@@ -1,7 +1,9 @@

 Package: scone

 Version: 0.99.2

 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 high-throughput analyses.

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

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

+	high-throughput analyses.

 Authors@R: c(person("Michael", "Cole", email = "mbeloc@gmail.com",

 	          role = c("aut", "cre", "cph")),

 	     person("Davide", "Risso", email = "risso.davide@gmail.com",

@@ -43,7 +45,6 @@ Imports:

   ggplot2,

   plotly,

   reshape2,

-  scran,

   visNetwork

 Suggests:

   BiocStyle,

@@ -60,6 +61,9 @@ biocViews:

   GeneExpression,

   RNASeq,

   Software,

-  Transcriptomics

+  Transcriptomics,

+  Sequencing,

+  SingleCell,

+  Coverage

 BugReports: https://github.com/YosefLab/scone/issues

 RoxygenNote: 5.0.1

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

 export(DESEQ_FN_POS)

 export(FQT_FN)

 export(FQ_FN)

-export(SCRAN_FN)

 export(TMM_FN)

 export(UQ_FN)

 export(UQ_FN_POS)

@@ -98,8 +97,6 @@ importFrom(rhdf5,h5ls)

 importFrom(rhdf5,h5read)

 importFrom(rhdf5,h5write)

 importFrom(rhdf5,h5write.default)

-importFrom(scran,computeSumFactors)

-importFrom(scran,quickCluster)

 importFrom(shiny,br)

 importFrom(shiny,brushedPoints)

 importFrom(shiny,column)

@@ -1,71 +1,72 @@

 #' Class SconeExperiment

-#'

-#' @description Objects of this class store, at minimum, a gene

-#'   expression matrix and a set of covariates (sample metadata) useful for

-#'   running \code{\link{scone}}. These include, the quality control (QC)

-#'   metrics, 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

-#'   \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

+#' 

+#' @description Objects of this class store, at minimum, a gene expression 

+#'   matrix and a set of covariates (sample metadata) useful for running 

+#'   \code{\link{scone}}. These include, the quality control (QC) metrics,

+#'   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 

+#'   \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 

 #'   Slots for a full list).

-#'

-#' @description This object extends the

+#'   

+#' @description This object extends the 

 #'   \code{\linkS4class{SummarizedExperiment}} class.

-#'

-#' @details The QC matrix, biological class, and batch information are stored as

-#'   elements of the `colData` of the object.

-#' @details The positive and negative control genes are stored as elements of

-#'   the `rowData` of the object.

-#'

+#'   

+#' @details The QC matrix, biological class, and batch information are 

+#'   stored as elements of the `colData` of the object.

+#' @details The positive and negative control genes are stored as 

+#'   elements of the `rowData` of the object.

+#'   

 #' @import methods

 #' @import SummarizedExperiment

 #' @importClassesFrom SummarizedExperiment SummarizedExperiment

-#'

+#'   

 #' @name SconeExperiment-class

 #' @import methods

 #' @aliases SconeExperiment

-#'

+#'   

 #' @export

-#'

-#' @slot which_qc integer. Index of columns of `colData` that contain the QC

-#'   metrics.

-#' @slot which_bio integer. Index of the column of `colData` that contains the

-#'   biological classes information (it must be a factor).

-#' @slot which_batch integer. Index of the column of `colData` that contains the

-#'   batch information (it must be a factor).

-#' @slot which_negconruv integer. Index of the column of `rowData` that contains

-#'   a logical vector indicating which genes to use as negative controls to

-#'   infer the factors of unwanted variation in RUV.

-#' @slot which_negconeval integer. Index of the column of `rowData` that

+#' 

+#' @slot which_qc integer. Index of columns of `colData` that contain the

+#'   QC metrics.

+#' @slot which_bio integer. Index of the column of `colData` that contains

+#'   the biological classes information (it must be a factor).

+#' @slot which_batch integer. Index of the column of `colData`

+#'   that contains the batch information (it must be a factor).

+#' @slot which_negconruv integer. Index of the column of `rowData` that

 #'   contains a logical vector indicating which genes to use as negative

+#'   controls to infer the factors of unwanted variation in RUV.

