Getting Started with NOVA

Introduction

NOVA (Network Of Versatile cell-cell communication Analysis) is a high-performance R package designed for inferring and visualizing ligand-receptor mediated cell-to-cell communication networks from single-cell and bulk transcriptomic data.

Key Features

Comprehensive LR Database: Utilizes connectomeDB2020 with 2,293 literature-curated ligand-receptor pairs
Specificity-weighted Scoring: Identifies biologically relevant communication signals
Multi-species Support: 21 species via NCBI HomoloGene orthology mapping
Seurat Integration: Native support for Seurat V4 and V5 objects
High Performance: Vectorized operations with C++ acceleration
Rich Visualization: Heatmaps, networks, chord diagrams, and more

Installation

# From R-universe (recommended)
install.packages("NOVA", repos = "https://zaoqu-liu.r-universe.dev")

# From GitHub
remotes::install_github("Zaoqu-Liu/NOVA")

Quick Start

Loading the Package

library(NOVA)

Simulating Example Data

For demonstration, we’ll create simulated single-cell expression data:

set.seed(42)

# Create expression matrix (genes x cells)
n_genes <- 500
n_cells <- 300
gene_names <- paste0("Gene", 1:n_genes)
cell_names <- paste0("Cell", 1:n_cells)

# Simulate sparse expression
expr <- matrix(0, nrow = n_genes, ncol = n_cells,
               dimnames = list(gene_names, cell_names))

# Add expression for ~30% of entries
expressed <- sample(length(expr), size = length(expr) * 0.3)
expr[expressed] <- abs(rnorm(length(expressed), mean = 2, sd = 1))

# Convert to sparse matrix for efficiency
expr <- Matrix::Matrix(expr, sparse = TRUE)

# Create cluster annotation
clusters <- sample(c("T_cells", "B_cells", "Macrophages", "Fibroblasts"), 
                   n_cells, replace = TRUE)
names(clusters) <- cell_names
annotation <- data.frame(cell = cell_names, cluster = clusters)

Loading LR Database

# Load the curated ligand-receptor database
lr_db <- GetLRDatabase("lrc2p")
head(lr_db)
#>    ligand receptor
#>    <char>   <char>
#> 1:    A2M     LRP1
#> 2:  AANAT   MTNR1A
#> 3:  AANAT   MTNR1B
#> 4:    ACE   BDKRB2
#> 5: ADAM10    EPHA3
#> 6: ADAM11    ITGA4
cat("Total LR pairs:", nrow(lr_db), "\n")
#> Total LR pairs: 2293

Running NOVA Analysis

# Note: In real analysis, use actual gene symbols that match the LR database
# Here we simulate matching genes for demonstration
ligands <- unique(lr_db$ligand)[1:20]
receptors <- unique(lr_db$receptor)[1:20]
rownames(expr)[1:20] <- ligands
rownames(expr)[21:40] <- receptors

# Run NOVA
result <- ExtractEdges(
  expression = expr,
  annotation = annotation,
  species = "human",
  database = "lrc2p",
  min_pct = 0.1
)

# View results
print(result)

Exploring Results

# Summary statistics
summary(result)

# Get edges for specific cluster pair
edges_subset <- GetEdges(result, 
                          sending = "T_cells", 
                          target = "Macrophages")
head(edges_subset)

Working with Seurat Objects

NOVA seamlessly integrates with Seurat:

# Convert Seurat object to NOVA input
nova_input <- SeuratToNOVA(
  seurat_obj,
  assay = "RNA",
  slot = "data",
  cluster_col = "cell_type"
)

# Run analysis
result <- ExtractEdges(
  expression = nova_input$expression,
  annotation = nova_input$annotation,
  species = "mouse"
)

# Store results back in Seurat object
seurat_obj <- AddNOVAResults(seurat_obj, result)

Basic Visualization

Communication Heatmap

# Create heatmap of communication strength
PlotHeatmap(result, metric = "count")
PlotHeatmap(result, metric = "specificity")

Network Graph

# Network visualization
PlotNetwork(result, layout = "circle")

Session Info

sessionInfo()
#> R version 4.4.0 (2024-04-24)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS 15.6.1
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
#> 
#> locale:
#> [1] C
#> 
#> time zone: Asia/Shanghai
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] NOVA_1.0.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] Matrix_1.7-4       gtable_0.3.6       jsonlite_2.0.0     dplyr_1.1.4       
#>  [5] compiler_4.4.0     Rcpp_1.1.1         tidyselect_1.2.1   parallel_4.4.0    
#>  [9] dichromat_2.0-0.1  jquerylib_0.1.4    systemfonts_1.3.1  scales_1.4.0      
#> [13] textshaping_1.0.4  yaml_2.3.12        fastmap_1.2.0      lattice_0.22-7    
#> [17] ggplot2_4.0.1      R6_2.6.1           generics_0.1.4     knitr_1.51        
#> [21] htmlwidgets_1.6.4  tibble_3.3.1       desc_1.4.3         bslib_0.9.0       
#> [25] pillar_1.11.1      RColorBrewer_1.1-3 rlang_1.1.7        cachem_1.1.0      
#> [29] xfun_0.56          fs_1.6.6           sass_0.4.10        S7_0.2.1          
#> [33] otel_0.2.0         cli_3.6.5          pkgdown_2.2.0      magrittr_2.0.4    
#> [37] digest_0.6.39      grid_4.4.0         lifecycle_1.0.5    vctrs_0.7.1       
#> [41] data.table_1.18.0  evaluate_1.0.5     glue_1.8.0         farver_2.1.2      
#> [45] ragg_1.5.0         rmarkdown_2.30     tools_4.4.0        pkgconfig_2.0.3   
#> [49] htmltools_0.5.9

Author

Zaoqu Liu

Email: liuzaoqu@163.com
GitHub: Zaoqu-Liu

Citation

If you use NOVA in your research, please cite:

Liu, Z. (2026). NOVA: Network Of Versatile Cell-Cell Communication Analysis. R package version 1.0.0. https://github.com/Zaoqu-Liu/NOVA

Zaoqu Liu

2026-01-26