@@ -236,6 +236,7 @@

 #' @return A \link[SingleCellExperiment]{SingleCellExperiment} object which combines all

 #' objects in sceList. The colData is merged.

 #' @examples

+#' data(scExample, package = "singleCellTK")

 #' combinedsce <- combineSCE(list(sce,sce), by.r = NULL, by.c = NULL, combined = TRUE)

 #' @export

@@ -12,6 +12,7 @@

 #' @format SingleCellExperiment

 #' @source DOI: 10.1126/science.aaa1934

 #' @keywords datasets

+#' @usage data("mouseBrainSubsetSCE")

 #' @examples

 #' data("mouseBrainSubsetSCE")

 "mouseBrainSubsetSCE"

@@ -30,6 +31,7 @@

 #' @docType data

 #' @format A \link[SingleCellExperiment]{SingleCellExperiment} object.

 #' @keywords datasets

+#' @usage data("scExample")

 #' @examples

 #' data("scExample")

 "sce"

@@ -43,7 +45,7 @@

 #' al., 2016, annotated as `'x'`. Two common cell types, `'alpha'` and

 #' `'beta'`, that could be found in both original studies with relatively

 #' large population were kept for cleaner demonstration.

-#' data('sceBatches')

+#' @usage data('sceBatches')

 "sceBatches"

 #' Stably Expressed Gene (SEG) list obect, with SEG sets for human and mouse.

@@ -55,12 +57,13 @@

 #' charactor vector.

 #' @source \code{data('segList', package='scMerge')}

 #' @keywords datasets

+#' @usage data('SEG')

 #' @examples

 #' data('SEG')

 #' humanSEG <- SEG$human

 "SEG"

-#' MSigDB gene get Cctegory table

+#' MSigDB gene get Category table

 #'

 #' A table of gene set categories that can be download from MSigDB. The

 #' categories and descriptions can be found here:

@@ -72,6 +75,7 @@

 #' @docType data

 #' @format A data.frame.

 #' @keywords datasets

+#' @usage data("msigdb_table")

 #' @examples

 #' data("msigdb_table")

 "msigdb_table"

@@ -87,6 +91,7 @@

 #' @docType data

 #' @format A list

 #' @keywords datasets

+#' @usage data("MitoGenes")

 #' @examples

 #' data("MitoGenes")

 "MitoGenes"

\ No newline at end of file

@@ -11,6 +11,7 @@

 #' the respective databases along with p-values, z-scores etc.,

 #' @export

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' enrichRSCE(mouseBrainSubsetSCE, "Cmtm5", "GO_Cellular_Component_2017")

 enrichRSCE <- function(inSCE, glist, db = NULL){

   internetConnection <- suppressWarnings(Biobase::testBioCConnection())

@@ -13,6 +13,7 @@

 #' @return getBiomarker(): A data.frame of expression values

 #' @export

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' getBiomarker(mouseBrainSubsetSCE, gene="C1qa")

 #'

 getBiomarker <- function(inSCE, gene, binary="Binary", useAssay="counts",

@@ -362,6 +362,7 @@

 #'  as the labels. If set to "none", no label will be plotted.

 #' @return a ggplot of the reduced dimension plot of coldata.

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' plotSCEDimReduceColData(

 #'   inSCE = mouseBrainSubsetSCE, colorBy = "tissue",

 #'   shape = NULL, conditionClass = "factor",

@@ -504,6 +505,7 @@ plotSCEDimReduceColData <- function(inSCE,

 #'  as the labels. If set to "none", no label will be plotted.

 #' @return a ggplot of the reduced dimension plot of feature data.

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' plotSCEDimReduceFeatures(

 #'   inSCE = mouseBrainSubsetSCE, feature = "Apoe",

 #'   shape = NULL, reducedDimName = "TSNE_counts",

@@ -656,6 +658,7 @@ plotSCEDimReduceFeatures <- function(inSCE,

 #'  as the labels. If set to "none", no label will be plotted.

 #' @return a ggplot of the reduced dimensions.

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' plotSCEScatter(

 #'   inSCE = mouseBrainSubsetSCE, legendTitle = NULL,

 #'   slot = "assays", annotation = "counts", feature = "Apoe",

@@ -972,6 +975,7 @@ plotSCEScatter <- function(inSCE,

 #'  as the labels. If set to "none", no label will be plotted.

 #' @return a ggplot of the violin plot of coldata.

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' plotSCEViolinColData(

 #'   inSCE = mouseBrainSubsetSCE,

 #'   coldata = "age", groupBy = "sex"

@@ -1132,6 +1136,7 @@ plotSCEViolinColData <- function(inSCE,

 #'  as the labels. If set to "none", no label will be plotted.