+#' @slot which_negconeval integer. Index of the column of `rowData` that 

+#'   contains a logical vector indicating which genes to use as negative 

+#'   controls to evaluate the performance of the normalizations.

+#' @slot which_poscon integer. Index of the column of `rowData` that

+#'   contains a logical vector indicating which genes to use as positive

 #'   controls to evaluate the performance of the normalizations.

-#' @slot which_poscon integer. Index of the column of `rowData` that contains a

-#'   logical vector indicating which genes to use as positive controls to

-#'   evaluate the performance of the normalizations.

-#' @slot hdf5_pointer character. A string specifying to which file to write /

-#'   read the normalized data.

-#' @slot imputation_fn list of functions used by scone for the imputation step.

+#' @slot hdf5_pointer character. A string specifying to which 

+#'   file to write / read the normalized data.

+#' @slot imputation_fn list of functions used by scone for 

+#'   the imputation step.

 #' @slot scaling_fn list of functions used by scone for the scaling step.

-#' @slot scone_metrics matrix. Matrix containing the "raw" performance metrics.

-#'   See \code{\link{scone}} for a description of each metric.

-#' @slot scone_scores matrix. Matrix containing the performance scores

-#'   (transformed metrics). See \code{\link{scone}} for a discussion on the

+#' @slot scone_metrics matrix. Matrix containing the "raw" 

+#'   performance metrics. See \code{\link{scone}} for a 

+#'   description of each metric.

+#' @slot scone_scores matrix. Matrix containing the performance scores 

+#'   (transformed metrics). See \code{\link{scone}} for a discussion on the 

 #'   difference between scores and metrics.

-#' @slot scone_params data.frame. A data frame containing the normalization

-#'   schemes applied to the data and compared.

-#' @slot scone_run character. Whether \code{\link{scone}} was run and in

-#'   which mode ("no", "in_memory", "hdf5").

+#' @slot scone_params data.frame. A data frame containing

+#'   the normalization schemes applied to the data and compared.

+#' @slot scone_run character. Whether \code{\link{scone}} was 

+#'   run and in which mode ("no", "in_memory", "hdf5").

 #' @slot is_log logical. Are the expression data in log scale?

-#' @slot nested logical. Is batch nested within bio? (Automatically set by

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

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

+#' @slot nested logical. Is batch nested within bio? 

+#'   (Automatically set by \code{\link{scone}}).

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

 #'   \code{rezero=TRUE}.

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

-#'

+#'   

 setClass(

   Class = "SconeExperiment",

   contains = "SummarizedExperiment",

@@ -130,7 +131,8 @@ setValidity("SconeExperiment", function(object) {

     return("Only one set of negative controls for RUV can be specified.")

   }

   if(length(object@which_negconeval) > 1) {

-    return("Only one set of negative controls for evaluation can be specified.")

+    return(paste0("Only one set of negative controls ",

+                  "for evaluation can be specified."))

   }

   if(length(object@which_poscon) > 1) {

     return("Only one set of positive controls can be specified.")

@@ -180,7 +182,8 @@ setValidity("SconeExperiment", function(object) {

       return(paste0("File ", object@hdf5_pointer, " not found."))

     }

     if(object@scone_run == "no" && file.exists(object@hdf5_pointer)) {

-      return(paste0("File ", object@hdf5_pointer, " exists. Please specify a new file."))

+      return(paste0("File ", object@hdf5_pointer,

+                    " exists. Please specify a new file."))

     }

   }

@@ -191,17 +194,17 @@ setValidity("SconeExperiment", function(object) {

 ## Constructor

 #' @rdname SconeExperiment-class

-#'

+#'   

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

 #'   class \code{SconeExperiment}.

-#'

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

+#'   

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

 #'   containing the raw gene expression.

 #' @param ... see specific S4 methods for additional arguments.

 #' @export

-#'

+#' 

 #' @examples

-#'

+#' 

 #' nrows <- 200

 #' ncols <- 6

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

@@ -209,12 +212,12 @@ setValidity("SconeExperiment", function(object) {

 #' 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)

-#'

+#' 

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

-#'

-#'

+#' 

+#' 

 setGeneric(

   name = "sconeExperiment",

   def = function(object, ...) {

@@ -223,22 +226,22 @@ setGeneric(

 )

 #' @rdname SconeExperiment-class

-#'

-#' @param which_qc index that specifies which columns of `colData` correspond to

-#'   QC measures.

