@@ -643,6 +643,7 @@ computeNetSimilarityPairwise <- function(object, slot.name = "netP", type = c("f
 #' @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 seed An integer value indicating the random seed passed to \code{\link[base]{set.seed}}, use `NULL` to re-initializes seed. Defaults to 42.
 #' @param ... Parameters passing to umap
 #' @importFrom methods slot
 #' @return
@@ -650,7 +651,7 @@ computeNetSimilarityPairwise <- function(object, slot.name = "netP", type = c("f
 #'
 #' @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 = c("umap-learn", "uwot"), n_neighbors = NULL, min_dist = 0.3, seed = 42,...) {
   umap.method <- match.arg(umap.method)
   if (object@options$mode == "single") {
     comparison <- "single"
@@ -674,11 +675,21 @@ netEmbedding <- function(object, slot.name = "netP", type = c("functional","stru
     n_neighbors <- ceiling(sqrt(dim(Similarity)[1])) + 1
   }
   options(warn = -1)
+
+  if (is.numeric(seed)) {
+    seed <- as.integer(seed)
+  }
+
   # dimension reduction
   if (umap.method == "umap-learn") {
-    Y <- runUMAP(Similarity, min_dist = min_dist, n_neighbors = n_neighbors,...)
+    Y <- runUMAP(Similarity, min_dist = min_dist, n_neighbors = n_neighbors, seed.use = seed,...)
   } else if (umap.method == "uwot") {
-    Y <- uwot::umap(Similarity, min_dist = min_dist, n_neighbors = n_neighbors,...)
+    if(packageVersion("uwot")<"0.1.15") {
+      set.seed(seed)
+      Y <- uwot::umap(Similarity, min_dist = min_dist, n_neighbors = n_neighbors,...)
+    } else {
+      Y <- uwot::umap(Similarity, min_dist = min_dist, n_neighbors = n_neighbors, seed = seed,...)
+    }
     colnames(Y) <- paste0('UMAP', 1:ncol(Y))
     rownames(Y) <- colnames(Similarity)
   }
diff --git a/R/utilities.R b/R/utilities.R
index fdc4a59..880a8d7 100644
--- a/R/utilities.R
+++ b/R/utilities.R
@@ -1077,6 +1077,7 @@ runUMAP <- function(
     b = b,
     metric_kwds = metric_kwds,
     angular_rp_forest = angular_rp_forest,
+    random_state = seed.use,
     verbose = verbose
   )
   Rumap <- umap$fit_transform
diff --git a/man/netEmbedding.Rd b/man/netEmbedding.Rd
index ef18dce..310d1f0 100644
--- a/man/netEmbedding.Rd
+++ b/man/netEmbedding.Rd
@@ -13,6 +13,7 @@ netEmbedding(
   umap.method = c("umap-learn", "uwot"),
   n_neighbors = NULL,
   min_dist = 0.3,
+  seed = 42,
   ...
 )
 }
@@ -37,6 +38,8 @@ Can be umap-learn: Run the python umap-learn package; uwot: Runs umap via the uw
 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.}
 
+\item{seed}{An integer value indicating the random seed passed to \code{\link{set.seed}}, use `NULL` to re-initializes seed. Defaults to 42.}
+
 \item{...}{Parameters passing to umap}
 }
 \description{