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jyyulab · Aug 27, 2024 · 8e47308 · 8e47308
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -18,7 +18,7 @@ License: Apache License (>= 2)
 Encoding: UTF-8
 LazyData: TRUE
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Depends: 
     R (>= 4.0),
     base (>= 4.2.2),

diff --git a/R/differential_analysis.R b/R/differential_analysis.R
@@ -99,7 +99,8 @@ combinePvalVector <- function(pvals,
 #'
 #' @examples
 #' ## to call this function
-#' res <- compare2groups(input_eset = input_eset,
+#' data(pbmc14k_expression.eset)
+#' res <- compare2groups(input_eset = pbmc14k_expression.eset,
 #'                       group_by = group_by,
 #'                       g1 = g1_tmp,
 #'                       g0 = g0_tmp,
@@ -203,25 +204,26 @@ compare2groups <- function(input_eset,
 #' @export
 #'
 #' @examples
+#' data(pbmc14k_expression.eset)
 #' ## 1. To perform differential expression analysis in a 1-vs-rest manner for all groups in "clusterID" column
-#' de_res <- getDE(input_eset = clustered.eset,
+#' de_res <- getDE(input_eset = pbmc14k_expression.eset,
 #'                 group_by = "clusterID",
 #'                 use_method = "limma")
 #'
 #' ## 2. To perform differential expression analysis in a 1-vs-rest manner for one specific group in "clusterID" column
-#' de_res <- getDE(input_eset = clustered.eset,
+#' de_res <- getDE(input_eset = pbmc14k_expression.eset,
 #'                 group_by = "clusterID",
 #'                 g1 = c("1"),
 #'                 use_method = "limma")
 #'
 #' ## 3. To perform differential expression analysis in a rest-vs-1 manner for one specific group in "clusterID" column
-#' de_res <- getDE(input_eset = clustered.eset,
+#' de_res <- getDE(input_eset = pbmc14k_expression.eset,
 #'                 group_by = "clusterID",
 #'                 g0 = c("1"),
 #'                 use_method = "limma")
 #'
 #' ## 4. To perform differential expression analysis in a 1-vs-1 manner for groups in "clusterID" column
-#' de_res <- getDE(input_eset = clustered.eset,
+#' de_res <- getDE(input_eset = pbmc14k_expression.eset,
 #'                 group_by = "clusterID",
 #'                 g1 = c("1"),
 #'                 g0 = c("3"),
@@ -306,6 +308,7 @@ getDE <- function(input_eset,
 #' @export
 #'
 #' @examples
+#' data(pbmc14k_expression.eset)
 #' ## 1. To perform differential activity analysis in a 1-vs-rest manner for all groups in "clusterID" column
 #' da_res <- getDA(input_eset = activity_clustered.eset,
 #'                 group_by = "clusterID",

diff --git a/R/network_analysis.R b/R/network_analysis.R
@@ -80,15 +80,16 @@ getDriverList <- function(species_type = "hg",
 #' @export
 #'
 #' @examples
+#' data(pbmc14k_expression.eset)
 #' ## 1. The most commonly used command: pre-defined driver lists, automatic down-sampling, no metacell method
-#' generateSJARACNeInput(input_eset = normalized.eset,
+#' generateSJARACNeInput(input_eset = pbmc14k_expression.eset,
 #'                       group_name = "cell_type",
 #'                       sjaracne_dir = "./SJARACNe",
 #'                       species_type = "hg",
 #'                       driver_type = "TF_SIG")
 #'
 #' ## 2. to disable the downsampling
-#' generateSJARACNeInput(input_eset = normalized.eset,
+#' generateSJARACNeInput(input_eset = pbmc14k_expression.eset,
 #'                       group_name = "cell_type",
 #'                       sjaracne_dir = "./SJARACNe",
 #'                       species_type = "hg",
@@ -98,20 +99,20 @@ getDriverList <- function(species_type = "hg",
 #' ## 3. Use the customized driver list: (add TUBB4A is the gene of interest but currently not in the pre-defined driver list)
 #'
