@@ -1,11 +1,12 @@

 Package: singleCellTK

 Type: Package

 Title: Interactive Analysis of Single Cell RNA-Seq Data

-Version: 0.1.8

+Version: 0.1.999

 Author: David Jenkins

 Maintainer: David Jenkins <[email protected]>

 Depends:

-    R (>= 3.2)

+    R (>= 3.2),

+    SingleCellExperiment

 Description: Run common single cell analysis directly through your browser

     including differential expression, downsampling analysis, and clustering.

 License: MIT + file LICENSE

@@ -38,11 +39,10 @@ Imports:

     rsvd,

     Rtsne,

     S4Vectors,

-    scater,

     shiny,

     shinyjs

 RoxygenNote: 6.0.1

-Suggests: 

+Suggests:

     Rsubread,

     knitr,

     rmarkdown

@@ -5,7 +5,7 @@ export(MAST)

 export(MASTregression)

 export(MASTviolin)

 export(alignSingleCellData)

-export(createSCESet)

+export(createSCE)

 export(differentialPower)

 export(filterSCData)

 export(getBiomarker)

@@ -22,6 +22,7 @@ export(plot_d3DiffEx)

 export(runDimRed)

 export(runPCA)

 export(scDiffEx)

+export(scDiffEx_anova)

 export(scDiffEx_deseq)

 export(scDiffEx_deseq2)

 export(scDiffEx_limma)

@@ -1,4 +1,5 @@

-#' Example Single Cell RNA-Seq data in SCESet Object, GSE60361 subest

+#' Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE60361

+#' subest

 #'

 #' A subset of 30 samples from a single cell RNA-Seq experiment from Zeisel, et

 #' al. Science 2015. The data was produced from cells from the mouse

@@ -6,93 +7,67 @@

 #' identified as oligodendrocytes and 15 of the cell were identified as

 #' microglia.

 #'

-#' @name GSE60361_subset

+#' @name GSE60361_subset_sce

 #' @docType data

-#' @format List of two data frames, with counts and annotations. Use them as

-#' input to createSCESet()

+#' @format SingleCellExperiment

 #' @source DOI: 10.1126/science.aaa1934

 #' @keywords datasets

 #' @examples

-#' library(scater)

-#' data("GSE60361_subset")

-#' GSE60361_SCESet <- createSCESet(countfile = GSE60361_subset$counts,

-#'                                 annotfile = GSE60361_subset$annot,

-#'                                 inputdataframes = TRUE)

-"GSE60361_subset"

+#' data("GSE60361_subset_sce")

+"GSE60361_subset_sce"

-#' Example Single Cell RNA-Seq data in SCESet Object, GSE73121

+#' Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE73121

 #'

 #' 117 Single-cell transcriptome profiling for metastatic renal cell carcinoma

 #' patient-derived cells

 #'

-#' @name GSE73121

+#' @name GSE73121_sce

 #' @docType data

-#' @format List of two data frames, with counts and annotations. Use them as

-#' input to createSCESet()

+#' @format SingleCellExperiment

 #' @source DOI: 10.1186/s13059-016-0945-9

 #' @keywords datasets

 #' @examples

-#' library(scater)

-#' data("GSE73121")

-#' GSE73121_SCESet <- createSCESet(countfile = GSE73121$counts,

-#'                                 annotfile = GSE73121$annot,

-#'                                 inputdataframes = TRUE)

-"GSE73121"

+#' data("GSE73121_sce")

+"GSE73121_sce"

-#' Example Single Cell RNA-Seq data in SCESet Object, GSE66507

+#' Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE66507

 #'

 #' 30 Single-cells from embryonic stem cells separated into three different

 #' tissue types.

 #'

-#' @name GSE66507

+#' @name GSE66507_sce

 #' @docType data

-#' @format List of two data frames, with counts and annotations. Use them as

-#' input to createSCESet()

+#' @format SingleCellExperiment

 #' @source DOI: 10.1242/dev.123547

 #' @keywords datasets

 #' @examples

-#' library(scater)

-#' data("GSE66507")

-#' GSE66507_SCESet <- createSCESet(countfile = GSE66507$counts,

-#'                                 annotfile = GSE66507$annot,

-#'                                 inputdataframes = TRUE)

-"GSE66507"

+#' data("GSE66507_sce")

+"GSE66507_sce"

-#' Example Single Cell RNA-Seq data in SCESet Object, GSE36552

+#' Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE36552

 #'

 #' 124 Single-cell transcriptome profiling from embryonic stem cells derived

 #' from donated human pre-implatation embryos.

