@@ -547,7 +547,7 @@ setMethod("recursiveSplitCell",

         beta = beta

       )

       tempModel <- .celda_CG(counts,

-        sampleLabel = s,

+        sampleLabel = sampleLabel,

         K = as.integer(currentK),

         L = as.integer(L),

         yInit = overallY,

@@ -660,7 +660,7 @@ setMethod("recursiveSplitCell",

       logfile = logfile

     )

     modelInitial <- .celda_C(countsY,

-      sampleLabel = s,

+      sampleLabel = sampleLabel,

       K = as.integer(initialK),

       zInitialize = "split",

       nchains = 1,

@@ -691,7 +691,7 @@ setMethod("recursiveSplitCell",

         beta = beta

       )

       tempModel <- .celda_C(countsY,

-        sampleLabel = s,

+        sampleLabel = sampleLabel,

         K = as.integer(currentK),

         nchains = 1,

         zInitialize = "random",

@@ -762,7 +762,7 @@ setMethod("recursiveSplitCell",

       logfile = logfile

     )

     modelInitial <- .celda_C(counts,

-      sampleLabel = s,

+      sampleLabel = sampleLabel,

       K = as.integer(initialK),

       zInitialize = "split",

       nchains = 1,

@@ -784,7 +784,7 @@ setMethod("recursiveSplitCell",

         beta = beta

       )

       tempModel <- .celda_C(counts,

-        sampleLabel = s,

+        sampleLabel = sampleLabel,

         K = as.integer(currentK),

         nchains = 1,

         zInitialize = "random",

@@ -135,7 +135,11 @@ if (!is.null(cellAnnotFinal)) {

   for (i in seq_along(cellAnnotFinal)) {

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

-    conditionClass <- ifelse(plotLabels[i], "factor", "numeric")

+    if(isTRUE(plotLabels[i])) {

+      conditionClass <- "factor"  

+    } else {

+      conditionClass <- NULL

+    }

     print(

       plotSCEDimReduceColData(

@@ -339,17 +343,8 @@ The probability matrix on the left contains the probability of each module withi

 print(celdaProbabilityMap(sce))

 ```

-### Overview Heatmap

-This general heatmap shows the actual relative expression of the

-top 10 features in each module (rows) for each cell (columns). Rows are z-score

-normalized. The columns of the heatmap are semi-supervised by cell population and the rows are semi-supervised by module. 

-```{r celda_heatmap, fig.height = 15, fig.width = 9}

-grid.draw(celdaHeatmap(sce, nfeatures = 10))

-```

-

-## Session Information

+## Session Information {.unnumbered}

 ```{r session, eval = showSession, echo = showSession}

 sessionInfo()

 ```

-<br><br>