 #' # when the driver-to-add is known as a transcription factor
-#' generateSJARACNeInput(input_eset = normalized.eset, group_name = "cell_type", sjaracne_dir = "./SJARACNe", species_type = "hg", driver_type = "TF_SIG",
+#' generateSJARACNeInput(input_eset = pbmc14k_expression.eset, group_name = "cell_type", sjaracne_dir = "./SJARACNe", species_type = "hg", driver_type = "TF_SIG",
 #'                       customDriver_TF = c(getDriverList(species_type = "hg", driver_type = "TF"), "TUBB4A"))
 #'
 #' # when the driver-to-add is known as a non-transcription factor
-#' generateSJARACNeInput(input_eset = normalized.eset, group_name = "cell_type", sjaracne_dir = "./SJARACNe", species_type = "hg", driver_type = "TF_SIG",
+#' generateSJARACNeInput(input_eset = pbmc14k_expression.eset, group_name = "cell_type", sjaracne_dir = "./SJARACNe", species_type = "hg", driver_type = "TF_SIG",
 #'                       customDriver_SIG = c(getDriverList(species_type = "hg", driver_type = "SIG"), "TUBB4A"))
 #'
 #' # when it's ambiguous to tell if the driver-to-add is a transcriptional factor
-#' generateSJARACNeInput(input_eset = normalized.eset, group_name = "cell_type", sjaracne_dir = "./SJARACNe", species_type = "hg", driver_type = "TF_SIG",
+#' generateSJARACNeInput(input_eset = pbmc14k_expression.eset, group_name = "cell_type", sjaracne_dir = "./SJARACNe", species_type = "hg", driver_type = "TF_SIG",
 #'                       customDriver_TF = c(getDriverList(species_type = "hg", driver_type = "TF"), "TUBB4A"),
 #'                       customDriver_SIG = c(getDriverList(species_type = "hg", driver_type = "SIG"), "TUBB4A"))
 #'
 #' ## 4. Use the metacell method
-#' generateSJARACNeInput(input_eset = normalized.eset, group_name = "cell_type", sjaracne_dir = "./SJARACNe", species_type = "hg", driver_type = "TF_SIG",
+#' generateSJARACNeInput(input_eset = pbmc14k_expression.eset, group_name = "cell_type", sjaracne_dir = "./SJARACNe", species_type = "hg", driver_type = "TF_SIG",
 #'                       superCell_N = 1000, superCell_count = 100, seed = 123)
 generateSJARACNeInput <- function(input_eset,
                                   group_name = "clusterID",
@@ -762,7 +763,8 @@ cal_Activity <- function(target_list = NULL, cal_mat = NULL, activity_method = '
 #' @export
 #'
 #' @examples
-#' activity_group.eset <- getActivity_individual(input_eset = group_specific.est,
+#' data(pbmc14k_expression.eset)
+#' activity_group.eset <- getActivity_individual(input_eset = pbmc14k_expression.eset,
 #'                                               network_file.tf = "consensus_network_ncol_.txt",
 #'                                               network_file.sig = "consensus_network_ncol_.txt",
 #'                                               driver_type = "TF_SIG")
@@ -860,16 +862,18 @@ getActivity_individual <- function(input_eset,
 #' @export
 #'
 #' @examples
+#' data(pbmc14k_expression.eset)
+#'
 #' ## 1. when no tag was used in runing SJARACNE: the network file folder ("sjaracne_workflow-*") is directly under TF/SIG folder of each group.
-#' activity.eset <- getActivity_inBatch(input_eset = normalized.eset,
+#' activity.eset <- getActivity_inBatch(input_eset = pbmc14k_expression.eset,
 #'                                      sjaracne_dir = "./SJARACNe",
 #'                                      group_name = "cell_type",
 #'                                      driver_type = "TF_SIG",
 #'                                      activity_method = "mean",
 #'                                      do.z_normalization = TRUE)
 #'
 #' ## 2. when tag (e.g. "bs_100" ) was used: the nework file folder ("sjaracne_workflow-*") is directly under a subfolder "bs_100" of the TF/SIG folder of each group.