 #' @return a ggplot of the violin plot of assay data.

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' plotSCEViolinAssayData(

 #'   inSCE = mouseBrainSubsetSCE,

 #'   feature = "Apoe", groupBy = "sex"

@@ -1313,6 +1318,7 @@ plotSCEViolinAssayData <- function(inSCE,

 #'  as the labels. If set to "none", no label will be plotted.

 #' @return a ggplot of the violin plot.

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' plotSCEViolin(

 #'   inSCE = mouseBrainSubsetSCE, slotName = "assays",

 #'   itemName = "counts", feature = "Apoe", groupBy = "sex"

@@ -1572,6 +1578,7 @@ plotSCEViolin <- function(inSCE,

 #'  as the labels. If set to "none", no label will be plotted.

 #' @return a ggplot of the density plot of colData.

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' plotSCEDensityColData(

 #'   inSCE = mouseBrainSubsetSCE,

 #'   coldata = "age", groupBy = "sex"

@@ -1707,6 +1714,7 @@ plotSCEDensityColData <- function(inSCE,

 #'  as the labels. If set to "none", no label will be plotted.

 #' @return a ggplot of the density plot of assay data.

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' plotSCEDensityAssayData(

 #'   inSCE = mouseBrainSubsetSCE,

 #'   feature = "Apoe"

@@ -1863,6 +1871,7 @@ plotSCEDensityAssayData <- function(inSCE,

 #'  as the labels. If set to "none", no label will be plotted.

 #' @return a ggplot object of the density plot.

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' plotSCEDensity(

 #'   inSCE = mouseBrainSubsetSCE, slotName = "assays",

 #'   itemName = "counts", feature = "Apoe", groupBy = "sex"

@@ -2440,6 +2449,7 @@ plotBarcodeRankScatter <- function(inSCE,

 #'  Default TRUE.

 #' @return a ggplot of the barplot of coldata.

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' plotSCEBarColData(

 #'   inSCE = mouseBrainSubsetSCE,

 #'   coldata = "age", groupBy = "sex"

@@ -2546,6 +2556,7 @@ plotSCEBarColData <- function(inSCE,

 #'  Default TRUE.

 #' @return a ggplot of the barplot of assay data.

 #' @examples

+#' data("mouseBrainSubsetSCE")

 #' plotSCEBarAssayData(

 #'   inSCE = mouseBrainSubsetSCE,

 #'   feature = "Apoe", groupBy = "sex"

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

 #' \code{"reducedDim"}.

 #' @return An object of class \code{"gtable"}, combining four \code{ggplot}s.

 #' @examples

+#' data("sceBatches")

 #' sceBatches <- scaterlogNormCounts(sceBatches, "logcounts")

 #' sceBatches <- runLimmaBC(sceBatches)

 #' plotBatchCorrCompare(sceBatches, "LIMMA", condition = "cell_type")

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

 A list

 }

 \usage{

-MitoGenes

+data("MitoGenes")

 }

 \description{

 A list of gene set that contains mitochondrial genes of multiple reference

@@ -12,7 +12,7 @@ charactor vector.

 \code{data('segList', package='scMerge')}

 }

 \usage{

-SEG

+data('SEG')

 }

 \description{

 The two gene sets came from dataset called `segList` of package `scMerge`.

@@ -30,5 +30,6 @@ objects in sceList. The colData is merged.

 Combine a list of SingleCellExperiment objects as one SingleCellExperiment object

 }

 \examples{

+data(scExample, package = "singleCellTK")

 combinedsce <- combineSCE(list(sce,sce), by.r = NULL, by.c = NULL, combined = TRUE)

 }

@@ -26,5 +26,6 @@ Given a list of genes this function runs the enrichR() to perform Gene

 enrichment

 }

 \examples{

+data("mouseBrainSubsetSCE")

 enrichRSCE(mouseBrainSubsetSCE, "Cmtm5", "GO_Cellular_Component_2017")

 }

@@ -37,6 +37,7 @@ Given a list of genes and a SingleCellExperiment object, return

 the binary or continuous expression of the genes.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 getBiomarker(mouseBrainSubsetSCE, gene="C1qa")

 }

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

 DOI: 10.1126/science.aaa1934

 }

 \usage{

-mouseBrainSubsetSCE

+data("mouseBrainSubsetSCE")

 }

 \description{

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

@@ -3,12 +3,12 @@

 \docType{data}

 \name{msigdb_table}

 \alias{msigdb_table}

-\title{MSigDB gene get Cctegory table}

+\title{MSigDB gene get Category table}

 \format{

 A data.frame.