 #'

-#' @name GSE36552

+#' @name GSE36552_sce

 #' @docType data

-#' @format List of two data frames, with counts and annotations. Use them as

-#' input to createSCESet()

+#' @format SingleCellExperiment

 #' @source DOI: 10.1038/nsmb.2660

 #' @keywords datasets

 #' @examples

-#' library(scater)

-#' data("GSE36552")

-#' GSE36552_SCESet <- createSCESet(countfile = GSE36552$counts,

-#'                                 annotfile = GSE36552$annot,

-#'                                 inputdataframes = TRUE)

-"GSE36552"

+#' data("GSE36552_sce")

+"GSE36552_sce"

 #' Example Single Cell RNA-Seq MAITS data from MAST package

 #'

 #' 96 Single-cell transcriptome profiling from Mucosal Associated Invariant T

 #' cells (MAITs), measured on the Fluidigm C1.

 #'

-#' @name maits_SCESet

+#' @name maits_sce

 #' @docType data

-#' @format List of three data frames, with counts, features and annotations.

-#' Use them as input to createSCESet()

+#' @format SingleCellExperiment

 #' @source DOI: 10.1186/s13059-015-0844-5

 #' @keywords datasets

 #' @examples

-#' library(scater)

-#' data("maits_SCESet")

-#' maits_SCESet <- createSCESet(countfile = maits_SCESet$counts,

-#'                              annotfile = maits_SCESet$annot,

-#'                              featurefile = maits_SCESet$features,

-#'                              inputdataframes = TRUE)

-"maits_SCESet"

+#' data("maits_sce")

+"maits_sce"

@@ -1,8 +1,8 @@

-#' Summarize SCESet

+#' Summarize SingleCellExperiment

 #'

-#' Creates a table of summary metrics from an input SCESet.

+#' Creates a table of summary metrics from an input SingleCellExperiment.

 #'

-#' @param indata Input SCESet

+#' @param indata Input SingleCellExperiment

 #'

 #' @return A data.frame object of summary metrics.

 #' @export summarizeTable

@@ -15,16 +15,16 @@ summarizeTable <- function(indata){

                                "Genes with no expression across all samples"),

                     "Value" = c(ncol(indata),

                               nrow(indata),

-                              as.integer(mean(apply(scater::counts(indata), 2, function(x) sum(x)))),

-                              as.integer(mean(apply(scater::counts(indata), 2, function(x) sum(x > 0)))),

-                              sum(apply(scater::counts(indata), 2, function(x) sum(as.numeric(x) == 0)) < 1700),

-                              sum(rowSums(scater::counts(indata)) == 0))))

+                              as.integer(mean(apply(assay(indata, "counts"), 2, function(x) sum(x)))),

+                              as.integer(mean(apply(assay(indata, "counts"), 2, function(x) sum(x > 0)))),

+                              sum(apply(assay(indata, "counts"), 2, function(x) sum(as.numeric(x) == 0)) < 1700),

+                              sum(rowSums(assay(indata, "counts")) == 0))))

 }

-#' Create a SCESet object

+#' Create a SingleCellExperiment object

 #'

-#' From a file of counts and a file of annotation information, create a SCESet

-#' object.

+#' From a file of counts and a file of annotation information, create a

+#' SingleCellExperiment object.

 #'

 #' @param countfile The path to a text file that contains a header row of sample

 #' names, and rows of raw counts per gene for those samples.

@@ -37,16 +37,15 @@ summarizeTable <- function(indata){

 #' @param inputdataframes If TRUE, countfile and annotfile are read as data

 #' frames instead of file paths. The default is FALSE.

