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

 ^.*\.Rproj$

 ^\.Rproj\.user$

-^docs$

 ^doc$

+^docs$

 ^_pkgdown\.yml$

 ^vignettes/webOnly/

 .travis.yml

+^doc/

+^docs/

@@ -50,7 +50,8 @@ Suggests:

     DT,

     mclust,

     scater,

-    ExPosition

+    ExPosition,

+    BiocStyle

 VignetteBuilder: knitr

 LazyData: false

 biocViews: SingleCell, GeneExpression

@@ -391,7 +391,7 @@ doWilcox <- function(exprsMat, cellTypes,

 #'

 #' @examples

 #' library(S4Vectors)

-#' data(sce_control_subset)

+#' data(sce_control_subset, package = "CiteFuse")

 #' sce_control_subset <- DEgenes(sce_control_subset,

 #' altExp_name = "none",

 #' group = sce_control_subset$SNF_W_louvain,

@@ -23,7 +23,7 @@

 #' a preprocessed expression matrix

 #'

 #' @examples

-#' data(CITEseq_example)

+#' data(CITEseq_example, package = "CiteFuse")

 #' sce_citeseq <- preprocessing(CITEseq_example)

 #'

 #' @importFrom SingleCellExperiment SingleCellExperiment altExp

@@ -312,7 +312,7 @@ readFrom10X <- function(dir,

 #' to add when log-transforming expression values. Default is 1

 #'

 #' @examples

-#' data(CITEseq_example)

+#' data(CITEseq_example, package = "CiteFuse")

 #' sce_citeseq <- preprocessing(CITEseq_example)

 #' sce_citeseq <- normaliseExprs(sce = sce_citeseq,

 #' altExp_name = "ADT",

@@ -456,7 +456,7 @@ normaliseExprs <- function(sce,

 #' @return A SingleCellExperiment Object

 #'

 #' @examples

-#' data(CITEseq_example)

+#' data(CITEseq_example, package = "CiteFuse")

 #' sce_citeseq <- preprocessing(CITEseq_example)

 #' sce_citeseq <- normaliseExprs(sce = sce_citeseq,

 #' altExp_name = "HTO",

@@ -577,7 +577,7 @@ crossSampleDoublets <- function(sce,

 #' @return A plot visualising the HTO expression

 #'

 #' @examples

-#' data(CITEseq_example)

+#' data(CITEseq_example, package = "CiteFuse")

 #' sce_citeseq <- preprocessing(CITEseq_example)

 #' sce_citeseq <- normaliseExprs(sce = sce_citeseq,

 #' altExp_name = "HTO",

@@ -737,7 +737,7 @@ plotHTOSingle <- function(sce,

 #'

 #' @examples

 #'

-#' data(CITEseq_example)

+#' data(CITEseq_example, package = "CiteFuse")

 #' sce_citeseq <- preprocessing(CITEseq_example)

 #' sce_citeseq <- normaliseExprs(sce = sce_citeseq,

 #' altExp_name = "HTO",

@@ -15,11 +15,10 @@

 #'

 #' @examples

 #'

-#' data(sce_control_subset)

+#' data(sce_control_subset, package = "CiteFuse")

 #' sce_control_subset <- CiteFuse(sce_control_subset)

 #' SNF_W <- S4Vectors::metadata(sce_control_subset)[["SNF_W"]]

-#' SNF_W_clust <- spectralClustering(SNF_W,

-#' K = 5)

+#' SNF_W_clust <- spectralClustering(SNF_W, K = 5)

 #'

 #' @importFrom igraph arpack

 #' @importFrom methods as

@@ -209,7 +208,7 @@ spectralClustering <- function(affinity, K = 20, type = 4,

 #' @return A SingleCellExperiment object

 #'

 #' @examples

-#' data(sce_control_subset)

+#' data(sce_control_subset, package = "CiteFuse")

 #' sce_control_subset <- CiteFuse(sce_control_subset)

 #' sce_control_subset <- reducedDimSNF(sce_control_subset,

 #' method = "tSNE",

@@ -296,7 +295,7 @@ reducedDimSNF <- function(sce,

 #' @return A ggplot of the reduced dimension visualisation

 #'

 #' @examples

-#' data(sce_control_subset)

+#' data(sce_control_subset, package = "CiteFuse")

 #' sce_control_subset <- CiteFuse(sce_control_subset)