-#' activity.eset <- getActivity_inBatch(input_eset = normalized.eset,
+#' activity.eset <- getActivity_inBatch(input_eset = pbmc14k_expression.eset,
 #'                                      sjaracne_dir = "./SJARACNe",
 #'                                      group_name = "cell_type",
 #'                                      network_tag.tf = "bs_100",
@@ -879,7 +883,7 @@ getActivity_individual <- function(input_eset,
 #'                                      do.z_normalization = TRUE)
 #'
 #' ## 3. to calculate the activities of TF only
-#' activity.eset <- getActivity_inBatch(input_eset = normalized.eset,
+#' activity.eset <- getActivity_inBatch(input_eset = pbmc14k_expression.eset,
 #'                                      sjaracne_dir = "./SJARACNe",
 #'                                      group_name = "cell_type",
 #'                                      network_tag.tf = "bs_100",
@@ -889,7 +893,7 @@ getActivity_individual <- function(input_eset,
 #'                                      do.z_normalization = TRUE)
 #'
 #' ## 4. to exclude some groups in the activity calculation (e.g. "NK" and "Monocyte")
-#' activity.eset <- getActivity_inBatch(input_eset = normalized.eset,
+#' activity.eset <- getActivity_inBatch(input_eset = pbmc14k_expression.eset,
 #'                                      sjaracne_dir = "./SJARACNe",
 #'                                      group_name = "cell_type",
 #'                                      group_exclude = c("NK", "Monocyte"),
@@ -900,7 +904,7 @@ getActivity_individual <- function(input_eset,
 #'                                      do.z_normalization = TRUE)
 #'
 #' ## 5. when calculate the activities from the gene expression values scaled by other methods (e.g. ScaleData() from Seurat package)
-#' activity.eset <- getActivity_inBatch(input_eset = normalized.eset,
+#' activity.eset <- getActivity_inBatch(input_eset = pbmc14k_expression.eset,
 #'                                      sjaracne_dir = "./SJARACNe",
 #'                                      group_name = "cell_type",
 #'                                      network_tag.tf = "bs_100",

diff --git a/R/visualization.R b/R/visualization.R
@@ -149,24 +149,26 @@ feature_vlnplot <- function(input_eset,
 #' @export
 #'
 #' @examples
+#' data(pbmc14k_expression.eset)
+#'
 #' ## 1. violin plots grouped by clusters (say the column name is 'clusterID')
-#' p_box <- feature_boxplot(input_eset = clustered.eset,
+#' p_box <- feature_boxplot(input_eset = pbmc14k_expression.eset,
 #'                          features = c("CD14", "CD19", "CD8A"),
 #'                          group_by = "clusterID")
 #'
 #' ## 2. violin plots grouped by cell types (say the column name is 'cellType')
-#' p_box <- feature_boxplot(input_eset = clustered.eset,
+#' p_box <- feature_boxplot(input_eset = pbmc14k_expression.eset,
 #'                          features = c("CD14", "CD19", "CD8A"),
 #'                          group_by = "cellType")
 #'
 #' ## 3. customize the colors to fill the violin plots
-#' p_box <- feature_boxplot(input_eset = clustered.eset,
+#' p_box <- feature_boxplot(input_eset = pbmc14k_expression.eset,
 #'                          features = c("CD14", "CD19", "CD8A"),
 #'                          group_by = "clusterID",
 #'                          colors = c("blue", "red", "green"))
 #'
 #' ## 4. add jittered points
-#' p_box <- feature_boxplot(input_eset = clustered.eset,
+#' p_box <- feature_boxplot(input_eset = pbmc14k_expression.eset,
 #'                          features = c("CD14", "CD19", "CD8A"),
