@@ -1,5 +1,5 @@

 Package: scone

-Version: 0.99.2

+Version: 0.99.6

 Title: Single Cell Overview of Normalized Expression data

 Description: SCONE is an R package for comparing and ranking the performance of

 	different normalization schemes for single-cell RNA-seq and other 

@@ -4,6 +4,7 @@ export(DESEQ_FN)

 export(DESEQ_FN_POS)

 export(FQT_FN)

 export(FQ_FN)

+export(SconeExperiment)

 export(TMM_FN)

 export(UQ_FN)

 export(UQ_FN_POS)

@@ -16,19 +17,24 @@ export(impute_null)

 export(lm_adjust)

 export(make_design)

 export(metric_sample_filter)

-export(sconeExperiment)

 export(sconeReport)

 export(scone_easybake)

 export(score_matrix)

 export(simple_FNR_params)

 exportClasses(SconeExperiment)

+exportMethods(SconeExperiment)

+exportMethods(get_batch)

+exportMethods(get_bio)

 exportMethods(get_design)

+exportMethods(get_negconeval)

+exportMethods(get_negconruv)

 exportMethods(get_normalized)

 exportMethods(get_params)

+exportMethods(get_poscon)

+exportMethods(get_qc)

 exportMethods(get_score_ranks)

 exportMethods(get_scores)

 exportMethods(scone)

-exportMethods(sconeExperiment)

 exportMethods(select_methods)

 import(BiocParallel)

 import(SummarizedExperiment)

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

 #'   batch information, and biological classes of interest (if available).

 #'   

 #' @description The typical way of creating \code{SconeExperiment} objects is 

-#'   via a call to the \code{\link{sconeExperiment}} function or to the 

+#'   via a call to the \code{\link{SconeExperiment}} function or to the 

 #'   \code{\link{scone}} function. If the object is a result to a 

 #'   \code{\link{scone}} call, it will contain the results, e.g., the 

 #'   performance metrics, scores, and normalization workflow comparisons. (See 

@@ -68,6 +68,14 @@

 #' @slot fixzero logical. TRUE if \code{\link{scone}} was run with 

 #'   \code{zero="postadjust"} or \code{zero="strong"}.

 #' @slot impute_args list. Arguments passed to all imputation functions.

+#' 

+#' @seealso \code{\link{get_normalized}}, \code{\link{get_params}},

+#' \code{\link{get_batch}}, \code{\link{get_bio}}, \code{\link{get_design}},

+#' \code{\link{get_negconeval}}, \code{\link{get_negconruv}},

+#' \code{\link{get_poscon}}, \code{\link{get_qc}}, 

+#' \code{\link{get_scores}}, and \code{\link{get_score_ranks}} 

+#' to access internal fields, \code{\link{select_methods}} for subsetting

+#' by method, and \code{\link{scone}} for running scone workflows.

 #'   

 setClass(

   Class = "SconeExperiment",

@@ -143,38 +151,38 @@ setValidity("SconeExperiment", function(object) {

   ## check that all QC columns are numeric

   if(length(object@which_qc) > 0) {

-    if(any(lapply(colData(object)[,object@which_qc], class) != "numeric")) {

+    if(any(lapply(get_qc(object), class) != "numeric")) {

       return("Only numeric QC metrics are allowed.")

     }

   }

   ## check that bio is a factor

   if(length(object@which_bio) > 0) {

-    if(!is.factor(colData(object)[,object@which_bio])) {

+    if(!is.factor(get_bio(object))) {

       return("`bio` must be a factor.")

     }

   }

   ## check that batch is a factor

   if(length(object@which_batch) > 0) {

-    if(!is.factor(colData(object)[,object@which_batch])) {

+    if(!is.factor(get_batch(object))) {

       return("`batch` must be a factor.")

     }

   }

   ## check that poscon and negcon are logical

   if(length(object@which_negconruv) > 0) {

-    if(!is.logical(rowData(object)[,object@which_negconruv])) {

+    if(!is.logical(get_negconruv(object))) {

       return("`negconruv` must be a logical vector.")

     }

   }

   if(length(object@which_negconeval) > 0) {

-    if(!is.logical(rowData(object)[,object@which_negconeval])) {

+    if(!is.logical(get_negconeval(object))) {

       return("`negconeval` must be a logical vector.")

     }

   }

   if(length(object@which_poscon) > 0) {

-    if(!is.logical(rowData(object)[,object@which_poscon])) {

+    if(!is.logical(get_poscon(object))) {

       return("`poscon` must be a logical vector.")