-#' @param which_bio index that specifies which column of `colData` corresponds

-#'   to `bio`.

-#' @param which_batch index that specifies which column of `colData` corresponds

-#'   to `batch`.

-#' @param which_negconruv index that specifies which column of `rowData` has

-#'   information on negative controls for RUV.

-#' @param which_negconeval index that specifies which column of `rowData` has

-#'   information on negative controls for evaluation.

-#' @param which_poscon index that specifies which column of `rowData` has

+#'   

+#' @param which_qc index that specifies which columns of `colData` 

+#'   correspond to QC measures.

+#' @param which_bio index that specifies which column of `colData`

+#'   corresponds to `bio`.

+#' @param which_batch index that specifies which column of `colData`

+#'   corresponds to `batch`.

+#' @param which_negconruv index that specifies which column of `rowData`

+#'   has information on negative controls for RUV.

+#' @param which_negconeval index that specifies which column of `rowData`

+#'   has information on negative controls for evaluation.

+#' @param which_poscon index that specifies which column of `rowData` has 

 #'   information on positive controls.

 #' @param is_log are the expression data in log scale?

 #' @export

-#'

+#' 

 setMethod(

   f = "sconeExperiment",

   signature = signature("SummarizedExperiment"),

@@ -275,18 +278,21 @@ setMethod(

 #' @rdname SconeExperiment-class

-#'

+#'   

 #' @param qc numeric matrix with the QC measures.

 #' @param bio factor with the biological class of interest.

 #' @param batch factor with the batch information.

-#' @param negcon_ruv a logical vector indicating which genes to use as negative controls for RUV.

-#' @param negcon_eval a logical vector indicating which genes to use as negative controls for evaluation.

-#' @param poscon a logical vector indicating which genes to use as positive controls.

-#'

+#' @param negcon_ruv a logical vector indicating which genes to use as negative

+#'   controls for RUV.

+#' @param negcon_eval a logical vector indicating which genes to use as 

+#'   negative controls for evaluation.

+#' @param poscon a logical vector indicating which genes to use as positive

+#'   controls.

+#'   

 #' @export

 #' 

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

-#'

+#'   

 setMethod(

   f = "sconeExperiment",

   signature = signature("matrix"),

@@ -7,36 +7,37 @@ setGeneric(

 )

 #' Retrieve Normalized Matrix

-#'

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

+#' 

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

 #' return a matrix of normalized counts (in log scale if \code{log=TRUE}).

-#'

+#' 

 #' @details If \code{\link{scone}} was run with \code{return_norm="in_memory"},

-#'   this function simply retrieves the normalized data from the \code{assays}

+#'   this function simply retrieves the normalized data from the \code{assays} 

 #'   slote of \code{object}.

-#'

+#'   

 #' @details If \code{\link{scone}} was run with \code{return_norm="hdf5"}, this

 #'   function will read the normalized matrix from the specified hdf5 file.

-#'

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

+#'   

+#' @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

-#'   the normalization method to retrieve (according to scone ranking of

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

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

+#'   the normalization method to retrieve (according to scone ranking of 

 #'   normalizations).

 #' @param ... additional arguments for specific methods.

-#'

+#'   

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

-#' 

+#'   

 #' @examples

 #' 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),

-#'            evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2, bpparam = BiocParallel::SerialParam())

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

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

 #' norm = get_normalized(res,1)

 #'            

 #' 

@@ -66,7 +67,8 @@ setGeneric(

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

 #' 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())

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

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

 #' null_design = get_design(res,1)

 #' 

 setGeneric(

@@ -77,28 +79,30 @@ setGeneric(

 )

 #' Get a subset of normalizations from a SconeExperiment object

-#'

+#' 

 #' @description This method let a user extract a subset of normalizations. This

-#'   is useful when the original dataset is large and/or many normalization

+#'   is useful when the original dataset is large and/or many normalization 

 #'   schemes have been applied.