 }

 \usage{

-msigdb_table

+data("msigdb_table")

 }

 \description{

 A table of gene set categories that can be download from MSigDB. The

@@ -55,6 +55,7 @@ necessary input. Otherwise, users can also customize the input. Future

 improvement might include solution to reduce redundant UMAP calculation.

 }

 \examples{

+data("sceBatches")

 sceBatches <- scaterlogNormCounts(sceBatches, "logcounts")

 sceBatches <- runLimmaBC(sceBatches)

 plotBatchCorrCompare(sceBatches, "LIMMA", condition = "cell_type")

@@ -83,6 +83,7 @@ Visualizes values stored in the assay slot of a

  SingleCellExperiment object via a bar plot.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 plotSCEBarAssayData(

   inSCE = mouseBrainSubsetSCE,

   feature = "Apoe", groupBy = "sex"

@@ -76,6 +76,7 @@ Visualizes values stored in the colData slot of a

  SingleCellExperiment object via a bar plot.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 plotSCEBarColData(

   inSCE = mouseBrainSubsetSCE,

   coldata = "age", groupBy = "sex"

@@ -79,6 +79,7 @@ Visualizes values stored in any slot of a

  SingleCellExperiment object via a densityn plot.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 plotSCEDensity(

   inSCE = mouseBrainSubsetSCE, slotName = "assays",

   itemName = "counts", feature = "Apoe", groupBy = "sex"

@@ -76,6 +76,7 @@ Visualizes values stored in the assay slot of a

  SingleCellExperiment object via a density plot.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 plotSCEDensityAssayData(

   inSCE = mouseBrainSubsetSCE,

   feature = "Apoe"

@@ -70,6 +70,7 @@ Visualizes values stored in the colData slot of a

  SingleCellExperiment object via a density plot.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 plotSCEDensityColData(

   inSCE = mouseBrainSubsetSCE,

   coldata = "age", groupBy = "sex"

@@ -138,6 +138,7 @@ Plot results of reduced dimensions data and

  colors by annotation data stored in the colData slot.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 plotSCEDimReduceColData(

   inSCE = mouseBrainSubsetSCE, colorBy = "tissue",

   shape = NULL, conditionClass = "factor",

@@ -125,6 +125,7 @@ Plot results of reduced dimensions data and

  colors by feature data stored in the assays slot.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 plotSCEDimReduceFeatures(

   inSCE = mouseBrainSubsetSCE, feature = "Apoe",

   shape = NULL, reducedDimName = "TSNE_counts",

@@ -130,6 +130,7 @@ Plot results of reduced dimensions data of counts stored in any

 slot in the SingleCellExperiment object.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 plotSCEScatter(

   inSCE = mouseBrainSubsetSCE, legendTitle = NULL,

   slot = "assays", annotation = "counts", feature = "Apoe",

@@ -100,6 +100,7 @@ Visualizes values stored in any slot of a

  SingleCellExperiment object via a violin plot.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 plotSCEViolin(

   inSCE = mouseBrainSubsetSCE, slotName = "assays",

   itemName = "counts", feature = "Apoe", groupBy = "sex"

@@ -97,6 +97,7 @@ Visualizes values stored in the assay slot of a

  SingleCellExperiment object via a violin plot.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 plotSCEViolinAssayData(

   inSCE = mouseBrainSubsetSCE,

   feature = "Apoe", groupBy = "sex"

@@ -94,6 +94,7 @@ Visualizes values stored in the colData slot of a

  SingleCellExperiment object via a violin plot.

 }

 \examples{

+data("mouseBrainSubsetSCE")

 plotSCEViolinColData(

   inSCE = mouseBrainSubsetSCE,

   coldata = "age", groupBy = "sex"

@@ -16,7 +16,7 @@ drawing scientific conclusions.}

 A \link[SingleCellExperiment]{SingleCellExperiment} object.

 }

 \usage{

-sce

+data("scExample")

 }

 \description{

 Example Single Cell RNA-Seq data in SingleCellExperiment Object,

@@ -9,7 +9,7 @@ different batches annotated}

 An object of class \code{SingleCellExperiment} with 100 rows and 250 columns.

 }

 \usage{

-sceBatches

+data('sceBatches')

 }

 \description{

 Two batches of pancreas scRNAseq dataset are combined with their original

@@ -18,6 +18,5 @@ Two batches came from Wang, et al., 2016, annotated as `'w'`; and Xin, et

 al., 2016, annotated as `'x'`. Two common cell types, `'alpha'` and

 `'beta'`, that could be found in both original studies with relatively

 large population were kept for cleaner demonstration.

-data('sceBatches')

 }

 \keyword{datasets}