     }

   }

@@ -198,7 +206,7 @@ setValidity("SconeExperiment", function(object) {

 #' @rdname SconeExperiment-class

 #'   

-#' @description The constructor \code{sconeExperiment} creates an object of the

+#' @description The constructor \code{SconeExperiment} creates an object of the

 #'   class \code{SconeExperiment}.

 #'   

 #' @param object Either a matrix or a \code{\link{SummarizedExperiment}} 

@@ -207,7 +215,7 @@ setValidity("SconeExperiment", function(object) {

 #' @export

 #' 

 #' @examples

-#' 

+#' set.seed(42)

 #' nrows <- 200

 #' ncols <- 6

 #' counts <- matrix(rpois(nrows * ncols, lambda=10), nrows)

@@ -216,15 +224,15 @@ setValidity("SconeExperiment", function(object) {

 #' se <- SummarizedExperiment(assays=SimpleList(counts=counts),

 #'                           rowData=rowdata, colData=coldata)

 #' 

-#' scone1 <- sconeExperiment(assay(se), bio=coldata$bio, poscon=rowdata$poscon)

+#' scone1 <- SconeExperiment(assay(se), bio=coldata$bio, poscon=rowdata$poscon)

 #' 

-#' scone2 <- sconeExperiment(se, which_bio=1L, which_poscon=1L)

+#' scone2 <- SconeExperiment(se, which_bio=1L, which_poscon=1L)

 #' 

 #' 

 setGeneric(

-  name = "sconeExperiment",

+  name = "SconeExperiment",

   def = function(object, ...) {

-    standardGeneric("sconeExperiment")

+    standardGeneric("SconeExperiment")

   }

 )

@@ -246,7 +254,7 @@ setGeneric(

 #' @export

 #' 

 setMethod(

-  f = "sconeExperiment",

+  f = "SconeExperiment",

   signature = signature("SummarizedExperiment"),

   definition = function(object, which_qc=integer(), which_bio=integer(),

                         which_batch=integer(),

@@ -295,10 +303,10 @@ setMethod(

 #'   

 #' @export

 #' 

-#' @return A \code{\link{sconeExperiment}} object.

+#' @return A \code{\link{SconeExperiment}} object.

 #'   

 setMethod(

-  f = "sconeExperiment",

+  f = "SconeExperiment",

   signature = signature("matrix"),

   definition = function(object, qc, bio, batch,

                         negcon_ruv=NULL, negcon_eval=negcon_ruv,

@@ -342,7 +350,7 @@ setMethod(

     }

     se <- SummarizedExperiment(object, rowData=rowdata, colData=coldata)

-    sconeExperiment(se,  which_qc, which_bio, which_batch,

+    SconeExperiment(se,  which_qc, which_bio, which_batch,

                     which_negconruv, which_negconeval, which_poscon, is_log)

   }

 )

@@ -21,7 +21,7 @@ setGeneric(

 #' @details If \code{\link{scone}} was run with \code{return_norm="no"}, this 

 #'   function will compute the normalized matrix on the fly.

 #'   

-#' @param x a \code{\link{sconeExperiment}} object containing the results of 

+#' @param x a \code{\link{SconeExperiment}} object containing the results of 

 #'   \code{\link{scone}}.

 #' @param method character or numeric. Either a string identifying the 

 #'   normalization scheme to be retrieved, or a numeric index with the rank of 

@@ -32,13 +32,14 @@ setGeneric(

 #' @return A matrix of normalized counts in log-scale.

 #'   

 #' @examples

+#' set.seed(42)

 #' mat <- matrix(rpois(500, lambda = 5), ncol=10)

 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-#' obj <- sconeExperiment(mat)

-#' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

+#' obj <- SconeExperiment(mat)

+#' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN),

 #'            evaluate=TRUE, k_ruv=0, k_qc=0, 

 #'            eval_kclust=2, bpparam = BiocParallel::SerialParam())

-#' norm = get_normalized(res,1)

+#' top_norm = get_normalized(res,1)

 #'            

 #' 

 setGeneric(

@@ -53,7 +54,7 @@ setGeneric(

 #' Given a \code{SconeExperiment} object created by a call to scone, it will

 #' return the design matrix of the selected method.

 #'

-#' @param x a \code{\link{sconeExperiment}} object containing the results of

+#' @param x a \code{\link{SconeExperiment}} object containing the results of

 #'   \code{\link{scone}}.