 #'

-#' @return a SCESet object

-#' @export createSCESet

+#' @return a SingleCellExperiment object

+#' @export createSCE

 #' @examples

-#' library(scater)

-#' data("GSE60361_subset")

-#' GSE60361_SCESet <- createSCESet(countfile = GSE60361_subset$counts,

-#'                                 annotfile = GSE60361_subset$annot,

-#'                                 inputdataframes = TRUE)

-createSCESet <- function(countfile=NULL, annotfile=NULL, featurefile=NULL,

-                         inputdataframes=FALSE){

+#' \dontrun{

+#' GSE60361_sce <- createSCE(countfile = "/path/to/input_counts.txt",

+#'                           annotfile = "/path/to/input_annots.txt")

+#'}

+createSCE <- function(countfile=NULL, annotfile=NULL, featurefile=NULL,

+                      inputdataframes=FALSE){

   if (is.null(countfile)){

     stop("You must supply a count file.")

   }

@@ -66,15 +65,16 @@ createSCESet <- function(countfile=NULL, annotfile=NULL, featurefile=NULL,

   if (is.null(annotfile)){

     annotin <- data.frame(row.names = colnames(countsin))

     annotin$Sample <- rownames(annotin)

+    annotin <- DataFrame(annotin)

   }

   if (is.null(featurefile)){

     featurein <- data.frame(Gene = rownames(countsin))

     rownames(featurein) <- featurein$Gene

+    featurein <- DataFrame(featurein)

   }

-  pd <- methods::new("AnnotatedDataFrame", data = annotin)

-  fd <- methods::new("AnnotatedDataFrame", data = featurein)

-  return(scater::newSCESet(countData = countsin, phenoData = pd,

-                           featureData = fd))

+  return(SummarizedExperiment(assays=list(counts=as.matrix(countsin)),

+                              colData=annotin,

+                              rowData=featurein))

 }

 #' Filter Genes and Samples from a Single Cell Object

@@ -92,22 +92,18 @@ createSCESet <- function(countfile=NULL, annotfile=NULL, featurefile=NULL,

 #' @export filterSCData

 #'

 #' @examples

-#' library(scater)

-#' data("GSE60361_subset")

-#' GSE60361_SCESet <- createSCESet(countfile = GSE60361_subset$counts,

-#'                                 annotfile = GSE60361_subset$annot,

-#'                                 inputdataframes = TRUE)

-#' GSE60361_SCESet_filtered <- filterSCData(GSE60361_SCESet,

-#'                                          deletesamples="X1772063061_G11")

+#' data("GSE60361_subset_sce")

+#' GSE60361_subset_sce <- filterSCData(GSE60361_subset_sce,

+#'                                     deletesamples="X1772063061_G11")

 filterSCData <- function(insceset, deletesamples=NULL, remove_noexpress=TRUE,

                          remove_bottom=0.5, minimum_detect_genes=1700){

   insceset <- insceset[, !(colnames(insceset) %in% deletesamples)]

   if (remove_noexpress){

-    insceset <- insceset[rowSums(counts(insceset)) != 0, ]

+    insceset <- insceset[rowSums(assay(insceset, "counts")) != 0, ]

   }

   nkeeprows <- ceiling((1 - remove_bottom) * as.numeric(nrow(insceset)))

-  tokeeprow <- order(rowSums(counts(insceset)), decreasing = TRUE)[1:nkeeprows]

-  tokeepcol <- apply(counts(insceset), 2, function(x) sum(as.numeric(x) == 0)) >= minimum_detect_genes

+  tokeeprow <- order(rowSums(assay(insceset, "counts")), decreasing = TRUE)[1:nkeeprows]

+  tokeepcol <- apply(assay(insceset, "counts"), 2, function(x) sum(as.numeric(x) == 0)) >= minimum_detect_genes

   insceset <- insceset[tokeeprow, tokeepcol]

   return(insceset)

 }

deleted file mode 100644

@@ -1,26 +0,0 @@

-% Generated by roxygen2: do not edit by hand

-% Please edit documentation in R/data.R

-\docType{data}

-\name{GSE36552}

-\alias{GSE36552}

-\title{Example Single Cell RNA-Seq data in SCESet Object, GSE36552}

-\format{List of two data frames, with counts and annotations. Use them as

-input to createSCESet()}

-\source{

-DOI: 10.1038/nsmb.2660

-}

-\usage{

-GSE36552

-}

-\description{

-124 Single-cell transcriptome profiling from embryonic stem cells derived

-from donated human pre-implatation embryos.