 #'                          group_by = "clusterID",
 #'                          add_jitter = TRUE,
@@ -260,18 +262,20 @@ feature_boxplot <- function(input_eset,
 #' @export
 #'
 #' @examples
+#' data(pbmc14k_expression.eset)
+#'
 #' ## 1. scatter plots with UMAP projections
-#' feature_scatterplot(input_eset = clustered.eset,
+#' feature_scatterplot(input_eset = pbmc14k_expression.eset,
 #'                     features = c("CD14", "CD19", "CD8A"),
 #'                     location_x = "UMAP_1", location_y = "UMAP_2")
 #'
 #' ## 2. scatter plots with t-SNE projections
-#' feature_scatterplot(input_eset = clustered.eset,
+#' feature_scatterplot(input_eset = pbmc14k_expression.eset,
 #'                     features = c("CD14", "CD19", "CD8A"),
 #'                     location_x = "tSNE_1", location_y = "tSNE_2")
 #'
 #' ## 3. change the point size and font size
-#' feature_scatterplot(input_eset = clustered.eset,
+#' feature_scatterplot(input_eset = pbmc14k_expression.eset,
 #'                     features = c("CD14", "CD19", "CD8A"),
 #'                     location_x = "UMAP_1", location_y = "UMAP_2",
 #'                     point.size = 1,
@@ -356,14 +360,16 @@ feature_scatterplot <- function(input_eset,
 #' @export
 #'
 #' @examples
-#' features_of_interest <- c("CD3D","CD27","IL7R","SELL","CCR7","IL32","GZMA","GZMK","DUSP2","CD8A","GZMH","GZMB","CD79A","CD79B","CD86","CD14")
+#' data(pbmc14k_expression.eset)
+#' features_of_interest <- c("CD3D","CD27","IL7R","SELL","CCR7","IL32","GZMA","GZMK",
+#'                           "DUSP2","CD8A","GZMH","GZMB","CD79A","CD79B","CD86","CD14")
 #' ## 1. the most commonly used command
-#' feature_bubbleplot(input_eset = clustered.eset,
+#' feature_bubbleplot(input_eset = pbmc14k_expression.eset,
 #'                    features = features_of_interest,
 #'                    group_by = "clusterID")
 #'
 #' ## 2. customize the colors
-#' feature_bubbleplot(input_eset = clustered.eset,
+#' feature_bubbleplot(input_eset = pbmc14k_expression.eset,
 #'                    features = features_of_interest,
 #'                    group_by = "clusterID",
 #'                    colors = c("lightgrey", "red"))
@@ -445,32 +451,34 @@ feature_bubbleplot <- function(input_eset,
 #' @export
 #'
 #' @examples
-#' features_of_interest <- c("CD3D","CD27","IL7R","SELL","CCR7","IL32","GZMA","GZMK","DUSP2","CD8A","GZMH","GZMB","CD79A","CD79B","CD86","CD14")
+#' data(pbmc14k_expression.eset)
+#' features_of_interest <- c("CD3D","CD27","IL7R","SELL","CCR7","IL32","GZMA","GZMK",
+#'                           "DUSP2","CD8A","GZMH","GZMB","CD79A","CD79B","CD86","CD14")
 #' ## 1. the most commonly used command
-#' feature_heatmap(input_eset = clustered.eset,
+#' feature_heatmap(input_eset = pbmc14k_expression.eset,
 #'                 features = features_of_interest,
 #'                 group_by = "clusterID")
 #'
 #' ## 2. add one more column ('true_label') for cell annotation
-#' feature_heatmap(input_eset = clustered.eset,
+#' feature_heatmap(input_eset = pbmc14k_expression.eset,
 #'                 features = features_of_interest,
 #'                 group_by = "clusterID",
 #'                 annotation_columns = c("true_label"))
 #'
 #' ## 3. scale the data by row
-#' feature_heatmap(input_eset = clustered.eset,
+#' feature_heatmap(input_eset = pbmc14k_expression.eset,
 #'                 features = features_of_interest,
 #'                 group_by = "clusterID",
 #'                 scale_method = "row")
 #'
 #' ## 4. cluster the rows