-#'

-#' @description In such cases, the user may want to run scone in mode

-#'   \code{return_norm = "no"}, explore the results, and then select the top

+#'   

+#' @description In such cases, the user may want to run scone in mode 

+#'   \code{return_norm = "no"}, explore the results, and then select the top 

 #'   performing methods for additional exploration.

-#'

+#'   

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

-#' @param methods either character or numeric specifying the normalizations to select.

-#' 

+#' @param methods either character or numeric specifying the normalizations to

+#'   select.

+#'   

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

-#' 

+#'   

 #' @examples

 #' 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),

-#'            evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2, bpparam = BiocParallel::SerialParam())

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

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

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

-#'

+#' 

 setGeneric(

   name = "select_methods",

   def = function(x, methods) {

@@ -107,8 +111,8 @@ setGeneric(

 )

 #' Get Negative and Positive Controls

-#'

-#' @aliases get_negconeval get_poscon get_negconruv,SconeExperiment-method

+#' 

+#' @aliases get_negconeval get_poscon get_negconruv,SconeExperiment-method 

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

 setGeneric(

   name = "get_negconruv",

@@ -134,7 +138,7 @@ setGeneric(

 )

 #' Get Quality Control Matrix

-#'

+#' 

 #' @aliases get_qc,SconeExperiment-method

 setGeneric(

   name = "get_qc",

@@ -163,8 +167,8 @@ setGeneric(

 )

 #' Extract scone scores

-#'

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

+#' 

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

 #'   get_score_ranks,SconeExperiment-method

 #'   

 #' @examples

@@ -172,7 +176,8 @@ setGeneric(

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

 #' 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())

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

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

 #' scores = get_scores(res)

 #' score_ranks = get_score_ranks(res)

 #' 

@@ -192,7 +197,7 @@ setGeneric(

 )

 #' Extract scone parameters

-#'

+#' 

 #' @aliases get_params get_params,SconeExperiment-method

 #' @examples

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

@@ -1,10 +1,11 @@

 #' Upper-quartile normalization wrapper.

 #' @importFrom EDASeq betweenLaneNormalization

-#' @details SCONE scaling wrapper for \code{\link[EDASeq]{betweenLaneNormalization}}).

+#' @details SCONE scaling wrapper for

+#'   \code{\link[EDASeq]{betweenLaneNormalization}}).

 #' @export

 #' @param ei Numerical matrix. (rows = genes, cols = samples).

 #' @return Upper-quartile normalized matrix.

-#' 

+#'   

 #' @examples

 #' ei <- matrix(0:20,nrow = 7)

 #' eo <- UQ_FN(ei)

@@ -15,11 +16,12 @@ UQ_FN = function(ei){

 }

 #' Upper-quartile normalization derived from positive data.

-#' @details SCONE scaling function scales expression data by upper quartile of positive data.

+#' @details SCONE scaling function scales expression data by upper quartile of

+#'   positive data.

 #' @export

 #' @param ei Numerical matrix. (rows = genes, cols = samples).

 #' @return Upper-quartile (positive) normalized matrix.

-#' 

+#'   

 #' @examples

 #' ei <- matrix(0:20,nrow = 7)

 #' ei[1:3,] <- 0

@@ -40,11 +42,12 @@ UQ_FN_POS = function(ei){

 #' Full-quantile normalization wrapper.

 #' @importFrom aroma.light normalizeQuantileRank.matrix

-#' @details SCONE "scaling" wrapper for \code{\link[aroma.light]{normalizeQuantileRank.matrix}}).

+#' @details SCONE "scaling" wrapper for

+#'   \code{\link[aroma.light]{normalizeQuantileRank.matrix}}).

 #' @export

 #' @param ei Numerical matrix. (rows = genes, cols = samples).

 #' @return Full-quantile normalized matrix.

-#' 

+#'   

 #' @examples

 #' ei <- matrix(0:20,nrow = 7)

 #' eo <- FQ_FN(ei)

@@ -55,7 +58,8 @@ FQ_FN = function(ei){

 }

 #' @rdname FQ_FN

-#' @details FQT_FN handles ties carefully (see \code{\link[limma]{normalizeQuantiles}}).

