@@ -55,7 +55,7 @@

 #' @importFrom doParallel registerDoParallel

 #' @importFrom methods is

 #' @examples

-#' \donttest{

+#' \dontrun{

 #' data(celdaCGSim)

 #' ## Run various combinations of parameters with 'celdaGridSearch'

 #' celdaCGGridSearchRes <- celdaGridSearch(celdaCGSim$counts,

@@ -55,7 +55,7 @@

 #' @importFrom doParallel registerDoParallel

 #' @importFrom methods is

 #' @examples

-#' \dontrun{

+#' \donttest{

 #' data(celdaCGSim)

 #' ## Run various combinations of parameters with 'celdaGridSearch'

 #' celdaCGGridSearchRes <- celdaGridSearch(celdaCGSim$counts,

@@ -46,7 +46,6 @@

 #' @return A \linkS4class{SingleCellExperiment} object. Function

 #'  parameter settings and celda model results are stored in the

 #'  \link{metadata} \code{"celda_grid_search"} slot.

-#' @param ... Ignored. Placeholder to prevent check warning.

 #' @seealso \link{celda_G} for feature clustering, \link{celda_C} for

 #'  clustering of cells, and \link{celda_CG} for simultaneous clustering of

 #'  features and cells. \link{subsetCeldaList} can subset the \code{celdaList}

@@ -68,7 +67,22 @@

 #'   cores = 1)

 #' }

 #' @export

-setGeneric("celdaGridSearch", function(x, ...) {

+setGeneric("celdaGridSearch",

+    function(

+        x,

+        useAssay = "counts",

+        altExpName = "featureSubset",

+        model,

+        paramsTest,

+        paramsFixed = NULL,

+        maxIter = 200,

+        nchains = 3,

+        cores = 1,

+        bestOnly = TRUE,

+        seed = 12345,

+        perplexity = TRUE,

+        verbose = TRUE,

+        logfilePrefix = "Celda") {

     standardGeneric("celdaGridSearch")})

@@ -442,7 +456,6 @@ setMethod("celdaGridSearch",

 #'  models in list \code{"celda_grid_search"} in \code{metadata(x)}.

 #' @param altExpName The name for the \link{altExp} slot

 #'  to use. Default "featureSubset".

-#' @param ... Ignored. Placeholder to prevent check warning.

 #' @return One of

 #' \itemize{

 #'  \item A new \linkS4class{SingleCellExperiment} object containing

@@ -464,7 +477,9 @@ setMethod("celdaGridSearch",

 #'  chains in parallel. \link{selectBestModel} can get the best model for each

 #'  combination of parameters.

 #' @export

-setGeneric("subsetCeldaList", function(x, ...) {

+setGeneric("subsetCeldaList",

+    function(x, params, altExpName = "featureSubset") {

+

     standardGeneric("subsetCeldaList")})

@@ -583,7 +598,6 @@ setMethod("subsetCeldaList",

 #'  corresponding celda model object.

 #' @param altExpName The name for the \link{altExp} slot

 #'  to use. Default "featureSubset".

-#' @param ... Ignored. Placeholder to prevent check warning.

 #' @return One of

 #' \itemize{

 #'  \item A new \linkS4class{SingleCellExperiment} object containing

@@ -602,7 +616,9 @@ setMethod("subsetCeldaList",

 #'  instead of a \code{celdaList} object.}

 #' @seealso \link{celdaGridSearch} \link{subsetCeldaList}

 #' @export

-setGeneric("selectBestModel", function(x, ...) {

+setGeneric("selectBestModel",

+    function(x, asList = FALSE, altExpName = "featureSubset") {

+

     standardGeneric("selectBestModel")})

@@ -622,6 +622,7 @@ setMethod("selectBestModel", signature(x = "SingleCellExperiment"),

             c("index", "chain", "logLikelihood", "mean_perplexity", "seed"))

         runParams <- S4Vectors::metadata(altExp)$celda_grid_search@runParams

         dt <- data.table::as.data.table(runParams)

+        .SD <- NULL # fix check note

         newRunParams <- as.data.frame(dt[, .SD[which.max(logLikelihood)],

             by = group])

         newRunParams <- newRunParams[, colnames(runParams)]

@@ -654,6 +655,7 @@ setMethod("selectBestModel", signature(x = "celdaList"),

         group <- setdiff(colnames(runParams(x)),

             c("index", "chain", "logLikelihood", "mean_perplexity", "seed"))

         dt <- data.table::as.data.table(runParams(x))

+        .SD <- NULL # fix check note

         newRunParams <- as.data.frame(dt[, .SD[which.max(logLikelihood)],

             by = group])

         newRunParams <- newRunParams[, colnames(runParams(x))]

@@ -12,8 +12,8 @@

 #'  with the matrix located in the assay slot under \code{useAssay}.

 #'  Rows represent features and columns represent cells.

 #' @param useAssay A string specifying the name of the

-#'  \link[SummarizedExperiment]{assay} slot to use. Default "counts".

-#' @param altExpName The name for the \link[SingleCellExperiment]{altExp} slot

+#'  \link{assay} slot to use. Default "counts".

+#' @param altExpName The name for the \link{altExp} slot

 #'  to use. Default "featureSubset".

 #' @param model Celda model. Options available in \link{availableModels}.

