diff --git a/R/mouse_deconvolution_methods.R b/R/mouse_deconvolution_methods.R
index 20bd021..7cf1f95 100644
--- a/R/mouse_deconvolution_methods.R
+++ b/R/mouse_deconvolution_methods.R
@@ -244,47 +244,93 @@ deconvolute_mouse <- function(gene_expression_matrix,
 
 #' This function converts the mouse gene symbols into corresponding human ones.
 #'
-#' This function relies on the `biomaRt`` package.
+#' This function relies on the `biomaRt`` package and connects to the ENSEMBL repository
+#'  to retrieve the gene symbols. If ENSEMBL cannot be reached, another solution will be
+#'  used. Since it is memory intensive, users can choose not to run it.
 #'
 #' @param gene_expression_matrix a m x n matrix with m genes and n samples.
 #'    Gene symbols must be the rownames of the matrix.
 #' @param mirror the ensembl mirror to use. Possible choices are 'www' (default),
 #'    'uswest', 'useast', 'asia'
+#' @param other_annot boolean, wether to run the other conversion method (might be memory intensive)
 #' @return the same matrix, with the counts for the corresponding human genes.
 #'    This matrix can directly be used with the immunedeconv methods. A message
 #'    will display the ratio of original genes which were converted.
 #'
 #' @export
-mouse_genes_to_human <- function(gene_expression_matrix, mirror = "www") {
+mouse_genes_to_human <- function(gene_expression_matrix, mirror = "www", manual_annot) {
   gene.names.mouse <- rownames(gene_expression_matrix)
   gene_expression_matrix$gene_name <- gene.names.mouse
 
-  # human = useMart('ensembl', dataset = 'hsapiens_gene_ensembl')
-  # mouse = useMart('ensembl', dataset = 'mmusculus_gene_ensembl')
   human <- useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl", mirror = mirror)
   mouse <- useEnsembl("ensembl", dataset = "mmusculus_gene_ensembl", mirror = mirror)
-  genes.retrieved <- getLDS(
-    attributes = c("mgi_symbol"), filters = "mgi_symbol", values = gene.names.mouse,
-    mart = mouse, attributesL = c("hgnc_symbol"), martL = human, uniqueRows = T
-  )
-
-
-  newGenes.counts <- gene_expression_matrix %>%
-    left_join(., genes.retrieved, by = c("gene_name" = "MGI.symbol")) %>%
-    select(., -c("gene_name")) %>%
-    select(., c("HGNC.symbol", everything())) %>%
-    .[!(is.na(.$HGNC.symbol)), ]
-
-  colnames(newGenes.counts)[1] <- "gene_name"
-  newGenes.counts <- newGenes.counts[!(duplicated(newGenes.counts$gene_name)), ] %>%
-    as.data.frame(.)
-  rownames(newGenes.counts) <- newGenes.counts$gene_name
-  newGenes.counts <- select(newGenes.counts, -c("gene_name"))
-
-  fraction <- 100 * (nrow(newGenes.counts) / nrow(gene_expression_matrix)) %>%
-    round(., 1)
 
-  message(paste0("ATTENTION: Only the ", fraction, "% of genes was maintained"))
+  genes.retrieved <- NULL
+  tryCatch(
+    expr = {
+      genes.retrieved <<- getLDS(attributes = c("mgi_symbol"),
+                                 filters = "mgi_symbol", values = gene.names.mouse,
+                                 mart = mouse, attributesL = c("hgnc_symbol"), martL = human, uniqueRows = T)
+
+      newGenes.counts <- gene_expression_matrix %>%
+        left_join(., genes.retrieved, by = c("gene_name" = "MGI.symbol")) %>%
+        select(., -c("gene_name")) %>%
+        select(., c("HGNC.symbol", everything())) %>%
+        .[!(is.na(.$HGNC.symbol)), ]
+
+      colnames(newGenes.counts)[1] <- "gene_name"
+      newGenes.counts <- newGenes.counts[!(duplicated(newGenes.counts$gene_name)), ] %>%
+        as.data.frame(.)
+      rownames(newGenes.counts) <- newGenes.counts$gene_name
+      newGenes.counts <- select(newGenes.counts, -c("gene_name"))
+
+      fraction <- 100 * (nrow(newGenes.counts) / nrow(gene_expression_matrix)) %>%
+        round(., 1)
+
+      message(paste0("ATTENTION: Only the ", fraction, "% of genes was maintained"))
+    },
+    error = function(e){
+      print('Cannot connect to ENSEMBL. Using alternative method. This will take some time.')
+
+      if(manual_annot){
+
+        # Code adapted from: https://support.bioconductor.org/p/129636/#9144606
+
+        mouse_human_genes = read.csv("http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt",sep="\t")
+
+        find_corr_gene <- function(gene, mouse_human_genes_df){
+          class_key = (mouse_human_genes_df %>%
+                         filter(Symbol == gene & Common.Organism.Name=="mouse, laboratory"))[['DB.Class.Key']]
+          if(!identical(class_key, integer(0)) ){
+            output <- NULL
+            human_genes = (mouse_human_genes_df %>% filter(DB.Class.Key == class_key & Common.Organism.Name=="human"))[,"Symbol"]
+            for(human_gene in human_genes){
+              output = append(output, human_gene)
+            }
+            if(!is.null(output)){
+              return(
+                data.frame(
+                  'human_gene' = output,
+                  'mouse_gene' = gene))
+            }
+
+          }}
+
+        genes.retrieved <- map_dfr(gene.names.mouse, function(x) find_corr_gene(x, mouse_human_genes))
+
+        newGenes.counts <- gene_expression_matrix %>%
+          left_join(., genes.retrieved, by = c("gene_name" = "mouse_gene")) %>%
+          select(., -c("gene_name")) %>%
+          select(., c("human_gene", everything())) %>%
+          .[!(is.na(.$human_gene)), ]
+
+        fraction <- 100 * (nrow(newGenes.counts) / nrow(gene_expression_matrix)) %>%
+          round(., 1)
+
+        message(paste0("ATTENTION: Only the ", fraction, "% of genes was maintained"))
+
+      }
+    })
 
   return(newGenes.counts)
 }