+#' @details FQT_FN handles ties carefully (see

+#'   \code{\link[limma]{normalizeQuantiles}}).

 #' @export

 #' 

 #' @examples

@@ -69,11 +73,12 @@ FQT_FN = function(ei){

 #' DESeq size factor normalization wrapper.

 #' @importFrom DESeq estimateSizeFactorsForMatrix

-#' @details SCONE scaling wrapper for \code{\link[DESeq]{estimateSizeFactorsForMatrix}}).

+#' @details SCONE scaling wrapper for

+#'   \code{\link[DESeq]{estimateSizeFactorsForMatrix}}).

 #' @export

 #' @param ei Numerical matrix. (rows = genes, cols = samples).

 #' @return DESeq size factor normalized matrix.

-#' 

+#'   

 #' @examples

 #' ei <- matrix(0:20,nrow = 7)

 #' eo <- DESEQ_FN(ei)

@@ -85,11 +90,12 @@ DESEQ_FN = function(ei){

 }

 #' DESeq size factor normalization derived from positive data.

-#' @details SCONE scaling function scales expression data by DESeq size factor derived from positive data.

+#' @details SCONE scaling function scales expression data by DESeq size factor

+#'   derived from positive data.

 #' @export

 #' @param ei Numerical matrix. (rows = genes, cols = samples).

 #' @return DESeq size factor (positive) normalized matrix.

-#' 

+#'   

 #' @examples

 #' ei <- matrix(0:20,nrow = 7)

 #' ei[1:3,] <- 0

@@ -101,7 +107,10 @@ DESEQ_FN_POS = function(ei){

   if(!is.null(dim(ei))){

     y = ei

     y[y == 0] = NA # Matrix with zeroes replaced w/ NA

-    geom_mean = exp(apply(log(y),1,sum,na.rm = TRUE)/rowSums(!is.na(y))) # Compute Geometric Mean of Expression for Each Gene (Use positive data only)

+    geom_mean = exp(apply(log(y),1,sum,na.rm = TRUE)/rowSums(!is.na(y)))

+    # Compute Geometric Mean of Expression

+    # for Each Gene (Use positive data only)

+    

   }else{stop("Null imput matrix dimension.")}

   if(!any(geom_mean > 0)){stop("Geometric mean non-positive for all genes.")}

@@ -128,7 +137,7 @@ DESEQ_FN_POS = function(ei){

 #' @export

 #' @param ei Numerical matrix. (rows = genes, cols = samples).

 #' @return TMM normalized matrix.

-#' 

+#'   

 #' @examples

 #' ei <- matrix(0:20,nrow = 7)

 #' eo <- TMM_FN(ei)

@@ -137,23 +146,4 @@ TMM_FN = function(ei){

   size_fac = calcNormFactors(ei,method = "TMM")

   eo = t(t(ei)/size_fac)

   return(eo)

-}

-

-#' Pooled Sample normalization wrapper.

-#'

-#' @description SCONE scaling wrapper for \code{\link[scran]{computeSumFactors}}

-#'   with clusters from \code{\link[scran]{quickCluster}}.

-#'

-#' @importFrom scran computeSumFactors

-#' @importFrom scran quickCluster

-#' @export

-#' @param ei Numerical matrix. (rows = genes, cols = samples).

-#' @return SCRAN size factor normalized matrix.

-#' 

-

-SCRAN_FN = function(ei){

-  clusters <- quickCluster(ei, min.size = 20)

-  size_fac = computeSumFactors(ei, cluster=clusters)

-  eo = t(t(ei)/size_fac)

-  return(eo)

-}

+}

\ No newline at end of file

@@ -1,33 +1,38 @@

-#' Function for biplotting with no point labels and with points color-coded according 

-#' to a quantitative variable. For example: the rank of normalization performance.

-#'

+#' Function for biplotting with no point labels and with

+#' points color-coded according to a quantitative variable.

+#' For example: the rank of normalization performance.

+#' 

 #' This function implements biplot for \code{\link[stats]{prcomp}} objects.