 #' @param paramsTest List. A list denoting the combinations of parameters to

@@ -45,7 +45,7 @@

 #'  and main process. Default "Celda".

 #' @return A \linkS4class{SingleCellExperiment} object. Function

 #'  parameter settings and celda model results are stored in the

-#'  \link[S4Vectors]{metadata} \code{"celda_grid_search"} slot.

+#'  \link{metadata} \code{"celda_grid_search"} slot.

 #' @param ... Ignored. Placeholder to prevent check warning.

 #' @seealso \link{celda_G} for feature clustering, \link{celda_C} for

 #'  clustering of cells, and \link{celda_CG} for simultaneous clustering of

@@ -440,7 +440,7 @@ setMethod("celdaGridSearch",

 #'  \item celdaList object.}

 #' @param params List. List of parameters used to subset the matching celda

 #'  models in list \code{"celda_grid_search"} in \code{metadata(x)}.

-#' @param altExpName The name for the \link[SingleCellExperiment]{altExp} slot

+#' @param altExpName The name for the \link{altExp} slot

 #'  to use. Default "featureSubset".

 #' @param ... Ignored. Placeholder to prevent check warning.

 #' @return One of

@@ -451,10 +451,10 @@ setMethod("celdaGridSearch",

 #'  \code{"celda_grid_search"} slot in \code{metadata(x)} matches

 #'  the given criteria, a new \linkS4class{SingleCellExperiment} object

 #'  with the matching model stored in the

-#'  \link[S4Vectors]{metadata}

+#'  \link{metadata}

 #'  \code{"celda_parameters"} slot will be returned. Otherwise, a new

 #'  \linkS4class{SingleCellExperiment} object with the subset models stored

-#'  in the \link[S4Vectors]{metadata}

+#'  in the \link{metadata}

 #'  \code{"celda_grid_search"} slot will be returned.

 #'  \item A new \code{celdaList} object containing all models matching the

 #'  provided criteria in \code{params}. If only one item in the

@@ -581,7 +581,7 @@ setMethod("subsetCeldaList",

 #'  best model as a

 #'  \code{celdaList} object or not. If \code{FALSE}, return the best model as a

 #'  corresponding celda model object.

-#' @param altExpName The name for the \link[SingleCellExperiment]{altExp} slot

+#' @param altExpName The name for the \link{altExp} slot

 #'  to use. Default "featureSubset".

 #' @param ... Ignored. Placeholder to prevent check warning.

 #' @return One of

@@ -591,10 +591,10 @@ setMethod("subsetCeldaList",

 #'  \code{metadata(x)}. If there is only one set of parameters,

 #'  a new \linkS4class{SingleCellExperiment} object

 #'  with the matching model stored in the

-#'  \link[S4Vectors]{metadata}

+#'  \link{metadata}

 #'  \code{"celda_parameters"} slot will be returned. Otherwise, a new

 #'  \linkS4class{SingleCellExperiment} object with the subset models stored

-#'  in the \link[S4Vectors]{metadata}

+#'  in the \link{metadata}

 #'  \code{"celda_grid_search"} slot will be returned.

 #'  \item A new \code{celdaList} object containing one model with the best

 #'  log-likelihood for each set of parameters. If only one set of parameters

@@ -440,9 +440,6 @@ setMethod("celdaGridSearch",

 #'  \item celdaList object.}

 #' @param params List. List of parameters used to subset the matching celda

 #'  models in list \code{"celda_grid_search"} in \code{metadata(x)}.

-#' @param useAssay A string specifying which \code{assay}

-#'  slot to use if \code{x} is a

-#'  \linkS4class{SingleCellExperiment} object. Default "counts".

 #' @param altExpName The name for the \link[SingleCellExperiment]{altExp} slot

 #'  to use. Default "featureSubset".

 #' @param ... Ignored. Placeholder to prevent check warning.

@@ -479,7 +476,7 @@ setGeneric("subsetCeldaList", function(x, ...) {

 #' @export

 setMethod("subsetCeldaList",

     signature(x = "SingleCellExperiment"),

-    function(x, params, useAssay = "counts", altExpName = "featureSubset") {

+    function(x, params, altExpName = "featureSubset") {

         ## Check for bad parameter names

         if (!all(names(params) %in% colnames(runParams(x,

@@ -584,9 +581,6 @@ setMethod("subsetCeldaList",

 #'  best model as a

 #'  \code{celdaList} object or not. If \code{FALSE}, return the best model as a

 #'  corresponding celda model object.

-#' @param useAssay A string specifying which \code{assay}

-#'  slot to use if \code{x} is a

-#'  \linkS4class{SingleCellExperiment} object. Default "counts".

 #' @param altExpName The name for the \link[SingleCellExperiment]{altExp} slot

 #'  to use. Default "featureSubset".

 #' @param ... Ignored. Placeholder to prevent check warning.

@@ -620,8 +614,7 @@ setGeneric("selectBestModel", function(x, ...) {

 #' @importFrom data.table as.data.table

 #' @export

 setMethod("selectBestModel", signature(x = "SingleCellExperiment"),

-    function(x, asList = FALSE, useAssay = "counts",

-        altExpName = "featureSubset") {

+    function(x, asList = FALSE, altExpName = "featureSubset") {

         altExp <- SingleCellExperiment::altExp(x, altExpName)

         logLikelihood <- NULL

@@ -46,6 +46,7 @@

 #' @return A \linkS4class{SingleCellExperiment} object. Function

 #'  parameter settings and celda model results are stored in the

 #'  \link[S4Vectors]{metadata} \code{"celda_grid_search"} slot.