-}

-\examples{

-library(scater)

-data("GSE36552")

-GSE36552_SCESet <- createSCESet(countfile = GSE36552$counts,

-                                annotfile = GSE36552$annot,

-                                inputdataframes = TRUE)

-}

-\keyword{datasets}

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/data.R

+\docType{data}

+\name{GSE36552_sce}

+\alias{GSE36552_sce}

+\title{Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE36552}

+\format{SingleCellExperiment}

+\source{

+DOI: 10.1038/nsmb.2660

+}

+\usage{

+GSE36552_sce

+}

+\description{

+124 Single-cell transcriptome profiling from embryonic stem cells derived

+from donated human pre-implatation embryos.

+}

+\examples{

+data("GSE36552_sce")

+}

+\keyword{datasets}

similarity index 51%

rename from man/GSE60361_subset.Rd

rename to man/GSE60361_subset_sce.Rd

@@ -1,16 +1,16 @@

 % Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/data.R

 \docType{data}

-\name{GSE60361_subset}

-\alias{GSE60361_subset}

-\title{Example Single Cell RNA-Seq data in SCESet Object, GSE60361 subest}

-\format{List of two data frames, with counts and annotations. Use them as

-input to createSCESet()}

+\name{GSE60361_subset_sce}

+\alias{GSE60361_subset_sce}

+\title{Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE60361

+subest}

+\format{SingleCellExperiment}

 \source{

 DOI: 10.1126/science.aaa1934

 }

 \usage{

-GSE60361_subset

+GSE60361_subset_sce

 }

 \description{

 A subset of 30 samples from a single cell RNA-Seq experiment from Zeisel, et

@@ -20,10 +20,6 @@ identified as oligodendrocytes and 15 of the cell were identified as

 microglia.

 }

 \examples{

-library(scater)

-data("GSE60361_subset")

-GSE60361_SCESet <- createSCESet(countfile = GSE60361_subset$counts,

-                                annotfile = GSE60361_subset$annot,

-                                inputdataframes = TRUE)

+data("GSE60361_subset_sce")

 }

 \keyword{datasets}

deleted file mode 100644

@@ -1,26 +0,0 @@

-% Generated by roxygen2: do not edit by hand

-% Please edit documentation in R/data.R

-\docType{data}

-\name{GSE66507}

-\alias{GSE66507}

-\title{Example Single Cell RNA-Seq data in SCESet Object, GSE66507}

-\format{List of two data frames, with counts and annotations. Use them as

-input to createSCESet()}

-\source{

-DOI: 10.1242/dev.123547

-}

-\usage{

-GSE66507

-}

-\description{

-30 Single-cells from embryonic stem cells separated into three different

-tissue types.

-}

-\examples{

-library(scater)

-data("GSE66507")

-GSE66507_SCESet <- createSCESet(countfile = GSE66507$counts,

-                                annotfile = GSE66507$annot,

-                                inputdataframes = TRUE)

-}

-\keyword{datasets}

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/data.R

+\docType{data}

+\name{GSE66507_sce}

+\alias{GSE66507_sce}

+\title{Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE66507}

+\format{SingleCellExperiment}

+\source{

+DOI: 10.1242/dev.123547

+}

+\usage{

+GSE66507_sce

+}

+\description{

+30 Single-cells from embryonic stem cells separated into three different

+tissue types.

+}

+\examples{

+data("GSE66507_sce")

+}

+\keyword{datasets}

deleted file mode 100644

@@ -1,26 +0,0 @@

-% Generated by roxygen2: do not edit by hand

-% Please edit documentation in R/data.R

-\docType{data}

-\name{GSE73121}

-\alias{GSE73121}

-\title{Example Single Cell RNA-Seq data in SCESet Object, GSE73121}

-\format{List of two data frames, with counts and annotations. Use them as

-input to createSCESet()}

-\source{

-DOI: 10.1186/s13059-016-0945-9

-}

-\usage{

-GSE73121

-}

-\description{

-117 Single-cell transcriptome profiling for metastatic renal cell carcinoma

-patient-derived cells

-}

-\examples{

-library(scater)

