added import in NAMESPACE

NAMESPACE

@@ -31,4 +31,10 @@ export(readInput_h5ad)
 export(readInput_table)
 export(updateSparseEset)
 exportClasses(SparseExpressionSet)
-importFrom(Biobase,ExpressionSet)
+import(Biobase)
+import(ggplot2)
+import(igraph)
+import(limma)
+import(pheatmap)
+import(rmarkdown)
+importFrom(dplyr,filter)

R/clustering_analysis.R

@@ -178,6 +178,7 @@ addMICAoutput <- function(input_eset,
 #' @param fontsize.axis_text Integer, font size of the axis text. Default: 8.
 #'
 #' @return A UMAP or T-SNE plot. It also print the plot to screen.
+#' @import ggplot2
 #' @export
 #'
 #' @examples

R/differential_analysis.R

@@ -95,6 +95,7 @@ combinePvalVector <- function(pvals,
 #' @param use_method Character, method used for differential analysis: "`limma`" (the default), "`wilcoxon`", and "`t.test`".
 #'
 #' @return A data frame. Rows are genes/drivers, and columns are 11 statistics of differential analysis.
+#' @import limma
 #'
 #' @examples
 #' ## to call this function

R/manipulate_sparseEset.R

@@ -3,7 +3,7 @@
 #'
 #' @title SparseExpressionSet
 #' @exportClass SparseExpressionSet
-#' @importFrom Biobase ExpressionSet
+#' @import Biobase
 methods::setClass(Class = "SparseExpressionSet",
                   contains = "ExpressionSet",
                   prototype = methods::prototype(methods::new("VersionedBiobase", versions = c(Biobase::classVersion("ExpressionSet"), SparseExpressionSet = "1.0.0" )))
@@ -267,6 +267,7 @@ combineSparseEset <- function(eset_list,
 #' @param addMetaData Logical, whether to update the meta data of features and cells based on the expression matrix. Default: `FALSE`.
 #'
 #' @return A sparse eset object with updated information
+#' @import Biobase
 #' @export
 #'
 #' @examples

R/network_analysis.R

@@ -8,6 +8,7 @@
 #' @param driver_type Character, type of drivers to be extracted: "`TF`" for transcriptional factor, "`SIG`" for signaling genes, and "`TF_SIG`" for both. Default: "`TF`".
 #'
 #' @return A vector of pre-defined driver genes
+#' @importFrom dplyr filter
 #' @export
 #'
 #' @examples
@@ -72,6 +73,7 @@ getDriverList <- function(species_type = "hg",
 #'      - a "**`SIG`**" folder containing a "**`.sig.txt`**" file: this file contains the SIG driver list.
 #'      - a bash script (**`runSJARACNe.sh`**) to run SJARACNe. Further modification is needed to run it.
 #'      - a json file (**`config_cwlexec.json`**) containing parameters to run SJARACNe.
+#' @importFrom dplyr filter
 #' @export
 #'
 #' @examples
@@ -353,6 +355,8 @@ generateSJARACNeInput <- function(input_eset,
 #' @param prefix Character or `NULL`, the character string to add in front of the html report file name. Default: `NULL`. Ignored if `generate_html` = `FALSE`.
 #'
 #' @return This function will print the statistics of several key quality metrics of network(s). If `generate_html` is set `TRUE`, it also generates a html file of quality control report and save it to the same folder of the network file (by default) or the folder specified by "`outdir`".
+#' @import igraph
+#' @import rmarkdown
 #' @export
 #'
 #' @examples

R/visualization.R

@@ -28,6 +28,7 @@
 #' @param jitter.size Numeric, size of the jittered points. Default: 0.1.
 #'
 #' @return A ggplot object with one or multiple violin plots
+#' @import ggplot2
 #' @export
 #'
 #' @examples
@@ -400,6 +401,7 @@ feature_bubbleplot <- function(input_eset,
 #' @param cluster_rows Logical, whether to cluster the rows. If `TRUE`, the rows will be clustered. If `FALSE`, the rows are displays following the order in '`features`'. Default: `FALSE`.
 #'
 #' @return Print the heatmap to screen
+#' @import pheatmap
 #' @export
 #'
 #' @examples

README.md

@@ -52,7 +52,7 @@ from [GitHub](https://github.com/) with:
 
 ``` r
 # install.packages("devtools")
-devtools::install_github("jyyulab/scMINER@dev-qpan")
+devtools::install_github("jyyulab/scMINER")
 ```
 
 ### Install MICA and SJARACNe