+#' @param ... Ignored. Placeholder to prevent check warning.

 #' @seealso \link{celda_G} for feature clustering, \link{celda_C} for

 #'  clustering of cells, and \link{celda_CG} for simultaneous clustering of

 #'  features and cells. \link{subsetCeldaList} can subset the \code{celdaList}

@@ -444,6 +445,7 @@ setMethod("celdaGridSearch",

 #'  \linkS4class{SingleCellExperiment} object. Default "counts".

 #' @param altExpName The name for the \link[SingleCellExperiment]{altExp} slot

 #'  to use. Default "featureSubset".

+#' @param ... Ignored. Placeholder to prevent check warning.

 #' @return One of

 #' \itemize{

 #'  \item A new \linkS4class{SingleCellExperiment} object containing

@@ -587,6 +589,7 @@ setMethod("subsetCeldaList",

 #'  \linkS4class{SingleCellExperiment} object. Default "counts".

 #' @param altExpName The name for the \link[SingleCellExperiment]{altExp} slot

 #'  to use. Default "featureSubset".

+#' @param ... Ignored. Placeholder to prevent check warning.

 #' @return One of

 #' \itemize{

 #'  \item A new \linkS4class{SingleCellExperiment} object containing

@@ -505,7 +505,8 @@ setMethod("subsetCeldaList",

         }

         ## Get index of selected models, subset celdaList, and return

-        ix <- match(newRunParams$index, runParams(x, altExpName = altExpName)$index)

+        ix <- match(newRunParams$index, runParams(x,

+            altExpName = altExpName)$index)

         altExp <- SingleCellExperiment::altExp(x, altExpName)

         if (length(ix) == 1) {

@@ -480,29 +480,32 @@ setMethod("subsetCeldaList",

     function(x, params, useAssay = "counts", altExpName = "featureSubset") {

         ## Check for bad parameter names

-        if (!all(names(params) %in% colnames(runParams(x)))) {

-            badParams <- setdiff(names(params), colnames(runParams(x)))

+        if (!all(names(params) %in% colnames(runParams(x,

+            altExpName = altExpName)))) {

+            badParams <- setdiff(names(params),

+                colnames(runParams(x, altExpName = altExpName)))

             stop("The following elements in 'params' are not columns in",

-                " runParams(x) ",

+                " runParams(x, altExpName = altExpName) ",

                 paste(badParams, collapse = ",")

             )

         }

         ## Subset 'runParams' based on items in 'params'

-        newRunParams <- runParams(x)

+        newRunParams <- runParams(x, altExpName = altExpName)

         for (i in names(params)) {

             newRunParams <-

                 subset(newRunParams, newRunParams[, i] %in% params[[i]])

             if (nrow(newRunParams) == 0) {

                 stop("No runs matched the criteria given in 'params'. Check",

-                    " 'runParams(x)' for complete list of parameters used",

+                    " 'runParams(x, altExpName = altExpName)' for complete",

+                    " list of parameters used",

                     " to generate 'x'.")

             }

         }

         ## Get index of selected models, subset celdaList, and return

-        ix <- match(newRunParams$index, runParams(x)$index)

+        ix <- match(newRunParams$index, runParams(x, altExpName = altExpName)$index)

         altExp <- SingleCellExperiment::altExp(x, altExpName)

         if (length(ix) == 1) {

@@ -618,7 +621,7 @@ setMethod("selectBestModel", signature(x = "SingleCellExperiment"),

         altExp <- SingleCellExperiment::altExp(x, altExpName)

         logLikelihood <- NULL

-        group <- setdiff(colnames(runParams(x)),

+        group <- setdiff(colnames(runParams(x, altExpName = altExpName)),

             c("index", "chain", "logLikelihood", "mean_perplexity", "seed"))

         runParams <- S4Vectors::metadata(altExp)$celda_grid_search@runParams

         dt <- data.table::as.data.table(runParams)

@@ -134,7 +134,7 @@ setMethod("celdaGridSearch",

             verbose = verbose,

             logfilePrefix = logfilePrefix)

         SingleCellExperiment::altExp(x, altExpName) <- altExp

-        return(sce)

+        return(x)

     })

@@ -6,14 +6,15 @@

 #'  Fixed parameters to be used in all models, such as \code{sampleLabel}, can

 #'  be passed as a list to the argument \code{paramsFixed}. When

 #'  \code{verbose = TRUE}, output from each chain will be sent to a log file

-#'  but not be displayed in stdout.

+#'  but not be displayed in \code{stdout}.

 #' @param x A numeric \link{matrix} of counts or a

 #'  \linkS4class{SingleCellExperiment}

 #'  with the matrix located in the assay slot under \code{useAssay}.

 #'  Rows represent features and columns represent cells.

-#' @param useAssay A string specifying which \link[SummarizedExperiment]{assay}

-#'  slot to use if \code{x} is a

-#'  \link[SingleCellExperiment]{SingleCellExperiment} object. Default "counts".

+#' @param useAssay A string specifying the name of the

+#'  \link[SummarizedExperiment]{assay} slot to use. Default "counts".

+#' @param altExpName The name for the \link[SingleCellExperiment]{altExp} slot

+#'  to use. Default "featureSubset".

