Package: inferCSN 1.1.7

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_1.1.7.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:

On CRAN:

Conda:

openblascpp

4.80 score 3 stars 6 scripts 262 downloads 42 exports 65 dependencies

Last updated 2 days agofrom:04cc7f3503. Checks:12 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 31 2025
R-4.5-win-x86_64OKMar 31 2025
R-4.5-mac-x86_64OKMar 31 2025
R-4.5-mac-aarch64OKMar 31 2025
R-4.5-linux-x86_64OKMar 31 2025
R-4.4-win-x86_64OKMar 31 2025
R-4.4-mac-x86_64OKMar 31 2025
R-4.4-mac-aarch64OKMar 31 2025
R-4.4-linux-x86_64OKMar 31 2025
R-4.3-win-x86_64OKMar 31 2025
R-4.3-mac-x86_64OKMar 31 2025
R-4.3-mac-aarch64OKMar 31 2025

Exports:%ss%as_matrixcalculate_accuracycalculate_auccalculate_auprccalculate_auroccalculate_f1calculate_gene_rankcalculate_jicalculate_metricscalculate_precisioncalculate_recallcalculate_sicheck_sparsityfilter_sort_matrixfit_srminferCSNlog_messagematrix_to_tablemeta_cellsnetwork_formatnetwork_siftnormalizationparallelize_funplot_coefficientplot_coefficientsplot_contrast_networksplot_dynamic_networksplot_edges_comparisonplot_embeddingplot_histogramplot_network_heatmapplot_scatterplot_static_networkssimulate_sparse_matrixsingle_networksparse_corsparse_regressionsplit_indicessubsamplingtable_to_matrixweight_sift

Dependencies:cachemclicodacodetoolscolorspacecpp11doParalleldplyrfansifarverfastmapforeachgenericsggforceggnetworkggplot2ggraphggrepelgluegraphlayoutsgridExtragtableigraphisobanditeratorsjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmunsellnetworknlmepbapplypillarpkgconfigpolyclippurrrR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrlangscalessnastatnet.commonstringistringrsystemfontstibbletidygraphtidyrtidyselecttweenrutf8vctrsviridisviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
_*inferCSN*_: *infer*ring *C*ell-*S*pecific gene regulatory *N*etworkinferCSN-package
Value selection operator%ss%
Convert sparse matrix into dense matrixas_matrix
Calculate Accuracycalculate_accuracy
Calculate AUC Metricscalculate_auc
Calculate AUPRC Metriccalculate_auprc
Calculate AUROC Metriccalculate_auroc
Calculate F1 Scorecalculate_f1
Rank TFs and genes in networkcalculate_gene_rank
Calculate Jaccard Indexcalculate_ji
Calculate Network Prediction Performance Metricscalculate_metrics
Calculate Precision Metriccalculate_precision
Calculate Recall Metriccalculate_recall
Calculate Set Intersectioncalculate_si
Check sparsity of matrixcheck_sparsity
Extracts a specific solution in the regularization pathcoef.srm coef.srm_cv
Example ground truth dataexample_ground_truth
Example matrix dataexample_matrix
Example meta dataexample_meta_data
Filter and sort matrixfilter_sort_matrix
Sparse regression modelfit_srm
*infer*ring *C*ell-*S*pecific gene regulatory *N*etworkinferCSN inferCSN,data.frame-method inferCSN,matrix-method inferCSN,sparseMatrix-method
Print diagnostic messagelog_message
Switch matrix to network tablematrix_to_table
Detection of metacells from single-cell gene expression matrixmeta_cells
Format network tablenetwork_format
Sifting networknetwork_sift
Normalize numeric vectornormalization
Parallelize a functionparallelize_fun
Correlation and covariance calculation for sparse matrixpearson_correlation
Plot coefficientsplot_coefficient
Plot coefficients for multiple targetsplot_coefficients
Plot contrast networksplot_contrast_networks
Plot dynamic networksplot_dynamic_networks
Network Edge Comparison Visualizationplot_edges_comparison
Plot embeddingplot_embedding
Plot histogramplot_histogram
Plot network heatmapplot_network_heatmap
Plot expression data in a scatter plotplot_scatter
Plot dynamic networksplot_static_networks
Predicts response for a given samplepredict.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 testingsimulate_sparse_matrix
Construct network for single target genesingle_network
Safe correlation function which returns a sparse matrix without missing valuessparse_cor
Fast correlation and covariance calcualtion for sparse matricessparse_cov_cor
Fit a sparse regression modelsparse_regression
Split indices.split_indices
Subsampling functionsubsampling
Switch network table to matrixtable_to_matrix
Weight siftweight_sift