version 1.0.5

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: inferCSN
 Title: Inferring Cell-Specific Gene Regulatory Network
-Version: 1.0.3
-Date: 2024-4-16
+Version: 1.0.5
+Date: 2024-6-26
 Authors@R: 
     person("Meng", "Xu", email = "mengxu98@qq.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-8300-1054"))
 Maintainer: Meng Xu <mengxu98@qq.com>
@@ -12,10 +12,11 @@ URL: https://mengxu98.github.io/inferCSN/
 BugReports: https://github.com/mengxu98/inferCSN/issues
 Depends: R (>= 3.3.0)
 Imports: ComplexHeatmap, doParallel, dplyr, foreach, ggnetwork,
-        ggplot2, ggraph, Matrix, methods, parallel, patchwork,
-        progress, purrr, Rcpp, stats, utils
-Suggests: circlize, gtools, igraph, precrec, pROC, testthat (>= 3.0.0),
-        tidygraph
+        ggplot2, ggraph, Matrix, methods, parallel, patchwork, pbapply,
+        purrr, Rcpp, RcppArmadillo, stats, utils
+Suggests: circlize, gtools, gganimate, ggExtra, ggpointdensity, ggpubr,
+        igraph, network, plotly, precrec, pROC, testthat (>= 3.0.0),
+        tidygraph, RColorBrewer, RTransferEntropy, viridis
 LinkingTo: Rcpp, RcppArmadillo
 Config/Needs/website: mengxu98/mxtemplate
 Config/testthat/edition: 3
@@ -24,7 +25,7 @@ LazyData: true
 RoxygenNote: 7.3.1
 Language: en-US
 NeedsCompilation: yes
-Packaged: 2024-04-17 08:23:53 UTC; mx
+Packaged: 2024-06-26 11:31:04 UTC; mx
 Author: Meng Xu [aut, cre] (<https://orcid.org/0000-0002-8300-1054>)
 Repository: CRAN
-Date/Publication: 2024-04-17 08:50:02 UTC
+Date/Publication: 2024-06-26 12:10:02 UTC