 #' @param model Celda model. Options available in \link{availableModels}.

 #' @param paramsTest List. A list denoting the combinations of parameters to

 #'  run in a celda model. For example,

@@ -76,6 +77,7 @@ setMethod("celdaGridSearch",

     signature(x = "SingleCellExperiment"),

     function(x,

         useAssay = "counts",

+        altExpName = "featureSubset",

         model,

         paramsTest,

         paramsFixed = NULL,

@@ -89,7 +91,19 @@ setMethod("celdaGridSearch",

         logfilePrefix = "Celda") {

         xClass <- "SingleCellExperiment"

-        counts <- SummarizedExperiment::assay(x, i = useAssay)

+

+        if (!altExpName %in% SingleCellExperiment::altExpNames(x)) {

+            stop(altExpName, " not in 'altExpNames(x)'. Run ",

+                "selectFeatures(x) first!")

+        }

+

+        altExp <- SingleCellExperiment::altExp(x, altExpName)

+

+        if (!useAssay %in% SummarizedExperiment::assayNames(altExp)) {

+            stop(useAssay, " not in assayNames(altExp(x, altExpName))")

+        }

+

+        counts <- SummarizedExperiment::assay(altExp, i = useAssay)

         celdaList <- .celdaGridSearch(counts = counts,

             model = paste0(".", model),

@@ -104,8 +118,8 @@ setMethod("celdaGridSearch",

             verbose = verbose,

             logfilePrefix = logfilePrefix)

-        sce <- .createSCEceldaGridSearch(celdaList = celdaList,

-            sce = x,

+        altExp <- .createSCEceldaGridSearch(celdaList = celdaList,

+            sce = altExp,

             xClass = xClass,

             useAssay = useAssay,

             model = model,

@@ -119,6 +133,7 @@ setMethod("celdaGridSearch",

             perplexity = perplexity,

             verbose = verbose,

             logfilePrefix = logfilePrefix)

+        SingleCellExperiment::altExp(x, altExpName) <- altExp

         return(sce)

     })

@@ -128,6 +143,8 @@ setMethod("celdaGridSearch",

 setMethod("celdaGridSearch",

     signature(x = "matrix"),

     function(x,

+        useAssay = "counts",

+        altExpName = "featureSubset",

         model,

         paramsTest,

         paramsFixed = NULL,

@@ -140,10 +157,12 @@ setMethod("celdaGridSearch",

         verbose = TRUE,

         logfilePrefix = "Celda") {

+        ls <- list()

+        ls[[useAssay]] <- x

+        sce <- SingleCellExperiment::SingleCellExperiment(assays = ls)

+        SingleCellExperiment::altExp(sce, altExpName) <- sce

         xClass <- "matrix"

-        useAssay <- NULL

-        sce <- SingleCellExperiment::SingleCellExperiment(

-            assays = list(counts = x))

+

         celdaList <- .celdaGridSearch(counts = x,

             model = paste0(".", model),

             paramsTest = paramsTest,

@@ -157,8 +176,8 @@ setMethod("celdaGridSearch",

             verbose = verbose,

             logfilePrefix = logfilePrefix)

-        sce <- .createSCEceldaGridSearch(celdaList = celdaList,

-            sce = sce,

+        altExp <- .createSCEceldaGridSearch(celdaList = celdaList,

+            sce = SingleCellExperiment::altExp(sce, altExpName),

             xClass = xClass,

             useAssay = useAssay,

             model = model,

@@ -172,6 +191,7 @@ setMethod("celdaGridSearch",

             perplexity = perplexity,

             verbose = verbose,

             logfilePrefix = logfilePrefix)

+        SingleCellExperiment::altExp(sce, altExpName) <- altExp

         return(sce)

     })

@@ -422,6 +442,8 @@ setMethod("celdaGridSearch",

 #' @param useAssay A string specifying which \code{assay}

 #'  slot to use if \code{x} is a

 #'  \linkS4class{SingleCellExperiment} object. Default "counts".

+#' @param altExpName The name for the \link[SingleCellExperiment]{altExp} slot

+#'  to use. Default "featureSubset".

 #' @return One of

 #' \itemize{

 #'  \item A new \linkS4class{SingleCellExperiment} object containing

@@ -455,13 +477,13 @@ setGeneric("subsetCeldaList", function(x, ...) {

 #' @export

 setMethod("subsetCeldaList",

     signature(x = "SingleCellExperiment"),

-    function(x, params, useAssay = "counts") {

+    function(x, params, useAssay = "counts", altExpName = "featureSubset") {

         ## Check for bad parameter names

         if (!all(names(params) %in% colnames(runParams(x)))) {

             badParams <- setdiff(names(params), colnames(runParams(x)))

             stop("The following elements in 'params' are not columns in",

-                " runParams (x) ",

+                " runParams(x) ",

                 paste(badParams, collapse = ",")

             )

         }

@@ -481,13 +503,17 @@ setMethod("subsetCeldaList",

         ## Get index of selected models, subset celdaList, and return

         ix <- match(newRunParams$index, runParams(x)$index)

+        altExp <- SingleCellExperiment::altExp(x, altExpName)

+

         if (length(ix) == 1) {

-            x <- .subsetCeldaListSCE(x, ix)

+            altExp <- .subsetCeldaListSCE(altExp, ix)

         } else {

-            x@metadata$celda_grid_search@runParams <-

+            altExp@metadata$celda_grid_search@runParams <-

                 as.data.frame(newRunParams)

-            x@metadata$celda_grid_search@resList <- resList(x)[ix]

+            altExp@metadata$celda_grid_search@resList <-

+                altExp@metadata$celda_grid_search@resList[ix]

         }

+        SingleCellExperiment::altExp(x, altExpName) <- altExp

         return(x)

     }

 )

@@ -555,6 +581,8 @@ setMethod("subsetCeldaList",

 #' @param useAssay A string specifying which \code{assay}

 #'  slot to use if \code{x} is a

 #'  \linkS4class{SingleCellExperiment} object. Default "counts".