-data("GSE73121")

-GSE73121_SCESet <- createSCESet(countfile = GSE73121$counts,

-                                annotfile = GSE73121$annot,

-                                inputdataframes = TRUE)

-}

-\keyword{datasets}

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/data.R

+\docType{data}

+\name{GSE73121_sce}

+\alias{GSE73121_sce}

+\title{Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE73121}

+\format{SingleCellExperiment}

+\source{

+DOI: 10.1186/s13059-016-0945-9

+}

+\usage{

+GSE73121_sce

+}

+\description{

+117 Single-cell transcriptome profiling for metastatic renal cell carcinoma

+patient-derived cells

+}

+\examples{

+data("GSE73121_sce")

+}

+\keyword{datasets}

similarity index 65%

rename from man/createSCESet.Rd

rename to man/createSCE.Rd

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

 % Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/misc_functions.R

-\name{createSCESet}

-\alias{createSCESet}

-\title{Create a SCESet object}

+\name{createSCE}

+\alias{createSCE}

+\title{Create a SingleCellExperiment object}

 \usage{

-createSCESet(countfile = NULL, annotfile = NULL, featurefile = NULL,

+createSCE(countfile = NULL, annotfile = NULL, featurefile = NULL,

   inputdataframes = FALSE)

 }

 \arguments{

@@ -20,20 +20,18 @@ annotation information for each gene in the count matrix. This file should

 have the same genes in the same order as countfile. This is optional.}

 \item{inputdataframes}{If TRUE, countfile and annotfile are read as data

-frames instead of file paths. The default is FALSE.

-instead of}

+frames instead of file paths. The default is FALSE.}

 }

 \value{

-a SCESet object

+a SingleCellExperiment object

 }

 \description{

-From a file of counts and a file of annotation information, create a SCESet

-object.

+From a file of counts and a file of annotation information, create a

+SingleCellExperiment object.

 }

 \examples{

-library(scater)

-data("GSE60361_subset")

-GSE60361_SCESet <- createSCESet(countfile = GSE60361_subset$counts,

-                                annotfile = GSE60361_subset$annot,

-                                inputdataframes = TRUE)

+\dontrun{

+GSE60361_sce <- createSCE(countfile = "/path/to/input_counts.txt",

+                          annotfile = "/path/to/input_annots.txt")

+}

 }

@@ -28,11 +28,7 @@ The filtered single cell object.

 Filter Genes and Samples from a Single Cell Object

 }

 \examples{

-library(scater)

-data("GSE60361_subset")

-GSE60361_SCESet <- createSCESet(countfile = GSE60361_subset$counts,

-                                annotfile = GSE60361_subset$annot,

-                                inputdataframes = TRUE)

-GSE60361_SCESet_filtered <- filterSCData(GSE60361_SCESet,

-                                         deletesamples="X1772063061_G11")

+data("GSE60361_subset_sce")

+GSE60361_subset_sce <- filterSCData(GSE60361_subset_sce,

+                                    deletesamples="X1772063061_G11")

 }

@@ -2,9 +2,7 @@

 % Please edit documentation in R/getBiomarker.R

 \name{getBiomarker}

 \alias{getBiomarker}

-\title{get Biomarker

-  

-Use this function to get expression or binary data of gene list}

+\title{get Biomarker}

 \usage{

 getBiomarker(count_data, gene, binary = "Binary")

 }

@@ -19,7 +17,5 @@ getBiomarker(count_data, gene, binary = "Binary")

 A PCA plot

 }

 \description{

-get Biomarker

-  

 Use this function to get expression or binary data of gene list

 }

@@ -2,11 +2,7 @@

 % Please edit documentation in R/getPCA.R

 \name{getPCA}

 \alias{getPCA}

-\title{Get PCA components for a feature counts table

-  

-Selects the 500 most variable genes in the feature count, performs

-PCA based on them and outputs the principal components in a data frame

-and their variances as percentVar attribute}

+\title{Get PCA components for a feature counts table}

 \usage{

 getPCA(count_data)