 #' @param method character or numeric. Either a string identifying the

 #'   normalization scheme to be retrieved, or a numeric index with the rank of

@@ -63,13 +64,16 @@ setGeneric(

 #' @return The design matrix.

 #' 

 #' @examples

+#' set.seed(42)

 #' mat <- matrix(rpois(500, lambda = 5), ncol=10)

 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-#' obj <- sconeExperiment(mat)

-#' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

+#' obj <- SconeExperiment(mat, bio = factor(rep(c(1,2),each = 5)),

+#'            batch = factor(rep(c(1,2),times = 5)))

+#' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN),

 #'            evaluate=TRUE, k_ruv=0, k_qc=0, 

+#'            adjust_batch = "yes", adjust_bio = "yes",

 #'            eval_kclust=2, bpparam = BiocParallel::SerialParam())

-#' null_design = get_design(res,1)

+#' design_top = get_design(res,1)

 #' 

 setGeneric(

   name = "get_design",

@@ -95,10 +99,11 @@ setGeneric(

 #' @return A \code{SconeExperiment} object with selected method data.

 #'   

 #' @examples

+#' set.seed(42)

 #' mat <- matrix(rpois(500, lambda = 5), ncol=10)

 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-#' obj <- sconeExperiment(mat)

-#' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

+#' obj <- SconeExperiment(mat)

+#' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN),

 #'            evaluate=TRUE, k_ruv=0, k_qc=0, 

 #'            eval_kclust=2, bpparam = BiocParallel::SerialParam())

 #' select_res = select_methods(res,1:2)

@@ -114,6 +119,18 @@ setGeneric(

 #' 

 #' @aliases get_negconeval get_poscon get_negconruv,SconeExperiment-method 

 #'   get_negconeval,SconeExperiment-method get_poscon,SconeExperiment-method

+#' 

+#' @examples

+#' set.seed(42)

+#' mat <- matrix(rpois(500, lambda = 5), ncol=10)

+#' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

+#' obj <- SconeExperiment(mat,negcon_ruv = 1:50 %in% 1:10,

+#'            negcon_eval = 1:50 %in% 11:20,

+#'            poscon = 1:50 %in% 21:30)

+#' negcon_ruv = get_negconruv(obj)

+#' negcon_eval = get_negconeval(obj)

+#' poscon = get_poscon(obj)

+#'

 setGeneric(

   name = "get_negconruv",

   def = function(x) {

@@ -139,6 +156,14 @@ setGeneric(

 #' Get Quality Control Matrix

 #' 

+#' @examples

+#' set.seed(42)

+#' mat <- matrix(rpois(500, lambda = 5), ncol=10)

+#' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

+#' obj <- SconeExperiment(mat,

+#'          qc = cbind(colSums(mat),colSums(mat > 0)))

+#' qc = get_qc(obj)

+#'

 #' @aliases get_qc,SconeExperiment-method

 setGeneric(

   name = "get_qc",

@@ -149,8 +174,18 @@ setGeneric(

 #' Get Factor of Biological Conditions and Batch

 #'

-#' @aliases get_batch get_bio,SconeExperiment-method

+#' @aliases get_bio get_batch get_bio,SconeExperiment-method

 #'   get_batch,SconeExperiment-method

+#'   

+#' @examples 

+#' set.seed(42)

+#' mat <- matrix(rpois(500, lambda = 5), ncol=10)

+#' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

+#' obj <- SconeExperiment(mat, bio = factor(rep(c(1,2),each = 5)),

+#'            batch = factor(rep(c(1,2),times = 5)))

+#' bio = get_bio(obj)

+#' batch = get_batch(obj)

+#' 

 setGeneric(

   name = "get_bio",

   def = function(x) {

@@ -168,14 +203,15 @@ setGeneric(

 #' Extract scone scores

 #' 

-#' @aliases get_scores get_score,SconeExperiment-method get_score_ranks 

+#' @aliases get_scores get_scores,SconeExperiment-method get_score_ranks 

 #'   get_score_ranks,SconeExperiment-method

 #'   

 #' @examples

+#' set.seed(42)

 #' mat <- matrix(rpois(500, lambda = 5), ncol=10)

 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-#' obj <- sconeExperiment(mat)

-#' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

+#' obj <- SconeExperiment(mat)

+#' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN),

 #'            evaluate=TRUE, k_ruv=0, k_qc=0, 

 #'            eval_kclust=2, bpparam = BiocParallel::SerialParam())

 #' scores = get_scores(res)

@@ -199,11 +235,13 @@ setGeneric(

 #' Extract scone parameters

 #' 

 #' @aliases get_params get_params,SconeExperiment-method

+#' 

 #' @examples

+#' set.seed(42)

 #' mat <- matrix(rpois(500, lambda = 5), ncol=10)

 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-#' obj <- sconeExperiment(mat)

-#' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

+#' obj <- SconeExperiment(mat)

+#' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN),

 #'            run = FALSE, k_ruv=0, k_qc=0, eval_kclust=2)

 #' params = get_params(res)

 #' 

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

 #'   in static documents, such as vignettes or markdown / knitr documents. See

 #'   \code{biplot_color} for more details on the internals.