+#' @param altExpName The name for the \link[SingleCellExperiment]{altExp} slot

+#'  to use. Default "featureSubset".

 #' @return One of

 #' \itemize{

 #'  \item A new \linkS4class{SingleCellExperiment} object containing

@@ -585,11 +613,14 @@ setGeneric("selectBestModel", function(x, ...) {

 #' @importFrom data.table as.data.table

 #' @export

 setMethod("selectBestModel", signature(x = "SingleCellExperiment"),

-    function(x, asList = FALSE, useAssay = "counts") {

+    function(x, asList = FALSE, useAssay = "counts",

+        altExpName = "featureSubset") {

+

+        altExp <- SingleCellExperiment::altExp(x, altExpName)

         logLikelihood <- NULL

         group <- setdiff(colnames(runParams(x)),

             c("index", "chain", "logLikelihood", "mean_perplexity", "seed"))

-        runParams <- S4Vectors::metadata(x)$celda_grid_search@runParams

+        runParams <- S4Vectors::metadata(altExp)$celda_grid_search@runParams

         dt <- data.table::as.data.table(runParams)

         newRunParams <- as.data.frame(dt[, .SD[which.max(logLikelihood)],

             by = group])

@@ -597,12 +628,14 @@ setMethod("selectBestModel", signature(x = "SingleCellExperiment"),

         ix <- match(newRunParams$index, runParams$index)

         if (nrow(newRunParams) == 1 & !asList) {

-            x <- .subsetCeldaListSCE(x, ix)

+            altExp <- .subsetCeldaListSCE(altExp, ix)

         } else {

-            x@metadata$celda_grid_search@runParams <-

+            altExp@metadata$celda_grid_search@runParams <-

                 as.data.frame(newRunParams)

-            x@metadata$celda_grid_search@resList <- resList(x)[ix]

+            altExp@metadata$celda_grid_search@resList <-

+                altExp@metadata$celda_grid_search@resList[ix]

         }

+        SingleCellExperiment::altExp(x, altExpName) <- altExp

         return(x)

     }

 )

@@ -676,7 +709,8 @@ setMethod("selectBestModel", signature(x = "celdaList"),

 .subsetCeldaListSCE <- function(x, ix) {

     cgsparam <- x@metadata$celda_grid_search@celdaGridSearchParameters

     if (cgsparam$model == "celda_C") {

-        x <- .createSCEceldaC(celdaCMod = resList(x)[[ix]],

+        x <- .createSCEceldaC(celdaCMod =

+                x@metadata$celda_grid_search@resList[[ix]],

             sce = x,

             xClass = cgsparam$xClass,

             useAssay = cgsparam$useAssay,

@@ -691,7 +725,8 @@ setMethod("selectBestModel", signature(x = "celdaList"),

             logfile = cgsparam$logfile,

             verbose = cgsparam$verbose)

     } else if (cgsparam$model == "celda_G") {

-        x <- .createSCEceldaG(celdaGMod = resList(x)[[ix]],

+        x <- .createSCEceldaG(celdaGMod =

+                x@metadata$celda_grid_search@resList[[ix]],

             sce = x,

             xClass = cgsparam$xClass,

             useAssay = cgsparam$useAssay,

@@ -705,7 +740,8 @@ setMethod("selectBestModel", signature(x = "celdaList"),

             logfile = cgsparam$logfile,

             verbose = cgsparam$verbose)

     } else if (cgsparam$model == "celda_CG") {

-        x <- .createSCEceldaCG(celdaCGMod = resList(x)[[ix]],

+        x <- .createSCEceldaCG(celdaCGMod =

+                x@metadata$celda_grid_search@resList[[ix]],

             sce = x,

             xClass = cgsparam$xClass,

             useAssay = cgsparam$useAssay,

@@ -722,7 +758,7 @@ setMethod("selectBestModel", signature(x = "celdaList"),

             logfile = cgsparam$logfile,

             verbose = cgsparam$verbose)

     } else {

-        stop("S4Vectors::metadata(X)$celda_grid_search@",

+        stop("S4Vectors::metadata(altExp(x, altExpName))$celda_grid_search@",

             "celdaGridSearchParameters$model must be",

             " one of 'celda_C', 'celda_G', or 'celda_CG'")