 }

@@ -17,8 +13,6 @@ getPCA(count_data)

 A reduced dimension object

 }

 \description{

-Get PCA components for a feature counts table

-  

 Selects the 500 most variable genes in the feature count, performs

 PCA based on them and outputs the principal components in a data frame

 and their variances as percentVar attribute

@@ -2,11 +2,7 @@

 % Please edit documentation in R/getTSNE.R

 \name{getTSNE}

 \alias{getTSNE}

-\title{Get t-SNE components for a feature counts table

-  

-Selects the 500 most variable genes in the feature count, performs

-t-SNE based on them and outputs the principal components in a data frame

-and their variances as percentVar attribute}

+\title{Get t-SNE components for a feature counts table}

 \usage{

 getTSNE(count_data)

 }

@@ -17,8 +13,6 @@ getTSNE(count_data)

 A reduced dimension object

 }

 \description{

-Get t-SNE components for a feature counts table

-  

 Selects the 500 most variable genes in the feature count, performs

 t-SNE based on them and outputs the principal components in a data frame

 and their variances as percentVar attribute

deleted file mode 100644

@@ -1,27 +0,0 @@

-% Generated by roxygen2: do not edit by hand

-% Please edit documentation in R/data.R

-\docType{data}

-\name{maits_SCESet}

-\alias{maits_SCESet}

-\title{Example Single Cell RNA-Seq MAITS data from MAST package}

-\format{List of three data frames, with counts, features and annotations.

-Use them as input to createSCESet()}

-\source{

-DOI: 10.1186/s13059-015-0844-5

-}

-\usage{

-maits_SCESet

-}

-\description{

-96 Single-cell transcriptome profiling from Mucosal Associated Invariant T

-cells (MAITs), measured on the Fluidigm C1.

-}

-\examples{

-library(scater)

-data("maits_SCESet")

-maits_SCESet <- createSCESet(countfile = maits_SCESet$counts,

-                             annotfile = maits_SCESet$annot,

-                             featurefile = maits_SCESet$features,

-                             inputdataframes = TRUE)

-}

-\keyword{datasets}

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/data.R

+\docType{data}

+\name{maits_sce}

+\alias{maits_sce}

+\title{Example Single Cell RNA-Seq MAITS data from MAST package}

+\format{SingleCellExperiment}

+\source{

+DOI: 10.1186/s13059-015-0844-5

+}

+\usage{

+maits_sce

+}

+\description{

+96 Single-cell transcriptome profiling from Mucosal Associated Invariant T

+cells (MAITs), measured on the Fluidigm C1.

+}

+\examples{

+data("maits_sce")

+}

+\keyword{datasets}

@@ -2,9 +2,7 @@

 % Please edit documentation in R/plotBiomarker.R

 \name{plotBiomarker}

 \alias{plotBiomarker}

-\title{get Biomarker

-  

-Use this function to get expression or binary data of gene list}

+\title{get Biomarker}

 \usage{

 plotBiomarker(count_data, gene, binary = "Binary", visual = "PCA",

   shape = "No Shape", axis_df = NULL, x = "PC1", y = "PC2")

@@ -30,7 +28,5 @@ plotBiomarker(count_data, gene, binary = "Binary", visual = "PCA",

 A Biomarker plot

 }

 \description{

-get Biomarker

-  

 Use this function to get expression or binary data of gene list

 }

@@ -2,9 +2,7 @@

 % Please edit documentation in R/plotDimRed.R

 \name{plotDimRed}

 \alias{plotDimRed}

-\title{Plot PCA or tSNE

-  

-Use this function to plot PCA or tSNE results}

+\title{Plot PCA or tSNE}

 \usage{

 plotDimRed(method, vals_method, count_data, colorClusters, pc1, pc2)

 }

@@ -25,7 +23,5 @@ plotDimRed(method, vals_method, count_data, colorClusters, pc1, pc2)

 A reduced dimension object

 }

 \description{

-Plot PCA or tSNE

-  

 Use this function to plot PCA or tSNE results

 }

@@ -2,9 +2,7 @@

 % Please edit documentation in R/plotPCA.R

 \name{plotPCA}

 \alias{plotPCA}

-\title{Plot PCA

-  

-Use this function to plot PCA or tSNE results}

+\title{Plot PCA}

 \usage{

 plotPCA(count_data, pca_df = NULL, colorBy = NULL, shape = NULL,

   pcX = "PC1", pcY = "PC2")