 #' sce_control_subset <- reducedDimSNF(sce_control_subset,

 #' method = "tSNE",

@@ -529,7 +528,7 @@ visualiseDim <- function(sce,

 #'

 #' @examples

 #'

-#' data(sce_control_subset)

+#' data(sce_control_subset, package = "CiteFuse")

 #' sce_control_subset <- CiteFuse(sce_control_subset)

 #' SNF_W_louvain <- igraphClustering(sce_control_subset,

 #' method = "louvain")

@@ -632,7 +631,7 @@ igraphClustering <- function(sce,

 #' @return A igraph plot

 #'

 #' @examples

-#' data(sce_control_subset)

+#' data(sce_control_subset, package = "CiteFuse")

 #' sce_control_subset <- CiteFuse(sce_control_subset)

 #' SNF_W_louvain <- igraphClustering(sce_control_subset,

 #' method = "louvain")

@@ -42,7 +42,7 @@

 #' @examples

 #' library(SingleCellExperiment)

 #' set.seed(2020)

-#' data(sce_control_subset)

+#' data(sce_control_subset, package = "CiteFuse")

 #' RNA_feature_subset <- sample(rownames(sce_control_subset), 50)

 #' ADT_feature_subset <- rownames(altExp(sce_control_subset, "ADT"))

 #'

@@ -50,7 +50,7 @@

 #'                RNA_feature_subset = RNA_feature_subset,

 #'                ADT_feature_subset = ADT_feature_subset,

 #'                cor_method = "pearson",

-#'               network_layout = igraph::layout_with_fr)

+#'                network_layout = igraph::layout_with_fr)

 #'

 #' @export

@@ -24,8 +24,8 @@

 #' @importFrom S4Vectors metadata

 #'

 #' @examples

-#' data(lr_pair_subset)

-#' data(sce_control_subset)

+#' data(lr_pair_subset, package = "CiteFuse")

+#' data(sce_control_subset, package = "CiteFuse")

 #'

 #' sce_control_subset <- normaliseExprs(sce = sce_control_subset,

 #' altExp_name = "ADT",

@@ -304,8 +304,8 @@ ligandReceptorTest <- function(sce,

 #' @import ggplot2

 #'

 #' @examples

-#' data(lr_pair_subset)

-#' data(sce_control_subset)

+#' data(lr_pair_subset, package = "CiteFuse")

+#' data(sce_control_subset, package = "CiteFuse")

 #'

 #' sce_control_subset <- normaliseExprs(sce = sce_control_subset,

 #' altExp_name = "ADT",

@@ -386,8 +386,8 @@ fitMixtures <- function(vec) {

 #' @return A ggplot to visualise te features distribution

 #'

 #' @examples

-#' data(sce_control_subset)

-#' data(sce_ctcl_subset)

+#' data(sce_control_subset, package = "CiteFuse")

+#' data(sce_ctcl_subset, package = "CiteFuse")

 #' visualiseExprsList(sce_list = list(control = sce_control_subset,

 #' ctcl = sce_ctcl_subset),

 #' plot = "boxplot",

@@ -18,7 +18,7 @@ the marker for each cluster

 }

 \examples{

 library(S4Vectors)

-data(sce_control_subset)

+data(sce_control_subset, package = "CiteFuse")

 sce_control_subset <- DEgenes(sce_control_subset,

 altExp_name = "none",

 group = sce_control_subset$SNF_W_louvain,

@@ -24,7 +24,7 @@ A SingleCellExperiment Object

 A function that perform normalisation for alternative expression

 }

 \examples{

-data(CITEseq_example)

+data(CITEseq_example, package = "CiteFuse")

 sce_citeseq <- preprocessing(CITEseq_example)

 sce_citeseq <- normaliseExprs(sce = sce_citeseq,

 altExp_name = "HTO",

@@ -74,7 +74,7 @@ A function to visualise the features distribtuion

 \examples{

 library(SingleCellExperiment)

 set.seed(2020)

-data(sce_control_subset)

+data(sce_control_subset, package = "CiteFuse")

 RNA_feature_subset <- sample(rownames(sce_control_subset), 50)

 ADT_feature_subset <- rownames(altExp(sce_control_subset, "ADT"))

@@ -31,7 +31,7 @@ A function to perform igraph clustering

 }

 \examples{

-data(sce_control_subset)

+data(sce_control_subset, package = "CiteFuse")

 sce_control_subset <- CiteFuse(sce_control_subset)