     }

@@ -673,7 +673,7 @@ setMethod("selectBestModel", signature(x = "celdaList"),

 }

-.subsetCeldaListSCE <- function (x, ix) {

+.subsetCeldaListSCE <- function(x, ix) {

     cgsparam <- x@metadata$celda_grid_search@celdaGridSearchParameters

     if (cgsparam$model == "celda_C") {

         x <- .createSCEceldaC(celdaCMod = resList(x)[[ix]],

@@ -686,10 +686,10 @@ setMethod("selectBestModel", signature(x = "celdaList"),

             splitOnIter = cgsparam$splitOnIter,

             splitOnLast = cgsparam$splitOnLast,

             nchains = cgsparam$nchains,

-            zInitialize = cgsparamp[["zInitialize"]],

-            zInit = cgsparamp[["zInit"]],

-            logfile = cgsparamp$logfile,

-            verbose = cgsparamp$verbose)

+            zInitialize = cgsparam[["zInitialize"]],

+            zInit = cgsparam[["zInit"]],

+            logfile = cgsparam$logfile,

+            verbose = cgsparam$verbose)

     } else if (cgsparam$model == "celda_G") {

         x <- .createSCEceldaG(celdaGMod = resList(x)[[ix]],

             sce = x,

@@ -675,7 +675,7 @@ setMethod("selectBestModel", signature(x = "celdaList"),

 .subsetCeldaListSCE <- function (x, ix) {

     cgsparam <- x@metadata$celda_grid_search@celdaGridSearchParameters

-    if (cgsparam$model == "celda_c") {

+    if (cgsparam$model == "celda_C") {

         x <- .createSCEceldaC(celdaCMod = resList(x)[[ix]],

             sce = x,

             xClass = cgsparam$xClass,

@@ -413,14 +413,15 @@ setMethod("celdaGridSearch",

 #' @param x Can be one of

 #' \itemize{

 #'  \item A \linkS4class{SingleCellExperiment} object returned from

-#'  \code{celdaGridSearch}. Must contain a list named

+#'  \code{celdaGridSearch}, \code{recursiveSplitModule},

+#'  or \code{recursiveSplitCell}. Must contain a list named

 #'  \code{"celda_grid_search"} in \code{metadata(x)}.

 #'  \item celdaList object.}

 #' @param params List. List of parameters used to subset the matching celda

 #'  models in list \code{"celda_grid_search"} in \code{metadata(x)}.

 #' @param useAssay A string specifying which \code{assay}

 #'  slot to use if \code{x} is a

-#'  \link[SingleCellExperiment]{SingleCellExperiment} object. Default "counts".

+#'  \linkS4class{SingleCellExperiment} object. Default "counts".

 #' @return One of

 #' \itemize{

 #'  \item A new \linkS4class{SingleCellExperiment} object containing

@@ -472,19 +473,16 @@ setMethod("subsetCeldaList",

                 subset(newRunParams, newRunParams[, i] %in% params[[i]])

             if (nrow(newRunParams) == 0) {

-                stop(

-                    "No runs matched the criteria given in 'params'. Check",

+                stop("No runs matched the criteria given in 'params'. Check",

                     " 'runParams(x)' for complete list of parameters used",

-                    " to generate 'x'."

-                )

+                    " to generate 'x'.")

             }

         }

         ## Get index of selected models, subset celdaList, and return

         ix <- match(newRunParams$index, runParams(x)$index)

         if (length(ix) == 1) {

-            x <- celdatosce(resList(x)[[ix]],

-                SummarizedExperiment::assay(x, i = useAssay))

+            x <- .subsetCeldaListSCE(x, ix)

         } else {

             x@metadata$celda_grid_search@runParams <-

                 as.data.frame(newRunParams)

@@ -543,9 +541,13 @@ setMethod("subsetCeldaList",

 #' @description Select the chain with the best log likelihood for each

 #'  combination of tested parameters from a \code{SCE} object gererated by

 #'  \link{celdaGridSearch} or from a \code{celdaList} object.

-#' @param x Object of class \linkS4class{SingleCellExperiment} or

-#'  \code{celdaList}. An object containing celda

-#'  models returned from \link{celdaGridSearch}.

+#' @param x Can be one of

+#' \itemize{

+#'  \item A \linkS4class{SingleCellExperiment} object returned from

+#'  \code{celdaGridSearch}, \code{recursiveSplitModule},

+#'  or \code{recursiveSplitCell}. Must contain a list named

+#'  \code{"celda_grid_search"} in \code{metadata(x)}.

+#'  \item celdaList object.}

 #' @param asList \code{TRUE} or \code{FALSE}. Whether to return the

 #'  best model as a

 #'  \code{celdaList} object or not. If \code{FALSE}, return the best model as a

@@ -595,8 +597,7 @@ setMethod("selectBestModel", signature(x = "SingleCellExperiment"),

         ix <- match(newRunParams$index, runParams$index)

         if (nrow(newRunParams) == 1 & !asList) {

-            x <- celdatosce(resList(x)[[ix]],

-                SummarizedExperiment::assay(x, i = useAssay))

+            x <- .subsetCeldaListSCE(x, ix)

         } else {

             x@metadata$celda_grid_search@runParams <-

                 as.data.frame(newRunParams)

@@ -670,3 +671,60 @@ setMethod("selectBestModel", signature(x = "celdaList"),

             logfilePrefix = logfilePrefix)

     return(sce)