 #'   

-#' @param x a \code{\link{sconeExperiment}} object.

+#' @param x a \code{\link{SconeExperiment}} object.

 #' @param ... passed to \code{\link{biplot_color}}.

 #'   

 #' @importFrom miniUI gadgetTitleBar miniContentPanel miniPage gadgetTitleBar

@@ -16,13 +16,13 @@

 #'   

 #' @export

 #' 

-#' @return A \code{\link{sconeExperiment}} object representing 

+#' @return A \code{\link{SconeExperiment}} object representing 

 #'   selected methods.

 #'   

 #' @examples

 #' mat <- matrix(rpois(1000, lambda = 5), ncol=10)

 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-#' obj <- sconeExperiment(mat)

+#' obj <- SconeExperiment(mat)

 #' res <- scone(obj, scaling=list(none=identity,

 #'    uq=UQ_FN, deseq=DESEQ_FN,  fq=FQT_FN),

 #' evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2,

@@ -1,6 +1,6 @@

 #' @rdname get_params

 #'   

-#' @param x an object of class \code{\link{sconeExperiment}}.

+#' @param x an object of class \code{\link{SconeExperiment}}.

 #'   

 #' @return A data.frame containing workflow parameters for each scone workflow.

 #'   

@@ -15,7 +15,7 @@ setMethod(

 #' @rdname get_scores

 #'   

-#' @param x an object of class \code{\link{sconeExperiment}}.

+#' @param x an object of class \code{\link{SconeExperiment}}.

 #'   

 #' @return \code{get_scores} returns a matrix with all (non-missing) scone 

 #'   scores, ordered by average score rank.

@@ -46,12 +46,15 @@ setMethod(

 #' @rdname get_negconruv

 #'   

-#' @param x an object of class \code{\link{sconeExperiment}}.

+#' @param x an object of class \code{\link{SconeExperiment}}.

 #'   

 #' @return NULL or a logical vector.

 #'   

 #' @return For \code{get_negconruv} the returned vector indicates which genes

 #'   are negative controls to be used for RUV.

+#'   

+#' @export

+#' 

 setMethod(

   f = "get_negconruv",

   signature = signature(x = "SconeExperiment"),

@@ -68,6 +71,9 @@ setMethod(

 #'   

 #' @return For \code{get_negconeval} the returned vector indicates which genes

 #'   are negative controls to be used for evaluation.

+#'

+#' @export

+#' 

 setMethod(

   f = "get_negconeval",

   signature = signature(x = "SconeExperiment"),

@@ -84,6 +90,9 @@ setMethod(

 #'   

 #' @return For \code{get_poscon} the returned vector indicates which genes are 

 #'   positive controls to be used for evaluation.

+#'

+#' @export

+#'

 setMethod(

   f = "get_poscon",

   signature = signature(x = "SconeExperiment"),

@@ -98,9 +107,12 @@ setMethod(

 #' @rdname get_qc

 #'   

-#' @param x an object of class \code{\link{sconeExperiment}}.

+#' @param x an object of class \code{\link{SconeExperiment}}.

 #'   

 #' @return NULL or the quality control (QC) metric matrix.

+#'

+#' @export

+#'

 setMethod(

   f = "get_qc",

   signature = signature(x = "SconeExperiment"),

@@ -116,9 +128,12 @@ setMethod(

 #' @rdname get_bio

 #'   

-#' @param x an object of class \code{\link{sconeExperiment}}.

+#' @param x an object of class \code{\link{SconeExperiment}}.

 #'   

 #' @return NULL or a factor containing bio or batch covariate.