MD5

@@ -1,50 +1,55 @@
-ae0e741cf81f24f22a23c20382d234eb *DESCRIPTION
+7a7a7c33132873aae6cb6e368c3b5b32 *DESCRIPTION
 5d82e9946ace180a66d2fe6db26780f1 *LICENSE
-a30b84939042241b2773f15e0052e8f5 *NAMESPACE
-d315bd0478e85c16cd0f3b1dc6b9aa57 *R/RcppExports.R
-fbcfdc8c66f538cfb932f46335e867c3 *R/calculate.gene.rank.R
-fde9c71a5934ff237a4dd19269046786 *R/crossweight.R
+51745bffb92b082c1e53c818530f0c20 *NAMESPACE
+4cb14cc19f78420e2ad189a0d6681d20 *R/RcppExports.R
+bc5f9aaa4bae796bc6356233ba23641e *R/calculate.gene.rank.R
 c6d322165785a8fa5b40ffa0f41b6806 *R/data.R
-f80bb002fce1e112814b67e8fb58712a *R/import.R
+8fe984d847aebe8bd7d1027f5b5d2db1 *R/import.R
 3c123d90a667dbf89091dade63e0fe11 *R/inferCSN-package.R
-71a760e4abed3739f10cac5399ec0fae *R/inferCSN.R
-0fab54600db880b5ed2c673b5065e21f *R/network.visualization.R
-333af8560030e7ac76b52d933baf2a58 *R/performance.calculate.R
-64d7d2369e200e78ddc8e9560d96c581 *R/sparse.regression.R
-40cfcd354a0f236e6b8a861628fd1b76 *R/utils.R
-dc8c6f04293b2d139fe73825c21f1889 *R/weight_filter.R
-d26a2baf46f6db9a64ca52fcd6a9f158 *README.md
+5179e97fc1dd607b3b1f7457df2c251f *R/inferCSN.R
+7877479f4a1686cfff9305941ca572de *R/network.visualization.R
+e49fdfb7a3c4e3e0683432b87165a13d *R/network_sift.R
+09cdc9df1eb35c9b9db3f96bb29496b2 *R/performance.calculate.R
+e8d4d6396f1f9a2f8832b0f601d24b38 *R/plotting_functions.R
+dbea8e4ce5a73cd53be033f45247166b *R/sparse.regression.R
+35ad7fd48c815aa96dd9138fef01b5c4 *R/utils.R
+d5353d3f570bb96aecd3a121d02adbec *README.md
 c865cc831c3370c46b93efe15036051c *data/example_ground_truth.rda
 7876ce105fe62fea51999bf207905aab *data/example_matrix.rda
 e14e9b714f5e7617ed2c4b727cc48e34 *data/example_meta_data.rda
-dd78aa884be2a0c6db2c7cc77bee9013 *man/acc.calculate.Rd
-b9f10b9ee2ce79f06fdb78d1dcf49cc9 *man/auc.calculate.Rd
-9184d10b757ee131fcb88f6faa2fbc12 *man/calculate.gene.rank.Rd
-2ec53a0f4a4b6070a92e031b5076fa5e *man/check.parameters.Rd
+20028a9b21c91cdd40bff32eb47da3e5 *man/acc.calculate.Rd
+6fe7d40b134589fa78962f3a4a638698 *man/as_matrix.Rd
+22ac94dd61556e306afcb7fac6c631c9 *man/auc.calculate.Rd
+b3647c37355dcfda3ed2456e5be1667c *man/calculate.gene.rank.Rd
+d95d1237f00cebdccc9b87a84ab21a3d *man/check.parameters.Rd
 852b7ad289881bacb30cf00b6d636f62 *man/coef.SRM_fit.Rd
-ae3dae8d622c792f1406a75153bc25a0 *man/contrast.networks.Rd
-e0bb70d025402de9fbae2b572baca605 *man/crossweight.Rd
-2900172baad4f9cd85e05e53393bb3a2 *man/crossweight_params.Rd
-fd98c1624d8e41509f9209d60fae8e52 *man/dynamic.networks.Rd
 bea92ebfc3cc63a4375cd260d58a1e42 *man/example_ground_truth.Rd
 369f179d33e62b3546d42b46591e907d *man/example_matrix.Rd
 4395d138df2bb37a4603150b74963ca3 *man/example_meta_data.Rd
 4cde6e83b2d3b96a27c12cd1ba8e0091 *man/figures/inferCSN.svg
-13d14f85d5b3651b4f96e5fc502a59ad *man/figures/logo.svg
-3a55f6a30549f83fba29e223c4e327d6 *man/filter_sort_matrix.Rd
+59e13bbcf63a9e9fdd8780892fca93a9 *man/figures/logo.svg
+0ee09d1af60d64a2d0b484fd53b6f4f9 *man/filter_sort_matrix.Rd
 660b9f63753b32fb314e0b0426641a72 *man/inferCSN-package.Rd
-694d0f11897c332cd4c85a362d99449e *man/inferCSN.Rd
-2bc5da6085decce830a9ae5a868eefc9 *man/model.fit.Rd
-26ea05c707c1cdc7d9f4d33a358c3541 *man/net.format.Rd
-8e02b2ecf27186fc54941eb3cca15160 *man/network.heatmap.Rd
+aeb9829d471f37c2a67ac2fcf6bfc44b *man/inferCSN.Rd
+55d7dca784553b8335e40e5383932280 *man/model.fit.Rd
+e6be8043e958b17758e1ee1dc6049a86 *man/network.heatmap.Rd
+c8f8bd085df8e1e67a545500917cc86c *man/network_format.Rd
+4d37e84a4f0e59c4a0cd233695ce8bb0 *man/network_sift.Rd
 b9ce4fb9d00a5b82f22de24f477aab76 *man/normalization.Rd
+d9c07f131a71416659ab93c8be6e952b *man/parallelize_fun.Rd
+c5d5053c700564015cdfcc430064d126 *man/plot_contrast_networks.Rd
+4d3a09160fc9df9c28988d6995549b6d *man/plot_dynamic_networks.Rd
+6d96ec3a6b5523d50f30d6139d5b2b66 *man/plot_scatter.Rd
+ce795a145a3d52c1e69420300875e50c *man/plot_static_networks.Rd
 22eb496af3e810feecd27c03bb334610 *man/predict.SRM_fit.Rd
-323bf94a8a8c9b21ca80695bc7a3b7c5 *man/prepare.performance.data.Rd
+6ce12a2e580bed1cad784d5ff5d32d87 *man/prepare.performance.data.Rd
 97db8cfd5710b3f88301b3228c1c8ab7 *man/print.SRM_fit.Rd
-4be9183d647ff5c6a3498e17d2e35f9e *man/single.network.Rd
-1abad38810d25683403a8ea9a5909e7b *man/sparse.regression.Rd
-28ae3951d7e466b631e4384096cccecf *man/table.to.matrix.Rd
-46dbe5afd90222f603d4875c5baf5a80 *man/weight_filter.Rd
+e097fb0f834bfc70cd722f45cfba9529 *man/r_square.Rd
+2ae7e55e80f568d18f8bec5daabe2a0b *man/rse.Rd
+0b3063ee7cea045057b8f12bbad36d88 *man/single.network.Rd
+30a773ce548fa7771efdb6773a2e0920 *man/sparse.regression.Rd
+7927698720ae259ac54f9a5928d47af2 *man/sse.Rd
+0ad065da42f23eebe3d7805617390682 *man/table.to.matrix.Rd
 0347ffda63bae663f9354d1e0b948d57 *src/BetaVector.cpp
 dadb1987bfb2f80d199a2e25b02a051d *src/CDL012LogisticSwaps.cpp
 64737baec03e2641f58d5681381874d6 *src/CDL012SquaredHingeSwaps.cpp
@@ -57,8 +62,9 @@ c2fbbe606dbc01e99513a08efa77bb27 *src/Makevars
 c2fbbe606dbc01e99513a08efa77bb27 *src/Makevars.win
 41bc93afdbfbce896558b943b882d7e7 *src/Normalize.cpp
 fb4ab9386a76250a1012fc799738465f *src/README.cpp
-5b2f7edf897fc18eea5f604275d365ef *src/RcppExports.cpp
+9b2d5112f4a516f521f0bc23155d8a36 *src/RcppExports.cpp
 b8da420687f2f9943625f9c8cddda799 *src/Test_Interface.cpp
+f34ac4c9ea5234a36682d3cd099a8a0c *src/asMatrix.cpp
 e447698f0493b967abd0e0aaf2eca2c2 *src/include/BetaVector.h
 e9aab72b23846ad7db4b0fa1e2597d09 *src/include/CD.h
 3006562807501591e7979d1b5af7c039 *src/include/CDL0.h