-#'

+#' 

 #' @param x \code{\link[stats]{prcomp}} object.

-#' @param y numeric. Quantitative values used to color the points. 

-#' If rank is FALSE, all values must be positive integers and less than or equal to the length of y.

-#' @param rank logical. If TRUE (default) y will be transformed by the rank() function

+#' @param y numeric. Quantitative values used to color the points. If rank is 

+#'   FALSE, all values must be positive integers and less than or equal to the 

+#'   length of y.

+#' @param rank logical. If TRUE (default) y will be transformed by the rank() 

+#'   function

 #' @param ties_method character. ties.method used by the rank() function

-#' @param choices numeric. 2 principal components to plot. Default to first two PCs.

-#' @param expand numeric. value used to adjust the spread of the arrows relative

-#'   to the points.

+#' @param choices numeric. 2 principal components to plot. Default to first two

+#'   PCs.

+#' @param expand numeric. value used to adjust the spread of the arrows

+#'   relative to the points.

 #' @param ... arguments passed to plot.

-#'

+#'   

 #' @importFrom grDevices colorRampPalette

 #' @export

 #' 

 #' @return Invisibly returns scaled point coordinates used in plot.

-#'

+#'   

 #' @examples

 #' mat <- matrix(rnorm(1000), ncol=10)

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

-#'

+#' 

 #' pc <- prcomp(mat)

-#'

+#' 

 #' biplot_color(pc, rank(pc$x[,1]))

-#'

+#' 

 biplot_color <- function(x, y, rank = TRUE, 

-                         ties_method = c("max", "min", "first", "last", "random"),  

+                         ties_method = c("max", "min", 

+                                         "first", "last", "random"),

                          choices = 1:2, expand = 1, ...) {

   if(rank){

@@ -1,28 +1,32 @@

 #' Interactive biplot

-#'

-#' This is a wrapper around \code{\link{biplot_color}}, creating a shiny

-#' gadget to allow the user to select specific points in the graph.

-#'

-#' @details Since this is based on the shiny gadget feature, it will not work in

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

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

-#'

+#' 

+#' This is a wrapper around \code{\link{biplot_color}}, creating a shiny gadget

+#' to allow the user to select specific points in the graph.

+#' 

+#' @details Since this is based on the shiny gadget feature, it will not work

+#'   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 ... passed to \code{\link{biplot_color}}.

-#'

+#'   

 #' @importFrom miniUI gadgetTitleBar miniContentPanel miniPage gadgetTitleBar

-#' @importFrom shiny plotOutput renderPlot observeEvent brushedPoints runGadget verbatimTextOutput stopApp renderText

-#'

+#' @importFrom shiny plotOutput renderPlot observeEvent brushedPoints runGadget

+#'   verbatimTextOutput stopApp renderText

+#'   

 #' @export

 #' 

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

-#'

+#' @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)

-#' 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, bpparam = BiocParallel::SerialParam())

+#' 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,

+#'    bpparam = BiocParallel::SerialParam())

 #' \dontrun{

 #' biplot_interactive(res)

 #' }

@@ -69,7 +73,10 @@ biplot_interactive <- function(x, ...) {

     # Handle the Done button being pressed.

     observeEvent(input$done, {

       # Return the brushed points. See ?shiny::brushedPoints.

-      names <- rownames(brushedPoints(data_out, input$plot_brush, xvar="PC1", yvar="PC2"))

+      names <- rownames(brushedPoints(data_out, 

+                                      input$plot_brush, 

+                                      xvar="PC1",

+                                      yvar="PC2"))

       out <- select_methods(x, names)

       stopApp(invisible(out))

     })

@@ -1,67 +1,66 @@

 #' Data: Positive and Negative Control Genes

-#'

-#' Sets of "positive" and "negative" control genes, useful arguments for

+#' 

+#' Sets of "positive" and "negative" control genes, useful arguments for 

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

-#'

-#' These gene sets can be used as negative or positive controls, either 

-#' for RUV factor normalization or for evaluation and ranking of the 

-#' normalization workflows.

-#'

-#' @details Gene set datasets are in the form of \code{data.frame}, with 

-#'  the first column containing the gene symbols and an (optional) second 

-#'  column containing additional information (such as cortical layer or 

-#'  cell cycle phase).