 }

+

+

+.subsetCeldaListSCE <- function (x, ix) {

+    cgsparam <- x@metadata$celda_grid_search@celdaGridSearchParameters

+    if (cgsparam$model == "celda_c") {

+        x <- .createSCEceldaC(celdaCMod = resList(x)[[ix]],

+            sce = x,

+            xClass = cgsparam$xClass,

+            useAssay = cgsparam$useAssay,

+            algorithm = cgsparam$algorithm,

+            stopIter = cgsparam$stopIter,

+            maxIter = cgsparam$maxIter,

+            splitOnIter = cgsparam$splitOnIter,

+            splitOnLast = cgsparam$splitOnLast,

+            nchains = cgsparam$nchains,

+            zInitialize = cgsparamp[["zInitialize"]],

+            zInit = cgsparamp[["zInit"]],

+            logfile = cgsparamp$logfile,

+            verbose = cgsparamp$verbose)

+    } else if (cgsparam$model == "celda_G") {

+        x <- .createSCEceldaG(celdaGMod = resList(x)[[ix]],

+            sce = x,

+            xClass = cgsparam$xClass,

+            useAssay = cgsparam$useAssay,

+            stopIter = cgsparam$stopIter,

+            maxIter = cgsparam$maxIter,

+            splitOnIter = cgsparam$splitOnIter,

+            splitOnLast = cgsparam$splitOnLast,

+            nchains = cgsparam$nchains,

+            yInitialize = cgsparam[["yInitialize"]],

+            yInit = cgsparam[["yInit"]],

+            logfile = cgsparam$logfile,

+            verbose = cgsparam$verbose)

+    } else if (cgsparam$model == "celda_CG") {

+        x <- .createSCEceldaCG(celdaCGMod = resList(x)[[ix]],

+            sce = x,

+            xClass = cgsparam$xClass,

+            useAssay = cgsparam$useAssay,

+            algorithm = cgsparam$algorithm,

+            stopIter = cgsparam$stopIter,

+            maxIter = cgsparam$maxIter,

+            splitOnIter = cgsparam$splitOnIter,

+            splitOnLast = cgsparam$splitOnLast,

+            nchains = cgsparam$nchains,

+            zInitialize = cgsparam[["zInitialize"]],

+            yInitialize = cgsparam[["yInitialize"]],

+            zInit = cgsparam[["zInit"]],

+            yInit = cgsparam[["yInit"]],

+            logfile = cgsparam$logfile,

+            verbose = cgsparam$verbose)

+    } else {

+        stop("S4Vectors::metadata(X)$celda_grid_search@",

+            "celdaGridSearchParameters$model must be",

+            " one of 'celda_C', 'celda_G', or 'celda_CG'")

+    }

+    return(x)

+}

@@ -587,12 +587,13 @@ setMethod("selectBestModel", signature(x = "SingleCellExperiment"),

         logLikelihood <- NULL

         group <- setdiff(colnames(runParams(x)),

             c("index", "chain", "logLikelihood", "mean_perplexity", "seed"))

-        dt <- data.table::as.data.table(runParams(celdaList))

+        runParams <- S4Vectors::metadata(x)$celda_grid_search@runParams

+        dt <- data.table::as.data.table(runParams)

         newRunParams <- as.data.frame(dt[, .SD[which.max(logLikelihood)],

             by = group])

-        newRunParams <- newRunParams[, colnames(runParams(celdaList))]

+        newRunParams <- newRunParams[, colnames(runParams)]

-        ix <- match(newRunParams$index, runParams(celdaList)$index)

+        ix <- match(newRunParams$index, runParams$index)

         if (nrow(newRunParams) == 1 & !asList) {

             x <- celdatosce(resList(x)[[ix]],

                 SummarizedExperiment::assay(x, i = useAssay))

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

 #'  \link[S4Vectors]{metadata} \code{"celda_grid_search"} slot.

 #' @seealso \link{celda_G} for feature clustering, \link{celda_C} for

 #'  clustering of cells, and \link{celda_CG} for simultaneous clustering of

-#'  features and cells. \link{subsetCeldaList} can subset the \link{celdaList}

+#'  features and cells. \link{subsetCeldaList} can subset the \code{celdaList}

 #'  object. \link{selectBestModel} can get the best model for each combination

 #'  of parameters.

 #' @import foreach

@@ -606,6 +606,7 @@ setMethod("selectBestModel", signature(x = "SingleCellExperiment"),

 )

+#' @rdname selectBestModel

 #' @examples

 #' data(celdaCGGridSearchRes)

 #' ## Returns same result as running celdaGridSearch with "bestOnly = TRUE"

@@ -33,7 +33,7 @@

 #' @param seed Integer. Passed to \link[withr]{with_seed}. For reproducibility,

 #'  a default value of 12345 is used. Seed values

 #'  \code{seq(seed, (seed + nchains - 1))} will be supplied to each chain in

-#'  \code{nchains} If NULL, no calls to

+#'  \code{nchains}. If NULL, no calls to

 #'  \link[withr]{with_seed} are made.

 #' @param perplexity Logical. Whether to calculate perplexity for each model.

 #'  If FALSE, then perplexity can be calculated later with

@@ -37,7 +37,7 @@

 #'  \link[withr]{with_seed} are made.

 #' @param perplexity Logical. Whether to calculate perplexity for each model.

 #'  If FALSE, then perplexity can be calculated later with

-#'  `resamplePerplexity()`. Default TRUE.

+#'  \link{resamplePerplexity}. Default TRUE.

 #' @param verbose Logical. Whether to print log messages during celda chain

 #'  execution. Default TRUE.

