diff --git a/DESCRIPTION b/DESCRIPTION
index 5c3246e..50cf012 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -17,6 +17,7 @@ Description: scMINER is a mutual information-based framework for single-cell/nuc
 License: Apache License (>= 2)
 Encoding: UTF-8
 LazyData: TRUE
+LazyDataCompression:xz
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2
 Depends: 
@@ -36,7 +37,8 @@ Imports:
     pheatmap (>= 1.0.12),
     reshape2 (>= 1.4.4),
     rmarkdown (>= 2.8),
-    sessioninfo (>= 1.2.2)
+    sessioninfo (>= 1.2.2),
+    RColorBrewer
 Suggests: 
     knitr,
     kableExtra,
diff --git a/NAMESPACE b/NAMESPACE
index fdb20d2..ed8354a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,7 +2,10 @@
 
 export(MICAplot)
 export(addMICAoutput)
+export(cal_Activity)
+export(combinePvalVector)
 export(combineSparseEset)
+export(compare2groups)
 export(createProjectSpace)
 export(createSparseEset)
 export(drawNetworkQC)
@@ -24,12 +27,15 @@ export(getDA)
 export(getDE)
 export(getDriverList)
 export(getTopFeatures)
+export(get_net2target_list)
+export(get_target_list2matrix)
 export(normalizeSparseEset)
 export(readInput_10x.dir)
 export(readInput_10x.h5)
 export(readInput_h5ad)
 export(readInput_table)
 export(updateSparseEset)
+export(z_normalization)
 exportClasses(SparseExpressionSet)
 import(Biobase)
 import(ggplot2)
diff --git a/R/differential_analysis.R b/R/differential_analysis.R
index a93a6b5..bef2edb 100644
--- a/R/differential_analysis.R
+++ b/R/differential_analysis.R
@@ -10,6 +10,7 @@
 #' @param twosided Logical, whether the input P values are two-sided. If FALSE, the input P values will be treated as one-tailed. Default: `TRUE`.
 #'
 #' @return A vector containing "Z-statistics" and "P.Value".
+#' @export
 #'
 #' @examples
 #' ## 1. combine P values from a vector
@@ -99,6 +100,7 @@ combinePvalVector <- function(pvals,
 #'
 #' @return A data frame. Rows are genes/drivers, and columns are 11 statistics of differential analysis.
 #' @import limma
+#' @export
 #'
 #' @examples
 #' ## to call this function
diff --git a/R/manipulate_sparseEset.R b/R/manipulate_sparseEset.R
index 34d82c1..9025115 100644
--- a/R/manipulate_sparseEset.R
+++ b/R/manipulate_sparseEset.R
@@ -43,7 +43,7 @@ methods::setClass(Class = "SparseExpressionSet",
 #'
 #' ## 2. Create SparseEset with customized meta data
 #' true_label <- read.table(system.file("extdata/demo_pbmc14k/PBMC14k_trueLabel.txt.gz", package = "scMINER"),
-#'                          header = T, row.names = 1, sep = "\t", quote = "", stringsAsFactors = FALSE)
+#'                          header = TRUE, row.names = 1, sep = "\t", quote = "", stringsAsFactors = FALSE)
 #' pbmc14k_raw.eset <- createSparseEset(input_matrix = pbmc14k_rawCount,
 #'                                      cellData = true_label,
 #'                                      featureData = NULL,
@@ -304,7 +304,7 @@ combineSparseEset <- function(eset_list,
 #' pbmc14k_raw.eset <- updateSparseEset(input_eset = pbmc14k_expression.eset, addMetaData = TRUE)
 #'
 #' ## 2. Update the meta data of cells: with this, the user can add more information of cells, like clustering results, cell type.
-#' true_label <- read.table(system.file("extdata/demo_pbmc14k/PBMC14k_trueLabel.txt.gz", package = "scMINER"), header = T, row.names = 1, sep = "\t", quote = "", stringsAsFactors = FALSE)
+#' true_label <- read.table(system.file("extdata/demo_pbmc14k/PBMC14k_trueLabel.txt.gz", package = "scMINER"), header = TRUE, row.names = 1, sep = "\t", quote = "", stringsAsFactors = FALSE)
 #' pbmc14k_raw.eset <- updateSparseEset(input_eset = pbmc14k_expression.eset, cellData = true_label, addMetaData = TRUE)
 #'
 updateSparseEset <- function(input_eset,
diff --git a/R/network_analysis.R b/R/network_analysis.R
index 18f8c32..668f195 100644
--- a/R/network_analysis.R
+++ b/R/network_analysis.R
@@ -617,6 +617,7 @@ drawNetworkQC <- function(network_file = NULL,
 #' @param x a vector of numbers for z-normalization
 #'
 #' @return a vector of numbers with z-normalized
+#' @export
 #'
 #' @examples z_normalization(c(1,2,3,4,5,NA,6,7,8,9))
 z_normalization <- function(x) {
@@ -634,6 +635,7 @@ z_normalization <- function(x) {
 #' @param net_dat a data frame generated from SJARANCe network file. These columns are needed: "`source`", "`target`", "`MI`" and "`spearman`".
 #'
 #' @return A list of source genes, for each source gene, the value is a data frame with "`target`", "`MI`" and "`spearman`" as the columns
+#' @export
 #'
 #' @examples
 #' net_data <-  read.table(system.file("extdata/demo_pbmc14k/SJARACNe/B/TF/bt100_pc001/consensus_network_ncol_.txt", package = "scMINER"),
@@ -676,6 +678,7 @@ get_net2target_list <- function(net_dat = NULL) {
 #'
 #' @return If "`weightedmean`" is given, it returns a matrix of signed (by Spearman correlation coefficient) mutual information, the sign of which will be used for activity calculation. For
 #' all the other methods ("`mean`", "`absmean`" or "`maxmean`"), it returns a matrix of 1.
+#' @export
 #'
 #' @examples
 #' net_data <-  read.table(system.file("extdata/demo_pbmc14k/SJARACNe/B/TF/bt100_pc001/consensus_network_ncol_.txt", package = "scMINER"),
@@ -710,6 +713,7 @@ get_target_list2matrix <- function(target_list = NULL, activity_method = 'mean')
 #' @param do.std Logical, whether to do the z-normalization on the gene expression values in each sample. Default: `TRUE`.
 #'
 #' @return a matrix of activities, drivers by cells
+#' @export
 #'
 #' @examples
 #' net_data <-  read.table(system.file("extdata/demo_pbmc14k/SJARACNe/B/TF/bt100_pc001/consensus_network_ncol_.txt", package = "scMINER"),
diff --git a/man/createSparseEset.Rd b/man/createSparseEset.Rd
index 9ea49b6..47f5160 100644
--- a/man/createSparseEset.Rd
+++ b/man/createSparseEset.Rd
@@ -53,7 +53,7 @@ pbmc14k_raw.eset <- createSparseEset(input_matrix = pbmc14k_rawCount,
 