+#'

+#' @export

+#'

 setMethod(

   f = "get_bio",

   signature = signature(x = "SconeExperiment"),

@@ -132,6 +147,9 @@ setMethod(

 )

 #' @rdname get_bio

+#'

+#' @export

+#'

 setMethod(

   f = "get_batch",

   signature = signature(x = "SconeExperiment"),

@@ -44,7 +44,7 @@

 #' set.seed(101)

 #' mat <- matrix(rpois(1000, lambda = 5), ncol=10)

 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-#' obj <- sconeExperiment(mat)

+#' obj <- SconeExperiment(mat)

 #' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

 #'            evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2, 

 #'            bpparam = BiocParallel::SerialParam())

@@ -139,8 +139,7 @@

 #' 

 #' mat <- matrix(rpois(1000, lambda = 5), ncol=10)

 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-#' obj <- sconeExperiment(mat)

-#' 

+#' obj <- SconeExperiment(mat)

 #' no_results <- scone(obj, scaling=list(none=identity,

 #'            uq=UQ_FN, deseq=DESEQ_FN),

 #'            run=FALSE, k_ruv=0, k_qc=0, eval_kclust=2)

@@ -92,7 +92,7 @@

 #' set.seed(101)

 #' mat <- matrix(rpois(1000, lambda = 5), ncol=10)

 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-#' obj <- sconeExperiment(mat)

+#' obj <- SconeExperiment(mat)

 #' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

 #'            evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2, 

 #'            bpparam = BiocParallel::SerialParam())

@@ -420,7 +420,7 @@ scone_easybake <- function(expr, qc,

     args_list = c( args_list, list( batch = batch ))

   }

-  my_scone <- do.call(sconeExperiment,args_list)

+  my_scone <- do.call(SconeExperiment,args_list)

   my_scone <- scone(my_scone,

                     imputation = imputation, impute_args = impute_args,

@@ -17,4 +17,6 @@ biocLite("devtools")