-#' feature_heatmap(input_eset = clustered.eset,
+#' feature_heatmap(input_eset = pbmc14k_expression.eset,
 #'                 features = features_of_interest,
 #'                 group_by = "clusterID",
 #'                 cluster_rows = TRUE)
 #'
 #' ## 5. add gaps
-#' feature_heatmap(input_eset = clustered.eset,
+#' feature_heatmap(input_eset = pbmc14k_expression.eset,
 #'                 features = features_of_interest,
 #'                 group_by = "clusterID",
 #'                 use_gaps.column = TRUE,
@@ -554,13 +562,15 @@ feature_heatmap <- function(input_eset,
 #' @export
 #'
 #' @examples
+#' data(pbmc14k_expression.eset)
+#'
 #' ## 1. bar plot grouped by clusters ("clusterID") and colored by true labels ("true_label)
-#' draw_barplot(input_eset = clustered.eset,
+#' draw_barplot(input_eset = pbmc14k_expression.eset,
 #'              group_by = "clusterID",
 #'              color_by = "true_label")
 #'
 #' ## 2. customize the colors
-#' draw_barplot(input_eset = clustered.eset,
+#' draw_barplot(input_eset = pbmc14k_expression.eset,
 #'              group_by = "clusterID",
 #'              color_by = "true_label",
 #'              colors = c("green", "red", "blue", "grey", "orange", "purple", "yellow"))
@@ -618,16 +628,18 @@ draw_barplot <- function(input_eset,
 #' @export
 #'
 #' @examples
-#' marker_file <- system.file('PBMC14KDS_DemoDataSet/DATA/', 'Immune_signatures.xlsx', package = "scMINER")
+#' data(pbmc14k_expression.eset)
+#' marker_file <- system.file('extdata/demo_pbmc14k/PBMC14k_signatureTable.txt', package = "scMINER")
 #' signature_table <- openxlsx::read.xlsx(marker_file)
 #' head(signature_table)
+#'
 #' ## 1. the most commonly used command
-#' draw_bubbleplot(input_eset = clustered.eset,
+#' draw_bubbleplot(input_eset = pbmc14k_expression.eset,
 #'                 signature_table = signature_table,
 #'                 group_by = "clusterID")
 #'
 #' ## 2. customize the colors
-#' draw_bubbleplot(input_eset = clustered.eset,
+#' draw_bubbleplot(input_eset = pbmc14k_expression.eset,
 #'                 signature_table = signature_table,
 #'                 group_by = "clusterID",
 #'                 colors = c("lightgrey", "red"))
@@ -730,8 +742,10 @@ draw_bubbleplot <- function(input_eset,
 #' @export
 #'
 #' @examples
+#' data(pbmc14k_expression.eset)
+#'
 #' ## 1. the most commonly used command
-#' generatePortalInputs(input_expression.eset = expression_clustered.eset,
+#' generatePortalInputs(input_expression.eset = pbmc14k_expression.eset,
 #'                      group_by = "cellType",
 #'                      input_activity.eset = activity_clustered.eset,
 #'                      input_network.dir = "./SJARACNe",
@@ -748,7 +762,7 @@ draw_bubbleplot <- function(input_eset,
 #'                                             "./sjaracne/CD4Treg/TF/consensus_network_ncol_.txt",
 #'                                             "./sjaracne/B/SIG/consensus_network_ncol_.txt",
 #'                                             "./sjaracne/B/TF/consensus_network_ncol_.txt"))
-#' generatePortalInputs(input_expression.eset = expression_clustered.eset,
+#' generatePortalInputs(input_expression.eset = pbmc14k_expression.eset,
 #'                      group_by = "cellType",
 #'                      input_network.table = network.table,
 #'                      output_dir = "./path-to-output_dir")

diff --git a/man/compare2groups.Rd b/man/compare2groups.Rd
diff --git a/man/draw_barplot.Rd b/man/draw_barplot.Rd
diff --git a/man/draw_bubbleplot.Rd b/man/draw_bubbleplot.Rd