@@ -112,24 +112,25 @@ opts_chunk$set(

 Reduced dimensional 2-D plots created by algorithms such as tSNE and UMAP are useful for visualizing the relationship between cells. Each point on the plot represents a single cell. Cells closer together on the plot have more similar expression profiles across all genes. The tabs below show the `r reducedDimName` dimensions colored by different variables. The *Cluster* tab colors cells by the `r K` subpopulation labels identified by celda_CG, The *Sample Labels* tab colors cells by the sample label supplied to celda_CG. If no sample label was supplied to celda_CG, then all cells will be the same color. The *Cell Annotations* tab contains colors points by other pre-specified cell-level annotations.

 ### Clusters

-```{r celda_clusters, fig.height = 9, fig.width = 9}

+```{r celda_clusters}

 plotDimReduceCluster(sce, reducedDimName = reducedDimName, labelClusters = TRUE)

 ```

 ### Sample Labels

-```{r celda_samples, fig.height = 9, fig.width = 9}

+```{r celda_samples}

 plotSCEDimReduceColData(

   altExp(sce),

   reducedDimName = reducedDimName,

   colorBy = "celda_sample_label",

-  labelClusters = FALSE

+  labelClusters = FALSE,

+  dotSize = 0.5

 )

 ```

 ### Cell Annotations {.tabset .tabset-fade}

-```{r celda_cellAnnot, results = "asis", fig.height = 9, fig.width = 10}

+```{r celda_cellAnnot, results = "asis"}

 if (!is.null(cellAnnotFinal)) {

   for (i in seq_along(cellAnnotFinal)) {

     cat(sprintf(tab4, cellAnnotFinal[i]))

@@ -139,11 +140,11 @@ if (!is.null(cellAnnotFinal)) {

     print(

       plotSCEDimReduceColData(

         altExp(sce),

-        sample = sce$sample,

         colorBy = cellAnnotFinal[i],

         conditionClass = conditionClass,

         reducedDim = reducedDimName,

-        labelClusters = plotLabels[i]

+        labelClusters = plotLabels[i],

+        dotSize = 0.5

       )

     )

     cat(space)

@@ -141,7 +141,7 @@ grid.arrange(p1, p2,  ncol = 2)

 ```

-## Determining the number of cell populaitons (K)

+## Determining the number of cell populations (K)

 The ```recursiveSplitCell``` function fits different celda models for a range of ```K``` values from `r initialK` to `r maxK`. The number of modules is set to ```L``` and the module labels from the ```recursiveSplitModule``` output are used for initialization. Similarly, the first model is fit with ```r paste0("K = ", initialK)```. Then the `celda_C` model is used to split each cell population into two new cell populations and the likelihood is re-calculated. The split that produced the best overall likelihood out of all splits is used for the next model with `K+1` cell populations. Perplexity and RPC are calculated as described in the previous section. The elbow can be used as a good starting point for possible choices of ```K```. Lastly, the final model is selected and the modules and cells are reordered using hierarchical clustering so that more similar modules and cell populations will have more similar values of L and K, respectively. Different choices for ```K``` are also visualized in reduced dimensional plots in the next section.

 ```{r cell_split}

@@ -182,7 +182,7 @@ Reduced dimensional 2-D plots created by algorithms such as tSNE and UMAP are us

 ### tSNE {.tabset .tabset-fade}

 ```{r dimreduce_tsne, results = "asis"}

-sce <- celdaTsne(sce)

+sce <- celdaTsne(sce, useAssay = useAssay, altExpName = altExpName)

 tsne <- reducedDim(altExp(sce, altExpName), "celda_tSNE")

 for (i in seq.int(initialK, maxK)) {

   cat(sprintf(tab4, paste0("K = ", i)))

@@ -202,7 +202,7 @@ for (i in seq.int(initialK, maxK)) {

 ### UMAP {.tabset .tabset-fade}

 ```{r dimreduce_umap, results = "asis"}

-sce <- celdaUmap(sce)

+sce <- celdaUmap(sce, useAssay = useAssay, altExpName = altExpName)

 umap <- reducedDim(altExp(sce, altExpName), "celda_UMAP")

 for (i in seq.int(initialK, maxK)) {

   cat(sprintf(tab4, paste0("K = ", i)))