 biocLite("YosefLab/scone", dependencies=TRUE)

 ```

-Note that SCONE requires R (>= 3.3) and Bioconductor (>= 3.3). You can download the latest version of R from [CRAN](https://cran.r-project.org). To update Bioconductor to its latest release, follow the instructions [here](https://www.bioconductor.org/install/#update-bioconductor-packages).

+Note that SCONE is currently under consideration in Bioconductor and hence requires R-devel (>= 3.4) and Bioconductor devel. 

+

+You can download the latest release of SCONE for R 3.3 [here](https://github.com/YosefLab/scone/releases/tag/v0.99.0).

@@ -3,20 +3,19 @@

 \docType{class}

 \name{SconeExperiment-class}

 \alias{SconeExperiment}

+\alias{SconeExperiment,SummarizedExperiment-method}

+\alias{SconeExperiment,matrix-method}

 \alias{SconeExperiment-class}

-\alias{sconeExperiment}

-\alias{sconeExperiment,SummarizedExperiment-method}

-\alias{sconeExperiment,matrix-method}

 \title{Class SconeExperiment}

 \usage{

-sconeExperiment(object, ...)

+SconeExperiment(object, ...)

-\S4method{sconeExperiment}{SummarizedExperiment}(object, which_qc = integer(),

+\S4method{SconeExperiment}{SummarizedExperiment}(object, which_qc = integer(),

   which_bio = integer(), which_batch = integer(),

   which_negconruv = integer(), which_negconeval = integer(),

   which_poscon = integer(), is_log = FALSE)

-\S4method{sconeExperiment}{matrix}(object, qc, bio, batch, negcon_ruv = NULL,

+\S4method{SconeExperiment}{matrix}(object, qc, bio, batch, negcon_ruv = NULL,

   negcon_eval = negcon_ruv, poscon = NULL, is_log = FALSE)

 }

 \arguments{

@@ -61,7 +60,7 @@ negative controls for evaluation.}

 controls.}

 }

 \value{

-A \code{\link{sconeExperiment}} object.

+A \code{\link{SconeExperiment}} object.

 }

 \description{

 Objects of this class store, at minimum, a gene expression 

@@ -70,7 +69,7 @@ Objects of this class store, at minimum, a gene expression

   batch information, and biological classes of interest (if available).

 The typical way of creating \code{SconeExperiment} objects is 

-  via a call to the \code{\link{sconeExperiment}} function or to the 

+  via a call to the \code{\link{SconeExperiment}} function or to the 

   \code{\link{scone}} function. If the object is a result to a 

   \code{\link{scone}} call, it will contain the results, e.g., the 

   performance metrics, scores, and normalization workflow comparisons. (See 

@@ -79,7 +78,7 @@ The typical way of creating \code{SconeExperiment} objects is

 This object extends the 

   \code{\linkS4class{SummarizedExperiment}} class.

-The constructor \code{sconeExperiment} creates an object of the

+The constructor \code{SconeExperiment} creates an object of the

   class \code{SconeExperiment}.

 }

 \details{

@@ -149,7 +148,7 @@ run and in which mode ("no", "in_memory", "hdf5").}

 \item{\code{impute_args}}{list. Arguments passed to all imputation functions.}

 }}

 \examples{

-

+set.seed(42)

 nrows <- 200

 ncols <- 6

 counts <- matrix(rpois(nrows * ncols, lambda=10), nrows)

@@ -158,10 +157,19 @@ coldata <- data.frame(bio=gl(2, 3))

 se <- SummarizedExperiment(assays=SimpleList(counts=counts),

                           rowData=rowdata, colData=coldata)

-scone1 <- sconeExperiment(assay(se), bio=coldata$bio, poscon=rowdata$poscon)

+scone1 <- SconeExperiment(assay(se), bio=coldata$bio, poscon=rowdata$poscon)

-scone2 <- sconeExperiment(se, which_bio=1L, which_poscon=1L)

+scone2 <- SconeExperiment(se, which_bio=1L, which_poscon=1L)

 }

+\seealso{

+\code{\link{get_normalized}}, \code{\link{get_params}},

+\code{\link{get_batch}}, \code{\link{get_bio}}, \code{\link{get_design}},

+\code{\link{get_negconeval}}, \code{\link{get_negconruv}},

+\code{\link{get_poscon}}, \code{\link{get_qc}}, 

+\code{\link{get_scores}}, and \code{\link{get_score_ranks}} 

+to access internal fields, \code{\link{select_methods}} for subsetting

+by method, and \code{\link{scone}} for running scone workflows.

+}

@@ -7,12 +7,12 @@

 biplot_interactive(x, ...)

 }

 \arguments{

-\item{x}{a \code{\link{sconeExperiment}} object.}

+\item{x}{a \code{\link{SconeExperiment}} object.}

 \item{...}{passed to \code{\link{biplot_color}}.}

 }

 \value{

-A \code{\link{sconeExperiment}} object representing 

+A \code{\link{SconeExperiment}} object representing 

   selected methods.

 }

 \description{

@@ -27,7 +27,7 @@ Since this is based on the shiny gadget feature, it will not work

 \examples{

 mat <- matrix(rpois(1000, lambda = 5), ncol=10)

 colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-obj <- sconeExperiment(mat)

+obj <- SconeExperiment(mat)

 res <- scone(obj, scaling=list(none=identity,