NAMESPACE

@@ -7,37 +7,34 @@ S3method(predict,SRM_fit_CV)
 S3method(print,SRM_fit)
 S3method(print,SRM_fit_CV)
 export(acc.calculate)
+export(as_matrix)
 export(auc.calculate)
 export(calculate.gene.rank)
 export(check.parameters)
-export(contrast.networks)
-export(crossweight)
-export(crossweight_params)
-export(dynamic.networks)
 export(filter_sort_matrix)
 export(inferCSN)
 export(model.fit)
-export(net.format)
 export(network.heatmap)
+export(network_format)
+export(network_sift)
 export(normalization)
+export(parallelize_fun)
+export(plot_contrast_networks)
+export(plot_dynamic_networks)
+export(plot_scatter)
+export(plot_static_networks)
 export(prepare.performance.data)
 export(single.network)
 export(sparse.regression)
 export(table.to.matrix)
-export(weight_filter)
 exportMethods(inferCSN)
 import(Matrix)
-import(doParallel)
-import(foreach)
 import(ggnetwork)
 import(ggplot2)
 import(ggraph)
-import(parallel)
 import(patchwork)
-import(progress)
 importFrom(Rcpp,evalCpp)
-importFrom(methods,as)
-importFrom(methods,is)
+importFrom(RcppArmadillo,armadillo_version)
 importFrom(stats,coef)
 importFrom(stats,predict)
 importFrom(utils,methods)

R/RcppExports.R

@@ -93,6 +93,10 @@ R_matrix_center_sparse <- function(mat, X_normalized, intercept) {
     .Call('_inferCSN_R_matrix_center_sparse', PACKAGE = 'inferCSN', mat, X_normalized, intercept)
 }
 
+asMatrix <- function(rp, cp, z, nrows, ncols) {
+    .Call('_inferCSN_asMatrix', PACKAGE = 'inferCSN', rp, cp, z, nrows, ncols)
+}
+
 table_to_matrix <- function(weight_table) {
     .Call('_inferCSN_table_to_matrix', PACKAGE = 'inferCSN', weight_table)
 }

R/calculate.gene.rank.R

@@ -1,31 +1,30 @@
 #' @title Calculate and rank TFs in network
 #'
-#' @inheritParams net.format
+#' @inheritParams network_format
 #' @param directed If network is directed or not.
 #'
 #' @return A data.table with three columns
 #' @export
 #'
 #' @examples
-#' library(inferCSN)
 #' data("example_matrix")
-#' weight_table <- inferCSN(example_matrix)
-#' head(calculate.gene.rank(weight_table))
-#' head(calculate.gene.rank(weight_table, regulators = "g1"))
+#' network_table <- inferCSN(example_matrix)
+#' head(calculate.gene.rank(network_table))
+#' head(calculate.gene.rank(network_table, regulators = "g1"))
 calculate.gene.rank <- function(
-    weight_table,
+    network_table,
     regulators = NULL,
     targets = NULL,
     directed = FALSE) {
-  colnames(weight_table) <- c("regulator", "target", "weight")
-  weight_table <- net.format(
-    weight_table,
+  colnames(network_table) <- c("regulator", "target", "weight")
+  network_table <- network_format(
+    network_table,
     regulators = regulators,
     targets = targets
   )
 
   network <- igraph::graph_from_data_frame(
-    weight_table,
+    network_table,
     directed = directed
   )
   page_rank_res <- data.frame(
@@ -41,7 +40,7 @@ calculate.gene.rank <- function(
   page_rank_res$is_regulator <- FALSE
   page_rank_res$is_regulator[
     page_rank_res$gene %in% unique(
-      weight_table$regulator
+      network_table$regulator
     )
   ] <- TRUE