@@ -281,7 +281,12 @@ for (i in seq.int(1, length(grids) - 1)) {

 This section displays a table of features in each module and can be used as a quick way to lookup features of interest. The features within each module are ordered from those with highest expression at the top to those with lower expression at the bottom (same as in the heatmaps in the previous tab).

 ```{r celda_modules_table}

-table <- featureModuleTable(sce)

+table <- featureModuleTable(

+    sce,

+    useAssay = useAssay,

+    altExpName = altExpName,

+    displayName = displayName

+  )

 kable(table, style = "html", row.names = FALSE) %>%

   kable_styling(bootstrap_options = "striped") %>%

   scroll_box(width = "100%", height = "800px")

@@ -291,6 +296,7 @@ if (!is.null(moduleFileName)) {

     sce,

     useAssay = useAssay,

     altExpName = altExpName,

+    displayName = displayName,

     outputFile = moduleFileName

   )

 }

@@ -316,7 +322,6 @@ if (!is.null(features)) {

         sce,

         reducedDimName = "celda_UMAP",

         features = rownames(sce)[ix],

-        displayName = displayName,

         colorHigh = "red",

         colorMid = "grey",

         colorLow = "blue",

@@ -157,7 +157,7 @@ plotGridSearchPerplexity(moduleSplit, altExpName = altExpName, sep = 10)

 The perplexity alone often does not show a clear elbow or "leveling off". However, the rate of perplexity change (RPC) can be more informative to determine when adding new modules does not add much additional information [Zhao et al., 2015](https://doi.org/10.1186/1471-2105-16-S13-S8){target="_blank"}). An RPC closer to zero indicates that the addition of new modules or cell clusters is not substantially decreasing the perplexity. The RPC of models can be visualized using function `plotRPC`:

 ```{r module_split_rpc, message = FALSE, warning = FALSE}

-plotRPC(moduleSplit, altExpName = altExpName, sep = 10, n = 30)

+plotRPC(moduleSplit, altExpName = altExpName)

 ```

 In this case, we will choose an `L` of 80 as the RPC curve tends to level off at this point:

@@ -182,7 +182,7 @@ The perplexities and RPC of models can be visualized using the same functions `p

 ```{r cell_split_perplexity, warning = FALSE}

 plotGridSearchPerplexity(sce)

-plotRPC(sce)

+plotRPC(sce, , altExpName = altExpName)

 ```

 The perplexity continues to decrease with larger values of `K`. The RPC generally levels off between 13 and 16 and we choose the model with `K = 14` for downstream analysis. The follow code selects the final `celda_CG` model with `L = 80` and `K = 14`:

@@ -410,7 +410,7 @@ All of the parameters in this function are the same that were used throughout th

 The second report takes in as input an SCE object with a fitted `celda_CG` model and systematically generates several plots that facilitate exploratory analysis including cell subpopulation cluster labels on 2-D embeddings, user-specified annotations on 2-D embeddings, module heatmaps, module probabilities, expression of marker genes on 2-D embeddings, and the celda probability map. The report can be generated with the following code:

 ```{r report_results, eval = FALSE}

-reportCeldaCGPlotResults(sce, reducedDimName = "celda_UMAP", features = markers, useAssay = useAssay, altExpName = altExpName, cellAnnot = c("total", "detected", "decontX_contamination", "subsets_mito_percent"), cellAnnotLabel = "scDblFinder_class")

+reportCeldaCGPlotResults(sce, reducedDimName = "celda_UMAP", features = markers, useAssay = useAssay, altExpName = altExpName, cellAnnot = c("total", "detected", "decontX_contamination", "subsets_mito_percent"), cellAnnotLabel = "scDblFinder_doublet_call")

 ```

 User-supplied annotations to plot on the 2-D embedding can be specified through the `cellAnnot` and `cellAnnotLabel` variables. Both parameters will allow for plotting of variables stored in the colData of the SCE on the 2-D embedding plot specified by `reducedDimName` parameter. For `cellAnnot`, integer and numeric variables will be plotted as as continuous variables while factors and characters will be plotted as categorical variables. For `cellAnnotLabel`, all variables will be coerced to a factor and the labels of the categories will be plotted on the scatter plot.