 ## 2. Create SparseEset with customized meta data
 true_label <- read.table(system.file("extdata/demo_pbmc14k/PBMC14k_trueLabel.txt.gz", package = "scMINER"),
-                         header = T, row.names = 1, sep = "\t", quote = "", stringsAsFactors = FALSE)
+                         header = TRUE, row.names = 1, sep = "\t", quote = "", stringsAsFactors = FALSE)
 pbmc14k_raw.eset <- createSparseEset(input_matrix = pbmc14k_rawCount,
                                      cellData = true_label,
                                      featureData = NULL,
diff --git a/man/updateSparseEset.Rd b/man/updateSparseEset.Rd
index c4bc978..12052a6 100644
--- a/man/updateSparseEset.Rd
+++ b/man/updateSparseEset.Rd
@@ -36,7 +36,7 @@ data("pbmc14k_expression.eset")
 pbmc14k_raw.eset <- updateSparseEset(input_eset = pbmc14k_expression.eset, addMetaData = TRUE)
 
 ## 2. Update the meta data of cells: with this, the user can add more information of cells, like clustering results, cell type.
-true_label <- read.table(system.file("extdata/demo_pbmc14k/PBMC14k_trueLabel.txt.gz", package = "scMINER"), header = T, row.names = 1, sep = "\t", quote = "", stringsAsFactors = FALSE)
+true_label <- read.table(system.file("extdata/demo_pbmc14k/PBMC14k_trueLabel.txt.gz", package = "scMINER"), header = TRUE, row.names = 1, sep = "\t", quote = "", stringsAsFactors = FALSE)
 pbmc14k_raw.eset <- updateSparseEset(input_eset = pbmc14k_expression.eset, cellData = true_label, addMetaData = TRUE)
 
 }