Package: inferCSN 1.1.1

Meng Xu

inferCSN: Inferring Cell-Specific Gene Regulatory Network

An R package for inferring cell-type specific gene regulatory network from single-cell RNA data.

Authors:Meng Xu [aut, cre]

inferCSN_1.1.1.tar.gz
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inferCSN.pdf |inferCSN.html✨
inferCSN/json (API)

# Install 'inferCSN' in R:

install.packages('inferCSN', repos = c('https://mengxu98.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mengxu98/infercsn/issues

Pkgdown site:https://mengxu98.github.io

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

example_ground_truth - Example ground truth data
example_matrix - Example matrix data
example_meta_data - Example meta data

On CRAN:

openblas cpp

4.81 score 3 stars 6 scripts 242 downloads 32 exports 65 dependencies

Last updated 2 days agofrom:38f90bf9f6. Checks:6 OK, 1 NOTE, 1 FAILURE, 3 ERROR. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Jan 27 2025
R-4.5-win-x86_64	OK	Jan 27 2025
R-4.5-mac-x86_64	ERROR	Jan 27 2025
R-4.5-mac-aarch64	OK	Jan 27 2025
R-4.5-linux-x86_64	NOTE	Jan 27 2025
R-4.4-win-x86_64	OK	Jan 27 2025
R-4.4-mac-x86_64	OK	Jan 27 2025
R-4.4-mac-aarch64	OK	Jan 27 2025
R-4.3-win-x86_64	ERROR	Jan 27 2025
R-4.3-mac-x86_64	ERROR	Jan 27 2025
R-4.3-mac-aarch64	OUTDATED	Jan 21 2025

Exports:%ss%as_matrix calculate_gene_rank calculate_metrics check_sparsity filter_sort_matrix fit_srm inferCSN log_message matrix_to_table meta_cells network_format network_sift normalization parallelize_fun plot_coefficient plot_coefficients plot_contrast_networks plot_dynamic_networks plot_embedding plot_histogram plot_network_heatmap plot_scatter plot_static_networks simulate_sparse_matrix single_network sparse_cor sparse_regression split_indices subsampling table_to_matrix weight_sift

Dependencies:cachem cli coda codetools colorspace cpp11 doParallel dplyr fansi farver fastmap foreach generics ggforce ggnetwork ggplot2 ggraph ggrepel glue graphlayouts gridExtra gtable igraph isoband iterators jsonlite labeling lattice lifecycle magrittr MASS Matrix memoise mgcv munsell network nlme pbapply pillar pkgconfig polyclip purrr R6 RColorBrewer Rcpp RcppArmadillo RcppEigen RcppParallel rlang scales sna statnet.common stringi stringr systemfonts tibble tidygraph tidyr tidyselect tweenr utf8 vctrs viridis viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
_inferCSN_: inferring Cell-Specific gene regulatory Network	inferCSN-package
Value selection operator	%ss%
Convert sparse matrix into dense matrix	as_matrix
Rank TFs and genes in network	calculate_gene_rank
Calculate Network Prediction Performance Metrics	calculate_metrics
Check sparsity of matrix	check_sparsity
Extracts a specific solution in the regularization path	coef.srm coef.srm_cv
Example ground truth data	example_ground_truth
Example matrix data	example_matrix
Example meta data	example_meta_data
Filter and sort matrix	filter_sort_matrix
Sparse regression model	fit_srm
inferring Cell-Specific gene regulatory Network	inferCSN inferCSN,data.frame-method inferCSN,matrix-method inferCSN,sparseMatrix-method
Print diagnostic message	log_message
Switch matrix to network table	matrix_to_table
Detection of metacells from single-cell gene expression matrix	meta_cells
Format network table	network_format
Sifting network	network_sift
Normalize numeric vector	normalization
Parallelize a function	parallelize_fun
Correlation and covariance calculation for sparse matrix	pearson_correlation
Plot coefficients	plot_coefficient
Plot coefficients for multiple targets	plot_coefficients
Plot contrast networks	plot_contrast_networks
Plot dynamic networks	plot_dynamic_networks
Plot embedding	plot_embedding
Plot histogram	plot_histogram
Plot network heatmap	plot_network_heatmap
Plot expression data in a scatter plot	plot_scatter
Plot dynamic networks	plot_static_networks
Predicts response for a given sample	predict.srm predict.srm_cv
Prints a summary of 'sparse_regression'	print.srm print.srm_cv
R^2 (coefficient of determination)	r_square
Generate a simulated sparse matrix for single-cell data testing	simulate_sparse_matrix
Construct network for single target gene	single_network
Safe correlation function which returns a sparse matrix without missing values	sparse_cor
Fit a sparse regression model	sparse_regression
Split indices.	split_indices
Subsampling function	subsampling
Switch network table to matrix	table_to_matrix
Weight sift	weight_sift