 #' @param logfilePrefix Character. Prefix for log files from worker threads

@@ -541,50 +541,97 @@ setMethod("subsetCeldaList",

 #' @title Select best chain within each combination of parameters

 #' @description Select the chain with the best log likelihood for each

-#'  combination of tested parameters from a `celdaList` object gererated by

-#'  `celdaGridSearch()`.

-#' @param celdaList Object of class `celdaList`. An object containing celda

-#'  models returned from `celdaGridSearch()`.

-#' @param asList `TRUE` or `FALSE`. Whether to return the best model as a

-#'  `celdaList` object or not. If `FALSE`, return the best model as a

-#'  corresponding `celda_C`, `celda_G` or `celda_CG` object.

-#' @return A new `celdaList` object containing one model with the best log

-#'  likelihood for each set of parameters. If only one set of parameters is in

-#'  the `celdaList`, the best model will be returned directly instead of a

-#'  `celdaList` object.

-#' @seealso `celdaGridSearch()` can run Celda with multiple parameters and

-#'  chains in parallel. `subsetCeldaList()` can subset the `celdaList` object.

+#'  combination of tested parameters from a \code{SCE} object gererated by

+#'  \link{celdaGridSearch} or from a \code{celdaList} object.

+#' @param x Object of class \linkS4class{SingleCellExperiment} or

+#'  \code{celdaList}. An object containing celda

+#'  models returned from \link{celdaGridSearch}.

+#' @param asList \code{TRUE} or \code{FALSE}. Whether to return the

+#'  best model as a

+#'  \code{celdaList} object or not. If \code{FALSE}, return the best model as a

+#'  corresponding celda model object.

+#' @param useAssay A string specifying which \code{assay}

+#'  slot to use if \code{x} is a

+#'  \linkS4class{SingleCellExperiment} object. Default "counts".

+#' @return One of

+#' \itemize{

+#'  \item A new \linkS4class{SingleCellExperiment} object containing

+#'  one model with the best log-likelihood for each set of parameters in

+#'  \code{metadata(x)}. If there is only one set of parameters,

+#'  a new \linkS4class{SingleCellExperiment} object

+#'  with the matching model stored in the

+#'  \link[S4Vectors]{metadata}

+#'  \code{"celda_parameters"} slot will be returned. Otherwise, a new

+#'  \linkS4class{SingleCellExperiment} object with the subset models stored

+#'  in the \link[S4Vectors]{metadata}

+#'  \code{"celda_grid_search"} slot will be returned.

+#'  \item A new \code{celdaList} object containing one model with the best

+#'  log-likelihood for each set of parameters. If only one set of parameters

+#'  is in the \code{celdaList}, the best model will be returned directly

+#'  instead of a \code{celdaList} object.}

+#' @seealso \link{celdaGridSearch} \link{subsetCeldaList}

+#' @export

+setGeneric("selectBestModel", function(x, ...) {

+    standardGeneric("selectBestModel")})

+

+

+#' @rdname selectBestModel

+#' @examples

+#' data(sceCeldaCGGridSearch)

+#' ## Returns same result as running celdaGridSearch with "bestOnly = TRUE"

+#' sce <- selectBestModel(sceCeldaCGGridSearch)

+#' @importFrom data.table as.data.table

+#' @export

+setMethod("selectBestModel", signature(x = "SingleCellExperiment"),

+    function(x, asList = FALSE, useAssay = "counts") {

+        logLikelihood <- NULL

+        group <- setdiff(colnames(runParams(x)),

+            c("index", "chain", "logLikelihood", "mean_perplexity", "seed"))

+        dt <- data.table::as.data.table(runParams(celdaList))

+        newRunParams <- as.data.frame(dt[, .SD[which.max(logLikelihood)],

+            by = group])

+        newRunParams <- newRunParams[, colnames(runParams(celdaList))]

+

+        ix <- match(newRunParams$index, runParams(celdaList)$index)

+        if (nrow(newRunParams) == 1 & !asList) {

+            x <- celdatosce(resList(x)[[ix]],

+                SummarizedExperiment::assay(x, i = useAssay))

+        } else {

+            x@metadata$celda_grid_search@runParams <-

+                as.data.frame(newRunParams)

+            x@metadata$celda_grid_search@resList <- resList(x)[ix]

+        }

+        return(x)

+    }

+)

+

+

 #' @examples

 #' data(celdaCGGridSearchRes)

 #' ## Returns same result as running celdaGridSearch with "bestOnly = TRUE"

 #' cgsBest <- selectBestModel(celdaCGGridSearchRes)

 #' @importFrom data.table as.data.table

 #' @export

-selectBestModel <- function(celdaList, asList = FALSE) {

-  if (!methods::is(celdaList, "celdaList")) {

-    stop("celdaList parameter was not of class celdaList.")

-  }

+setMethod("selectBestModel", signature(x = "celdaList"),

+    function(x, asList = FALSE) {

+        logLikelihood <- NULL

+        group <- setdiff(colnames(runParams(x)),

+            c("index", "chain", "logLikelihood", "mean_perplexity", "seed"))

+        dt <- data.table::as.data.table(runParams(x))

+        newRunParams <- as.data.frame(dt[, .SD[which.max(logLikelihood)],

+            by = group])

+        newRunParams <- newRunParams[, colnames(runParams(x))]

-  logLikelihood <- NULL

-  group <- setdiff(