 SNF_W_louvain <- igraphClustering(sce_control_subset,

 method = "louvain")

@@ -46,8 +46,8 @@ A SingleCellExperiment object with ligand receptor results

 A function to perform ligand receptor analysis

 }

 \examples{

-data(lr_pair_subset)

-data(sce_control_subset)

+data(lr_pair_subset, package = "CiteFuse")

+data(sce_control_subset, package = "CiteFuse")

 sce_control_subset <- normaliseExprs(sce = sce_control_subset,

 altExp_name = "ADT",

@@ -34,7 +34,7 @@ a SingleCellExperiment object

 A function that perform normalisation for alternative expression

 }

 \examples{

-data(CITEseq_example)

+data(CITEseq_example, package = "CiteFuse")

 sce_citeseq <- preprocessing(CITEseq_example)

 sce_citeseq <- normaliseExprs(sce = sce_citeseq,

 altExp_name = "ADT",

@@ -22,7 +22,7 @@ A plot visualising the HTO expression

 A function to plot HTO expression

 }

 \examples{

-data(CITEseq_example)

+data(CITEseq_example, package = "CiteFuse")

 sce_citeseq <- preprocessing(CITEseq_example)

 sce_citeseq <- normaliseExprs(sce = sce_citeseq,

 altExp_name = "HTO",

@@ -44,7 +44,7 @@ and filter the features that are all zeros across samples,

 and finally construct a \code{SingleCellExperiment} object

 }

 \examples{

-data(CITEseq_example)

+data(CITEseq_example, package = "CiteFuse")

 sce_citeseq <- preprocessing(CITEseq_example)

 }

@@ -26,7 +26,7 @@ A SingleCellExperiment object

 A function to reduce the dimension of the similarity matrix

 }

 \examples{

-data(sce_control_subset)

+data(sce_control_subset, package = "CiteFuse")

 sce_control_subset <- CiteFuse(sce_control_subset)

 sce_control_subset <- reducedDimSNF(sce_control_subset,

 method = "tSNE",

@@ -40,7 +40,7 @@ A function to perform spectral clustering

 }

 \examples{

-data(sce_control_subset)

+data(sce_control_subset, package = "CiteFuse")

 sce_control_subset <- CiteFuse(sce_control_subset)

 SNF_W <- S4Vectors::metadata(sce_control_subset)[["SNF_W"]]

 SNF_W_clust <- spectralClustering(SNF_W,

@@ -28,8 +28,8 @@ A plot visualise the ligand receptor results

 A function to visualise ligand receptor analysis

 }

 \examples{

-data(lr_pair_subset)

-data(sce_control_subset)

+data(lr_pair_subset, package = "CiteFuse")

+data(sce_control_subset, package = "CiteFuse")

 sce_control_subset <- normaliseExprs(sce = sce_control_subset,

 altExp_name = "ADT",

@@ -46,7 +46,7 @@ A ggplot of the reduced dimension visualisation

 A function to visualise the reduced dimension

 }

 \examples{

-data(sce_control_subset)

+data(sce_control_subset, package = "CiteFuse")

 sce_control_subset <- CiteFuse(sce_control_subset)

 sce_control_subset <- reducedDimSNF(sce_control_subset,

 method = "tSNE",

@@ -46,8 +46,8 @@ A function to visualise the features distribtuion for

 a list of SingleCellExperiment

 }

 \examples{

-data(sce_control_subset)

-data(sce_ctcl_subset)

+data(sce_control_subset, package = "CiteFuse")

+data(sce_ctcl_subset, package = "CiteFuse")

 visualiseExprsList(sce_list = list(control = sce_control_subset,

 ctcl = sce_ctcl_subset),

 plot = "boxplot",

@@ -20,7 +20,7 @@ A igraph plot

 A function to perform louvain clustering

 }

 \examples{

-data(sce_control_subset)

+data(sce_control_subset, package = "CiteFuse")

 sce_control_subset <- CiteFuse(sce_control_subset)

 SNF_W_louvain <- igraphClustering(sce_control_subset,

 method = "louvain")

@@ -23,7 +23,7 @@ doublet identification within batch

 }

 \examples{

-data(CITEseq_example)

+data(CITEseq_example, package = "CiteFuse")

 sce_citeseq <- preprocessing(CITEseq_example)

 sce_citeseq <- normaliseExprs(sce = sce_citeseq,

 altExp_name = "HTO",