@@ -26,7 +24,5 @@ plotPCA(count_data, pca_df = NULL, colorBy = NULL, shape = NULL,

 A PCA plot

 }

 \description{

-Plot PCA

-  

 Use this function to plot PCA or tSNE results

 }

@@ -2,9 +2,7 @@

 % Please edit documentation in R/plotTSNE.R

 \name{plotTSNE}

 \alias{plotTSNE}

-\title{Plot TSNE

-  

-Use this function to plot PCA or tSNE results}

+\title{Plot TSNE}

 \usage{

 plotTSNE(count_data, tsne_df = NULL, colorBy = NULL, shape = NULL)

 }

@@ -21,7 +19,5 @@ plotTSNE(count_data, tsne_df = NULL, colorBy = NULL, shape = NULL)

 A TSNE plot

 }

 \description{

-Plot TSNE

-  

 Use this function to plot PCA or tSNE results

 }

@@ -2,9 +2,7 @@

 % Please edit documentation in R/runDimRed.R

 \name{runDimRed}

 \alias{runDimRed}

-\title{Run PCA or tSNE

-  

-Use this function to run limma differential expression and load an interactive D3 heatmap}

+\title{Run PCA or tSNE}

 \usage{

 runDimRed(method, count_data, colorClusters, pc1, pc2)

 }

@@ -23,7 +21,5 @@ runDimRed(method, count_data, colorClusters, pc1, pc2)

 A reduced dimension object

 }

 \description{

-Run PCA or tSNE

-  

 Use this function to run limma differential expression and load an interactive D3 heatmap

 }

@@ -2,9 +2,7 @@

 % Please edit documentation in R/runPCA.R

 \name{runPCA}

 \alias{runPCA}

-\title{Run multiple PCA approach

-  

-Use this function to run Principle component Analysis using different approach and load plot}

+\title{Run multiple PCA approach}

 \usage{

 runPCA(plot.type, method, countm, annotm, featurem, involving.variables,

   additional.variables, colorClusters)

@@ -30,7 +28,6 @@ runPCA(plot.type, method, countm, annotm, featurem, involving.variables,

 A reduced dimension object

 }

 \description{

-Run multiple PCA approach

-  

-Use this function to run Principle component Analysis using different approach and load plot

+Use this function to run Principle component Analysis using different

+approach and load plot

 }

new file mode 100644

@@ -0,0 +1,21 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/scDiffEx.R

+\name{scDiffEx_anova}

+\alias{scDiffEx_anova}

+\title{Perform ANOVA analysis}

+\usage{

+scDiffEx_anova(inSCESet, condition)

+}

+\arguments{

+\item{inSCESet}{Input SCESet object. Required}

+

+\item{condition}{The name of the condition to use for differential

+expression. Must be a name of a column from pData that contains two labels.

+Required}

+}

+\value{

+A data frame of gene names and adjusted p-values

+}

+\description{

+Returns a data frame of gene names and adjusted p-values

+}

@@ -14,7 +14,7 @@ singlecell_SVM(train_set, train_label, test_set, var, tune_para = FALSE,

 \item{test_set}{The test dataset (cells x features)}

-\item{var}{feature names used for predicting cell quality ##}

+\item{var}{feature names used for predicting cell quality}

 \item{tune_para}{boolean values determining if doing the parameter tuning}

@@ -2,16 +2,16 @@

 % Please edit documentation in R/misc_functions.R

 \name{summarizeTable}

 \alias{summarizeTable}

-\title{Summarize SCESet}

+\title{Summarize SingleCellExperiment}

 \usage{

 summarizeTable(indata)

 }

 \arguments{

-\item{indata}{Input SCESet}

+\item{indata}{Input SingleCellExperiment}

 }

 \value{

 A data.frame object of summary metrics.

 }

 \description{

-Creates a table of summary metrics from an input SCESet.

+Creates a table of summary metrics from an input SingleCellExperiment.

 }