@@ -162,9 +162,9 @@ recodeClusterZ <- function(sce, from, to, altExpName = "featureSubset") {

         stop("Provided 'sce' argument does not have a 'celda_cell_cluster'",

             " column in 'colData(altExp(sce, altExpName))'")

     }

-    new.clusters <- plyr::mapvalues(celdaClusters(sce,

-                                                  altExpName = altExpName),

-                                    from, to)

+    z <- as.integer(celdaClusters(sce, altExpName = altExpName))

+    new.clusters <- plyr::mapvalues(z,

+                                    from = from, to = to)

     new.clusters <- factor(new.clusters, levels =

                              sort(as.numeric(unique(new.clusters))))

@@ -181,8 +181,12 @@ recodeClusterZ <- function(sce, from, to, altExpName = "featureSubset") {

     if (is.null(celdaClusters(celdaMod)$z)) {

         stop("Provided celdaMod argument does not have a z attribute")

     }

-    celdaMod@clusters$z <- plyr::mapvalues(celdaClusters(celdaMod)$z,

-        from, to)

+    z <- as.integer(celdaClusters(celdaMod)$z)

+    new.clusters <- plyr::mapvalues(z, from = from, to = to)

+    new.clusters <- factor(new.clusters, levels =

+                             sort(as.numeric(unique(new.clusters))))

+    

+    celdaMod@clusters$z <- new.clusters

     return(celdaMod)

 }

@@ -215,14 +219,12 @@ recodeClusterY <- function(sce, from, to, altExpName = "featureSubset") {

         stop("Provided 'sce' argument does not have a 'celda_feature_module'",

             " column in 'rowData(altExp(sce, altExpName))'")

     }

-    new.clusters <- plyr::mapvalues(celdaModules(sce,

-                                                  altExpName = altExpName),

-                                  from, to)

+    y <- as.integer(celdaModules(sce, altExpName = altExpName))

+    new.clusters <- plyr::mapvalues(y, from = from, to = to)

     new.clusters <- factor(new.clusters, levels =

                              sort(as.numeric(unique(new.clusters))))

-

-    celdaModules(sce, altExpName = altExpName) <- plyr::mapvalues(

-        celdaModules(sce, altExpName = altExpName), from, to)

+    

+    celdaModules(sce, altExpName = altExpName) <- new.clusters

     return(sce)

 }

@@ -235,8 +237,13 @@ recodeClusterY <- function(sce, from, to, altExpName = "featureSubset") {

     if (is.null(celdaClusters(celdaMod)$y)) {

         stop("Provided celdaMod argument does not have a y attribute")

     }

-    celdaMod@clusters$y <- plyr::mapvalues(celdaClusters(celdaMod)$y,

-        from, to)

+    y <- as.integer(celdaClusters(celdaMod)$y)

+    

+    new.clusters <- plyr::mapvalues(y, from = from, to = to)

+    new.clusters <- factor(new.clusters, levels =

+                             sort(as.numeric(unique(new.clusters))))

+    

+    celdaMod@clusters$y <- new.clusters

     return(celdaMod)

 }

@@ -152,8 +152,8 @@ setMethod("factorizeMatrix", signature(x = "ANY", celdaMod = "celda_CG"),

         counts <- .processCounts(x)

         compareCountMatrix(counts, celdaMod)

-        z <- celdaClusters(celdaMod)$z

-        y <- celdaClusters(celdaMod)$y

+        z <- as.integer(celdaClusters(celdaMod)$z)

+        y <- as.integer(celdaClusters(celdaMod)$y)

         # Sometimes, fewer clusters get returned by celda_C/G

         # Taking the max(z)/max(y) rather than

         # the original K/L will prevent errors

@@ -318,7 +318,7 @@ setMethod("factorizeMatrix", signature(x = "ANY", celdaMod = "celda_C"),

         counts <- .processCounts(x)

         compareCountMatrix(counts, celdaMod)

-        z <- celdaClusters(celdaMod)$z

+        z <- as.integer(celdaClusters(celdaMod)$z)

         # Sometimes, fewer clusters get returned by celda_C

         # Taking the max(z) rather than the

         # original K will prevent errors

@@ -430,7 +430,7 @@ setMethod("factorizeMatrix", signature(x = "ANY", celdaMod = "celda_G"),

         counts <- .processCounts(x)

         compareCountMatrix(counts, celdaMod)

-        y <- celdaClusters(celdaMod)$y

+        y <- as.integer(celdaClusters(celdaMod)$y)

         # Sometimes, fewer clusters get returned by celda_G

         # Taking the max(y) rather than the original

         # L will prevent errors

@@ -85,7 +85,7 @@ setMethod("logLikelihood", signature(x = "SingleCellExperiment"),

 setMethod("logLikelihood", signature(x = "matrix", celdaMod = "celda_C"),

     function(x, celdaMod) {

         sampleLabel <- sampleLabel(celdaMod)