-#'

-#' @details Note that the gene symbols follow the mouse conventions (i.e.

-#'  capitalized) or the human conventions (i.e, all upper-case), based on

-#'  the original publication. One can use the \code{\link[base]{toupper}},

-#'  \code{\link[base]{tolower}}, and \code{\link[tools]{toTitleCase}} 

-#'  functions to alter symbol conventions.

-#'

-#' @details Mouse gene symbols in \code{cortical_markers} are transcribed

-#'  from Figure 3 of Molyneaux et al. (2007): "laminar-specific expression

-#'  of 66 genes within the neocortex." 

 #' 

-#' @details Human gene symbols in \code{housekeeping} are derived from the

-#'  list of "housekeeping" (HK) genes from the cDNA microarray analysis of

-#'  Eisenberg and Levanon (2003): "[HK genes] belong to the class of genes

-#'  that are EXPRESSED in all tissues." "... from 47 different human tissues

-#'  and cell lines."

-#'     

-#' @details Human gene symbols in \code{housekeeping_revised} are from 

-#'  Eisenberg and Levanon (2013): "This list provided ... is based on 

-#'  analysis of next-generation sequencing (RNA-seq) data. At least one 

-#'  variant of these genes is expressed in all tissues uniformly... The 

-#'  RefSeq transcript according to which we deemed the gene 

-#'  'housekeeping' is given." Housekeeping exons satisfy "(i) expression

-#'  observed in all tissues; (ii) low variance over tissues: 

-#'  standard-deviation [log2(RPKM)]<1; and (iii) no exceptional expression

-#'  in any single tissue; that is, no log-expression value differed from

-#'  the averaged log2(RPKM) by two (fourfold) or more." "We define a 

-#'  housekeeping gene as a gene for which at least one RefSeq transcript

-#'  has more than half of its exons meeting the previous criteria (thus

-#'  being housekeeping exons)."

+#' These gene sets can be used as negative or positive controls, either for RUV

+#' factor normalization or for evaluation and ranking of the normalization

+#' workflows.

+#' 

+#' @details Gene set datasets are in the form of \code{data.frame}, with the

+#'   first column containing the gene symbols and an (optional) second column

+#'   containing additional information (such as cortical layer or cell cycle

+#'   phase).

+#'   

+#' @details Note that the gene symbols follow the mouse conventions (i.e. 

+#'   capitalized) or the human conventions (i.e, all upper-case), based on the

+#'   original publication. One can use the \code{\link[base]{toupper}}, 

+#'   \code{\link[base]{tolower}}, and \code{\link[tools]{toTitleCase}} 

+#'   functions to alter symbol conventions.

+#'   

+#' @details Mouse gene symbols in \code{cortical_markers} are transcribed from

+#'   Figure 3 of Molyneaux et al. (2007): "laminar-specific expression of 66

+#'   genes within the neocortex."

+#'   

+#' @details Human gene symbols in \code{housekeeping} are derived from the list

+#'   of "housekeeping" genes from the cDNA microarray analysis of Eisenberg

+#'   and Levanon (2003): "[HK genes] belong to the class of genes that are

+#'   EXPRESSED in all tissues." "... from 47 different human tissues and cell

+#'   lines."

 #'   

-#' @details Human gene symbols in \code{cellcycle_genes} are from Macosko et al.

-#'   (2015) and represent a set of genes marking G1/S, S, G2/M, M, and M/G1

+#' @details Human gene symbols in \code{housekeeping_revised} from Eisenberg

+#'   and Levanon (2013): "This list provided ... is based on analysis of

+#'   next-generation sequencing (RNA-seq) data. At least one variant of these

+#'   genes is expressed in all tissues uniformly... The RefSeq transcript

+#'   according to which we deemed the gene 'housekeeping' is given."

+#'   Housekeeping exons satisfy "(i) expression observed in all tissues; (ii)

+#'   low variance over tissues: standard-deviation [log2(RPKM)]<1; and (iii) no

+#'   exceptional expression in any single tissue; that is, no log-expression

+#'   value differed from the averaged log2(RPKM) by two (fourfold) or more."