    uq=UQ_FN, deseq=DESEQ_FN,  fq=FQT_FN),

 evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2,

@@ -17,7 +17,7 @@ get_batch(x)

 \S4method{get_batch}{SconeExperiment}(x)

 }

 \arguments{

-\item{x}{an object of class \code{\link{sconeExperiment}}.}

+\item{x}{an object of class \code{\link{SconeExperiment}}.}

 }

 \value{

 NULL or a factor containing bio or batch covariate.

@@ -25,4 +25,14 @@ NULL or a factor containing bio or batch covariate.

 \description{

 Get Factor of Biological Conditions and Batch

 }

+\examples{

+set.seed(42)

+mat <- matrix(rpois(500, lambda = 5), ncol=10)

+colnames(mat) <- paste("X", 1:ncol(mat), sep="")

+obj <- SconeExperiment(mat, bio = factor(rep(c(1,2),each = 5)),

+           batch = factor(rep(c(1,2),times = 5)))

+bio = get_bio(obj)

+batch = get_batch(obj)

+

+}

@@ -14,7 +14,7 @@ get_design(x, method)

 \S4method{get_design}{SconeExperiment,numeric}(x, method)

 }

 \arguments{

-\item{x}{a \code{\link{sconeExperiment}} object containing the results of

+\item{x}{a \code{\link{SconeExperiment}} object containing the results of

 \code{\link{scone}}.}

 \item{method}{character or numeric. Either a string identifying the

@@ -51,13 +51,16 @@ by the character string. The string must be one of the

 according to the scone ranking.

 }}

 \examples{

+set.seed(42)

 mat <- matrix(rpois(500, lambda = 5), ncol=10)

 colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-obj <- sconeExperiment(mat)

-res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

+obj <- SconeExperiment(mat, bio = factor(rep(c(1,2),each = 5)),

+           batch = factor(rep(c(1,2),times = 5)))

+res <- scone(obj, scaling=list(none=identity, uq=UQ_FN),

            evaluate=TRUE, k_ruv=0, k_qc=0, 

+           adjust_batch = "yes", adjust_bio = "yes",

            eval_kclust=2, bpparam = BiocParallel::SerialParam())

-null_design = get_design(res,1)

+design_top = get_design(res,1)

 }

@@ -23,7 +23,7 @@ get_poscon(x)

 \S4method{get_poscon}{SconeExperiment}(x)

 }

 \arguments{

-\item{x}{an object of class \code{\link{sconeExperiment}}.}

+\item{x}{an object of class \code{\link{SconeExperiment}}.}

 }

 \value{

 NULL or a logical vector.

@@ -40,4 +40,16 @@ For \code{get_poscon} the returned vector indicates which genes are

 \description{

 Get Negative and Positive Controls

 }

+\examples{

+set.seed(42)

+mat <- matrix(rpois(500, lambda = 5), ncol=10)

+colnames(mat) <- paste("X", 1:ncol(mat), sep="")

+obj <- SconeExperiment(mat,negcon_ruv = 1:50 \%in\% 1:10,

+           negcon_eval = 1:50 \%in\% 11:20,

+           poscon = 1:50 \%in\% 21:30)

+negcon_ruv = get_negconruv(obj)

+negcon_eval = get_negconeval(obj)

+poscon = get_poscon(obj)

+

+}

@@ -14,7 +14,7 @@ get_normalized(x, method, ...)

 \S4method{get_normalized}{SconeExperiment,numeric}(x, method, log = FALSE)

 }

 \arguments{

-\item{x}{a \code{\link{sconeExperiment}} object containing the results of 

+\item{x}{a \code{\link{SconeExperiment}} object containing the results of 

 \code{\link{scone}}.}

 \item{method}{character or numeric. Either a string identifying the 

@@ -65,13 +65,14 @@ by the character string.The string must be one of the

 matrix according to the scone ranking.

 }}

 \examples{

+set.seed(42)

 mat <- matrix(rpois(500, lambda = 5), ncol=10)

 colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-obj <- sconeExperiment(mat)

-res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

+obj <- SconeExperiment(mat)

+res <- scone(obj, scaling=list(none=identity, uq=UQ_FN),

            evaluate=TRUE, k_ruv=0, k_qc=0, 

            eval_kclust=2, bpparam = BiocParallel::SerialParam())

-norm = get_normalized(res,1)

+top_norm = get_normalized(res,1)

 }

@@ -11,7 +11,7 @@ get_params(x)

 \S4method{get_params}{SconeExperiment}(x)

 }

 \arguments{

-\item{x}{an object of class \code{\link{sconeExperiment}}.}

+\item{x}{an object of class \code{\link{SconeExperiment}}.}

 }

 \value{

 A data.frame containing workflow parameters for each scone workflow.

@@ -20,10 +20,11 @@ A data.frame containing workflow parameters for each scone workflow.

 Extract scone parameters

 }

 \examples{

+set.seed(42)

 mat <- matrix(rpois(500, lambda = 5), ncol=10)

 colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-obj <- sconeExperiment(mat)

-res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

+obj <- SconeExperiment(mat)

+res <- scone(obj, scaling=list(none=identity, uq=UQ_FN),

            run = FALSE, k_ruv=0, k_qc=0, eval_kclust=2)

 params = get_params(res)

@@ -11,7 +11,7 @@ get_qc(x)

 \S4method{get_qc}{SconeExperiment}(x)

 }

 \arguments{

-\item{x}{an object of class \code{\link{sconeExperiment}}.}

