diff --git a/DESCRIPTION b/DESCRIPTION
index e38608d..9332d76 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: SingleR
 Title: Reference-Based Single-Cell RNA-Seq Annotation
-Version: 2.7.8
-Date: 2024-10-26
+Version: 2.7.9
+Date: 2024-10-28
 Authors@R: c(person("Dvir", "Aran", email="dvir.aran@ucsf.edu", role=c("aut", "cph")),
     person("Aaron", "Lun", email="infinite.monkeys.with.keyboards@gmail.com", role=c("ctb", "cre")),
     person("Daniel", "Bunis", role="ctb"),
diff --git a/R/plotMarkerHeatmap.R b/R/plotMarkerHeatmap.R
index 4a37c90..a9a6eaa 100644
--- a/R/plotMarkerHeatmap.R
+++ b/R/plotMarkerHeatmap.R
@@ -5,7 +5,7 @@
 #' @param results A \linkS4class{DataFrame} containing the output from \code{\link{SingleR}},
 #' \code{\link{classifySingleR}}, \code{\link{combineCommonResults}}, or \code{\link{combineRecomputedResults}}.
 #' @param test A numeric matrix of log-normalized expression values where rows are genes and columns are cells.
-#' Each row should be named with the gene name.
+#' Each row should be named with the same gene name that was used to compute \code{results}.
 #'
 #' Alternatively, a \linkS4class{SummarizedExperiment} object containing such a matrix.
 #' @param label String specifying the label of interest.
@@ -14,6 +14,9 @@
 #' @param assay.type Integer scalar or string specifying the matrix of expression values to use if \code{test} is a \linkS4class{SummarizedExperiment}.
 #' @param use.pruned Logical scalar indicating whether the pruned labels should be used instead.
 #' @param order.by String specifying the column of the output of \code{\link[scran]{scoreMarkers}} with which to sort for interesting markers.
+#' @param display.row.names Character vector of length equal to the number of rows of \code{test},
+#' containing the names of the features to show on the heatmap (e.g., to replace IDs with symbols).
+#' If \code{NULL}, the existing row names of \code{test} are used.
 #' @param top Integer scalar indicating the top most interesting markers to show in the heatmap.
 #' @param BPPARAM A \linkS4class{BiocParallelParam} object specifying the parallelization scheme to use for marker scoring.
 #'
@@ -48,6 +51,7 @@ plotMarkerHeatmap <- function(
     label,
     other.labels=NULL,
     assay.type="logcounts",
+    display.row.names=NULL,
     use.pruned=FALSE,
     order.by="rank.logFC.cohen",
     top=20,
@@ -92,13 +96,19 @@ plotMarkerHeatmap <- function(
         to.show <- names(abundance)[order(abundance, decreasing=TRUE)]
     }
 
-    to.show <- head(to.show, top)
-    limits <- range(test, na.rm=TRUE)
+    to.show <- match(head(to.show, top), rownames(test))
     colnames(test) <- seq_len(ncol(test))
     col.order <- order(predictions)
+    test <- test[to.show,col.order,drop=FALSE]
+    predictions <- predictions[col.order]
+
+    if (!is.null(display.row.names)) {
+        rownames(test) <- display.row.names[rkeep][to.show]
+    }
 
+    limits <- range(test, na.rm=TRUE)
     pheatmap::pheatmap(
-        test[to.show,col.order,drop=FALSE],
+        test,
         breaks=seq(limits[1], limits[2], length.out=26),
         color=viridis::viridis(25),
         annotation_col=data.frame(labels=predictions, row.names=colnames(test)),
diff --git a/inst/NEWS.Rd b/inst/NEWS.Rd
index a99e3b9..4c7c6bc 100644
--- a/inst/NEWS.Rd
+++ b/inst/NEWS.Rd
@@ -21,6 +21,8 @@ Rather, filtering should be done prior to \code{trainSingleR()}, as is done in t
 
 \item \code{combineRecomputedResults()} now supports fine-tuning to resolve closely-related labels from different references.
 This is similar to the fine-tuning in \code{classifySingleR()} where the feature space is iterately redefined as the union of markers of labels with near-highest scores.
+
+\item Added the \code{plotMarkerHeatmap()} function to plot a diagnostic heatmap of the most interesting markers for each label.
 }}
 
 \section{Version 2.0.0}{\itemize{
diff --git a/man/plotMarkerHeatmap.Rd b/man/plotMarkerHeatmap.Rd
index d020a7a..4603b22 100644
--- a/man/plotMarkerHeatmap.Rd
+++ b/man/plotMarkerHeatmap.Rd
@@ -10,6 +10,7 @@ plotMarkerHeatmap(
   label,
   other.labels = NULL,
   assay.type = "logcounts",
+  display.row.names = NULL,
   use.pruned = FALSE,
   order.by = "rank.logFC.cohen",
   top = 20,
@@ -21,7 +22,7 @@ plotMarkerHeatmap(
 \code{\link{classifySingleR}}, \code{\link{combineCommonResults}}, or \code{\link{combineRecomputedResults}}.}
 
 \item{test}{A numeric matrix of log-normalized expression values where rows are genes and columns are cells.
-Each row should be named with the gene name.
+Each row should be named with the same gene name that was used to compute \code{results}.
 
 Alternatively, a \linkS4class{SummarizedExperiment} object containing such a matrix.}
 
@@ -32,6 +33,10 @@ Defaults to all available labels.}
 
 \item{assay.type}{Integer scalar or string specifying the matrix of expression values to use if \code{test} is a \linkS4class{SummarizedExperiment}.}
 
+\item{display.row.names}{Character vector of length equal to the number of rows of \code{test},
+containing the names of the features to show on the heatmap (e.g., to replace IDs with symbols).
+If \code{NULL}, the existing row names of \code{test} are used.}
+
 \item{use.pruned}{Logical scalar indicating whether the pruned labels should be used instead.}
 
 \item{order.by}{String specifying the column of the output of \code{\link[scran]{scoreMarkers}} with which to sort for interesting markers.}
diff --git a/tests/testthat/test-plotMarkerHeatmap.R b/tests/testthat/test-plotMarkerHeatmap.R
index 64407a9..1e559ab 100644
--- a/tests/testthat/test-plotMarkerHeatmap.R
+++ b/tests/testthat/test-plotMarkerHeatmap.R
@@ -18,6 +18,11 @@ test_that("marker heatmaps work with a subset of other labels", {
     expect_s3_class(plotMarkerHeatmap(pred, test, pred$labels[1], other.labels=unique(pred$labels)[1:2]), "pheatmap")
 })
 
+test_that("marker heatmaps work with other names", {
+    alt.names <- paste0("X_", rownames(test))
+    expect_s3_class(plotMarkerHeatmap(pred, test, pred$labels[1], display.row.names=alt.names), "pheatmap")
+})
+
 test_that("marker heatmap falls back to average abundances", {
     lab <- pred$labels[1]
     keep <- pred$labels == lab