-        z <- celdaClusters(celdaMod)$z

+        z <- as.integer(celdaClusters(celdaMod)$z)

         K <- params(celdaMod)$K

         alpha <- params(celdaMod)$alpha

         beta <- params(celdaMod)$beta

@@ -105,7 +105,7 @@ setMethod("logLikelihood", signature(x = "matrix", celdaMod = "celda_C"),

 #' @export

 setMethod("logLikelihood", signature(x = "matrix", celdaMod = "celda_G"),

     function(x, celdaMod) {

-        y <- celdaClusters(celdaMod)$y

+        y <- as.integer(celdaClusters(celdaMod)$y)

         L <- params(celdaMod)$L

         beta <- params(celdaMod)$beta

         delta <- params(celdaMod)$delta

@@ -127,8 +127,8 @@ setMethod("logLikelihood", signature(x = "matrix", celdaMod = "celda_G"),

 setMethod("logLikelihood", signature(x = "matrix", celdaMod = "celda_CG"),

     function(x, celdaMod) {

         sampleLabel <- sampleLabel(celdaMod)

-        z <- celdaClusters(celdaMod)$z

-        y <- celdaClusters(celdaMod)$y

+        z <- as.integer(celdaClusters(celdaMod)$z)

+        y <- as.integer(celdaClusters(celdaMod)$y)

         K <- params(celdaMod)$K

         L <- params(celdaMod)$L

         alpha <- params(celdaMod)$alpha

@@ -534,7 +534,7 @@ setMethod("recursiveSplitCell",

       verbose = FALSE,

       reorder = reorder)

     currentK <- length(unique(celdaClusters(modelInitial)$z)) + 1

-    overallZ <- celdaClusters(modelInitial)$z

+    overallZ <- as.integer(celdaClusters(modelInitial)$z)

     resList <- list(modelInitial)

     while (currentK <= maxK) {

       # previousY <- overallY

@@ -574,14 +574,14 @@ setMethod("recursiveSplitCell",

       # If the number of clusters is still "currentK", then keep the

       # reordering, otherwise keep the previous configuration

       if (length(unique(celdaClusters(tempModel)$z)) == currentK) {

-        overallZ <- celdaClusters(tempModel)$z

+        overallZ <- as.integer(celdaClusters(tempModel)$z)

       } else {

         overallZ <- tempSplit$z

         ll <- .logLikelihoodcelda_CG(

           counts,

           s,

           overallZ,

-          celdaClusters(tempModel)$y,

+          as.integer(celdaClusters(tempModel)$y),

           currentK,

           L,

           alpha,

@@ -590,7 +590,7 @@ setMethod("recursiveSplitCell",

           gamma

         )

         tempModel <- methods::new("celda_CG",

-          clusters = list(z = overallZ, y = celdaClusters(tempModel)$y),

+          clusters = list(z = overallZ, y = as.integer(celdaClusters(tempModel)$y)),

           params = list(

             K = as.integer(currentK),

             L = as.integer(L),

@@ -670,7 +670,7 @@ setMethod("recursiveSplitCell",

       reorder = reorder

     )

     currentK <- length(unique(celdaClusters(modelInitial)$z)) + 1

-    overallZ <- celdaClusters(modelInitial)$z

+    overallZ <- as.integer(celdaClusters(modelInitial)$z)

     ll <- .logLikelihoodcelda_C(

       counts, s, overallZ, currentK,

       alpha, beta

@@ -707,7 +707,7 @@ setMethod("recursiveSplitCell",

       # Handle rare cases where a population has no cells after running

       # the model

       if (length(unique(celdaClusters(tempModel)$z)) == currentK) {

-        overallZ <- celdaClusters(tempModel)$z

+        overallZ <- as.integer(celdaClusters(tempModel)$z)

       } else {

         overallZ <- tempSplit$z

       }

@@ -772,7 +772,7 @@ setMethod("recursiveSplitCell",

       reorder = reorder

     )

     currentK <- length(unique(celdaClusters(modelInitial)$z)) + 1

-    overallZ <- celdaClusters(modelInitial)$z

+    overallZ <- as.integer(celdaClusters(modelInitial)$z)

     resList <- list(modelInitial)

     while (currentK <= maxK) {

       tempSplit <- .singleSplitZ(counts,

@@ -798,7 +798,7 @@ setMethod("recursiveSplitCell",

       )

       if (length(unique(celdaClusters(tempModel)$z)) == currentK) {

-        overallZ <- celdaClusters(tempModel)$z

+        overallZ <- as.integer(celdaClusters(tempModel)$z)