+\item{x}{an object of class \code{\link{SconeExperiment}}.}

 }

 \value{

 NULL or the quality control (QC) metric matrix.

@@ -19,4 +19,13 @@ NULL or the quality control (QC) metric matrix.

 \description{

 Get Quality Control Matrix

 }

+\examples{

+set.seed(42)

+mat <- matrix(rpois(500, lambda = 5), ncol=10)

+colnames(mat) <- paste("X", 1:ncol(mat), sep="")

+obj <- SconeExperiment(mat,

+         qc = cbind(colSums(mat),colSums(mat > 0)))

+qc = get_qc(obj)

+

+}

@@ -2,7 +2,6 @@

 % Please edit documentation in R/AllGenerics.R, R/helper.R

 \docType{methods}

 \name{get_scores}

-\alias{get_score,SconeExperiment-method}

 \alias{get_score_ranks}

 \alias{get_score_ranks,SconeExperiment-method}

 \alias{get_scores}

@@ -18,7 +17,7 @@ get_score_ranks(x)

 \S4method{get_score_ranks}{SconeExperiment}(x)

 }

 \arguments{

-\item{x}{an object of class \code{\link{sconeExperiment}}.}

+\item{x}{an object of class \code{\link{SconeExperiment}}.}

 }

 \value{

 \code{get_scores} returns a matrix with all (non-missing) scone 

@@ -30,10 +29,11 @@ get_score_ranks(x)

 Extract scone scores

 }

 \examples{

+set.seed(42)

 mat <- matrix(rpois(500, lambda = 5), ncol=10)

 colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-obj <- sconeExperiment(mat)

-res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

+obj <- SconeExperiment(mat)

+res <- scone(obj, scaling=list(none=identity, uq=UQ_FN),

            evaluate=TRUE, k_ruv=0, k_qc=0, 

            eval_kclust=2, bpparam = BiocParallel::SerialParam())

 scores = get_scores(res)

@@ -155,8 +155,7 @@ In all cases, the normalized matrices can be retrieved via the

 mat <- matrix(rpois(1000, lambda = 5), ncol=10)

 colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-obj <- sconeExperiment(mat)

-

+obj <- SconeExperiment(mat)

 no_results <- scone(obj, scaling=list(none=identity,

            uq=UQ_FN, deseq=DESEQ_FN),

            run=FALSE, k_ruv=0, k_qc=0, eval_kclust=2)

@@ -47,7 +47,7 @@ of a variety of normalization schemes.

 set.seed(101)

 mat <- matrix(rpois(1000, lambda = 5), ncol=10)

 colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-obj <- sconeExperiment(mat)

+obj <- SconeExperiment(mat)

 res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

            evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2, 

            bpparam = BiocParallel::SerialParam())

@@ -128,7 +128,7 @@ Wrapper for Running Essential SCONE Modules

 set.seed(101)

 mat <- matrix(rpois(1000, lambda = 5), ncol=10)

 colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-obj <- sconeExperiment(mat)

+obj <- SconeExperiment(mat)

 res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

            evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2, 

            bpparam = BiocParallel::SerialParam())

@@ -52,10 +52,11 @@ string must be a subset of the \code{row.names} of the slot

 according to the scone ranking.

 }}

 \examples{

+set.seed(42)

 mat <- matrix(rpois(500, lambda = 5), ncol=10)

 colnames(mat) <- paste("X", 1:ncol(mat), sep="")

-obj <- sconeExperiment(mat)

-res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

+obj <- SconeExperiment(mat)

+res <- scone(obj, scaling=list(none=identity, uq=UQ_FN),

            evaluate=TRUE, k_ruv=0, k_qc=0, 

            eval_kclust=2, bpparam = BiocParallel::SerialParam())

 select_res = select_methods(res,1:2)

@@ -11,7 +11,7 @@ test_that("all back-ends work", {

   negcon_ruv <- c(rep(TRUE, 100), rep(FALSE, NROW(e)-100))

-  obj <- sconeExperiment(e, bio=bio, batch=batch, qc=qc_mat, negcon_ruv=negcon_ruv)

+  obj <- SconeExperiment(e, bio=bio, batch=batch, qc=qc_mat, negcon_ruv=negcon_ruv)

   # serial

   res1 <- scone(obj, imputation=list(none=impute_null),

@@ -10,7 +10,7 @@ test_that("get_normalized works in all three modes", {

   bio <- gl(2, 5)

   batch <- as.factor(rep(1:2, 5))

-  obj <- sconeExperiment(e, qc=qc_mat,

+  obj <- SconeExperiment(e, qc=qc_mat,

                          negcon_ruv=c(rep(TRUE, 100), rep(FALSE, NROW(e)-100)),

                          bio = as.factor(bio), batch=as.factor(batch))

@@ -10,7 +10,7 @@ test_that("get_normalized works in all three modes", {

   bio <- gl(2, 5)

   batch <- as.factor(rep(1:2, 5))

-  obj <- sconeExperiment(e, qc=qc_mat,

+  obj <- SconeExperiment(e, qc=qc_mat,

                          negcon_ruv=c(rep(TRUE, 100), rep(FALSE, NROW(e)-100)),

                          bio = as.factor(bio), batch=as.factor(batch))

@@ -68,7 +68,7 @@ test_that("get_normalized works in all three modes with nested model", {

   bio <- gl(2, 5)

   batch <- as.factor(c(1,2,1,2,1,3,4,3,4,3))