+#'   "We define a housekeeping gene as a gene for which at least one RefSeq

+#'   transcript has more than half of its exons meeting the previous criteria

+#'   (thus being housekeeping exons)."

+#'   

+#' @details Human gene symbols in \code{cellcycle_genes} from Macosko et al.

+#'   (2015) and represent a set of genes marking G1/S, S, G2/M, M, and M/G1 

 #'   phases.

-#'

-#' @references Molyneaux, B.J., Arlotta, P., Menezes, J.R. and Macklis, J.D..

-#'   Neuronal subtype specification in the cerebral cortex. Nature Reviews

+#'   

+#' @references Molyneaux, B.J., Arlotta, P., Menezes, J.R. and Macklis, J.D.. 

+#'   Neuronal subtype specification in the cerebral cortex. Nature Reviews 

 #'   Neuroscience, 2007, 8(6):427-437.

-#' @references Eisenberg E, Levanon EY. Human housekeeping genes are compact.

+#' @references Eisenberg E, Levanon EY. Human housekeeping genes are compact. 

 #'   Trends in Genetics, 2003, 19(7):362-5.

-#' @references Eisenberg E, Levanon EY. Human housekeeping genes, revisited.

+#' @references Eisenberg E, Levanon EY. Human housekeeping genes, revisited. 

 #'   Trends in Genetics, 2013, 29(10):569-74.

-#' @references Macosko, E. Z., et al. Highly parallel genome-wide expression

-#'   profiling of individual cells using nanoliter droplets. Cell, 2015,

+#' @references Macosko, E. Z., et al. Highly parallel genome-wide expression 

+#'   profiling of individual cells using nanoliter droplets. Cell, 2015, 

 #'   161.5:1202-1214.

-#'

+#'   

 #' @name control_genes

-#'

+#'   

 #' @docType data

 #' @aliases cortical_markers housekeeping housekeeping_revised cellcycle_genes

-#'

+#'   

 #' @examples

 #' data(housekeeping)

 #' data(housekeeping_revised)

@@ -1,11 +1,13 @@

-#' @describeIn get_design If \code{method} is a character, it will return the

-#'   design matrix corresponding to the normalization scheme specified by the

-#'   character string. The string must be one of the \code{row.names} of the

-#'   slot \code{scone_params}.

-#'

+#' @describeIn get_design 

+#'   If 

+#'   \code{method} is a character, it will return the design

+#'   matrix corresponding to the normalization scheme specified

+#'   by the character string. The string must be one of the

+#'   \code{row.names} of the slot \code{scone_params}.

+#'   

 #' @export

-#'

-#'

+#' 

+#' 

 setMethod(

   f = "get_design",

   signature = signature(x = "SconeExperiment", method = "character"),

@@ -45,26 +47,32 @@ setMethod(

     }

-    parsed <- .parse_row(params, get_bio(x), get_batch(x), ruv_factors, qc_factors)

+    parsed <- .parse_row(params, get_bio(x), get_batch(x), 

+                         ruv_factors, qc_factors)

     design_mat <- make_design(parsed$bio, parsed$batch, parsed$W,

-                              nested=(x@nested & !is.null(parsed$bio) & !is.null(parsed$batch)))

+                              nested=(x@nested &

+                                        !is.null(parsed$bio) &

+                                        !is.null(parsed$batch)))

     return(design_mat)

   }

 )

-#' @describeIn get_design If \code{method} is a numeric, it will return the

-#'   design matrix according to the scone ranking.

-#'

-#' @details The numeric method will always return the design matrix

-#'   corresponding to row \code{method} of the \code{scone_params} slot. This

-#'   means that if \code{\link{scone}} was run with \code{eval=TRUE},

-#'   \code{get_design(x, 1)} will return the top ranked method. If

-#'   \code{\link{scone}} was run with \code{eval=FALSE}, \code{get_design(x, 1)}

-#'   will return the first normalization in the order saved by scone.

-#'

+#' @describeIn get_design

+#'   If

+#'   \code{method} is a numeric, it will return the design matrix

+#'   according to the scone ranking.

+#'