       } else {

         overallZ <- tempSplit$z

         ll <-

@@ -1336,7 +1336,7 @@ setMethod("recursiveSplitModule",

       verbose = FALSE,

       reorder = reorder)

     currentL <- length(unique(celdaClusters(modelInitial)$y)) + 1

-    overallY <- celdaClusters(modelInitial)$y

+    overallY <- as.integer(celdaClusters(modelInitial)$y)

     resList <- list(modelInitial)

     while (currentL <= maxL) {

@@ -1365,7 +1365,7 @@ setMethod("recursiveSplitModule",

         zInit = overallZ,

         reorder = reorder

       )

-      overallY <- celdaClusters(tempModel)$y

+      overallY <- as.integer(celdaClusters(tempModel)$y)

       ## Add new model to results list and increment L

       .logMessages(

@@ -1428,8 +1428,8 @@ setMethod("recursiveSplitModule",

     )

     modelInitial@params$countChecksum <- countChecksum

-    currentL <- length(unique(celdaClusters(modelInitial)$y)) + 1

-    overallY <- celdaClusters(modelInitial)$y

+    currentL <- length(unique(as.integer(celdaClusters(modelInitial)$y))) + 1

+    overallY <- as.integer(celdaClusters(modelInitial)$y)

     ## Decomposed counts for full count matrix

     p <- .cGDecomposeCounts(counts, overallY, currentL)

@@ -1465,7 +1465,7 @@ setMethod("recursiveSplitModule",

         yInit = tempSplit$y,

         reorder = reorder

       )

-      overallY <- celdaClusters(tempModel)$y

+      overallY <- as.integer(celdaClusters(tempModel)$y)

       # Adjust decomposed count matrices

       p <- .cGReDecomposeCounts(counts,

@@ -1543,7 +1543,7 @@ setMethod("recursiveSplitModule",

       nchains = 1,

       verbose = FALSE

     )

-    overallY <- celdaClusters(modelInitial)$y

+    overallY <- as.integer(celdaClusters(modelInitial)$y)

     currentL <- length(unique(overallY)) + 1

     ## Perform splitting for y labels

@@ -1572,7 +1572,7 @@ setMethod("recursiveSplitModule",

         yInit = tempSplit$y,

         reorder = reorder

       )

-      overallY <- celdaClusters(tempModel)$y

+      overallY <- as.integer(celdaClusters(tempModel)$y)

       ## Add new model to results list and increment L

       .logMessages(

@@ -47,7 +47,7 @@

       verbose = FALSE,

       reorder = FALSE

     )

-    clustSplit[[i]] <- celdaClusters(clustLabel)$z

+    clustSplit[[i]] <- as.integer(celdaClusters(clustLabel)$z)

   }

   ## Find second best assignment give current assignments for each cell

@@ -205,7 +205,7 @@

       verbose = FALSE,

       reorder = FALSE

     )

-    clustSplit[[i]] <- celdaClusters(clustLabel)$z

+    clustSplit[[i]] <- as.integer(celdaClusters(clustLabel)$z)

   }

   ## Find second best assignment give current assignments for each cell

@@ -399,7 +399,7 @@

         verbose = FALSE,

         reorder = FALSE

       )

-      tempZ[ix] <- celdaClusters(clustLabel)$z + currentTopZ

+      tempZ[ix] <- as.integer(celdaClusters(clustLabel)$z) + currentTopZ

     }

     currentTopZ <- max(tempZ, na.rm = TRUE)

   }

@@ -443,7 +443,7 @@

       verbose = FALSE,

       reorder = FALSE

     )

-    clustSplit[[i]] <- celdaClusters(clustLabel)$y

+    clustSplit[[i]] <- as.integer(celdaClusters(clustLabel)$y)

   }

   ## Find second best assignment give current assignments for each cell

@@ -637,7 +637,7 @@

       verbose = FALSE,

       reorder = FALSE

     )

-    clustSplit[[i]] <- celdaClusters(clustLabel)$y

+    clustSplit[[i]] <- as.integer(celdaClusters(clustLabel)$y)

   }

   ## Find second best assignment give current assignments for each cell

@@ -6,6 +6,11 @@

 using namespace Rcpp;

+#ifdef RCPP_USE_GLOBAL_ROSTREAM

+Rcpp::Rostream<true>&  Rcpp::Rcout = Rcpp::Rcpp_cout_get();

+Rcpp::Rostream<false>& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get();

+#endif

+

 // decontXEM

 Rcpp::List decontXEM(const Eigen::MappedSparseMatrix<double>& counts, const NumericVector& counts_colsums, const NumericVector& theta, const bool& estimate_eta, const NumericMatrix& eta, const NumericMatrix& phi, const IntegerVector& z, const bool& estimate_delta, const NumericVector& delta, const double& pseudocount);

 RcppExport SEXP _celda_decontXEM(SEXP countsSEXP, SEXP counts_colsumsSEXP, SEXP thetaSEXP, SEXP estimate_etaSEXP, SEXP etaSEXP, SEXP phiSEXP, SEXP zSEXP, SEXP estimate_deltaSEXP, SEXP deltaSEXP, SEXP pseudocountSEXP) {