scSignalMap: scRNA-Seq Ligand–Receptor Signaling Analysis in R

scSignalmap performs a full ligand–receptor signaling analysis for single-cell RNA sequencing data. Utilizing user-defined parameters, it performs a streamlined workflow including:

• Ligand–receptor interaction mapping
• Differential expression analysis
• Receptor filtering
• Receptor–ligand intersection
• Pathway enrichment analysis

Table of Contents

• Requirements and Dependencies
• Installation
• Dockerfile and Image
• Tutorials
  • Running scSignalMap
  • Accessing Outputs
• Maintainers

Requirements and Dependencies

scSignalMap usage requires R packages: scSignalMap, Seurat, enrichR, dplyr, tidyr, stringr, EnsDb.Hsapiens.v86, AnnotationDbi, fastmatch, and data.table to run the full pipline. Installation can be done using:

install.packages(c('scSignalMap', 'Seurat', 'enrichR', 'dplyr', 'tidyr', 'stringr', 'EnsDb.Hsapiens.v86', 'AnnotationDbi', 'fastmatch', 'data.table'))

Installation

Installation of scSignalMap in R is accomplished by:

remotes::install_github("plaisier-lab/scSignalMap/scSignalMap")

Dockerfile and Image

For those who are interested we also provide a Dockerfile and image that include all depdencies: Command to pull the image down:

docker pull cplaisier/quadculture

Command to run the docker image. Note that the should be replaced with the path to your files that you want to be mounted onto the docker instance. The files can then be found in /files on the instance and locally on your computer in the path specified.

docker run -it -v '<replace with the location for your files>:/files' cplaisier/quadculture

Running scSignalMap

The scSignalMap pipeline is performed through one function, run_full_scSignalMap_pipeline() and an optional function, create_master_interaction_list, that creates a master list of scSignalMap outputs.

Below is an example of inputs for the run_full_scSignalMap_pipeline function:

results = run_full_scSignalMap_pipeline(
workingdir = "/files", 
seurat_obj = seurat_obj, 
prep_SCT = TRUE, 
cond_column = "orig.ident",
cond_name1 = "MN2", 
cond_name2 = "MN1", 
celltype_column = "celltype", 
celltype_name = "GB3_Tumor", 
sender_celltypes = c("Astrocyte", "HUVEC"), 
receiver_celltypes = "GB3_Tumor", 
secreted_lig = TRUE, 
FC_cutoff = 0.3, 
adj_p_val_cutoff = 0.05, 
enrichr_databases = c("BioCarta_2016", 
		    "GO_Biological_Process_2025", 
		    "KEGG_2021_Human", 	
		    "NCI-Nature_2016", 	
		    "WikiPathways_2024_Human"), 
adj_p_val_method = "BH")

Below is an example of inputs for the create_master_interaction_list function:

master_interaction_list = create_master_interaction_list(
  enrichr_results = results$enrichr_results,
  de_receptors = results$upreg_receptors_filtered_and_compared,
  scSignalMap_data_filtered = results$interactions_filtered)

Accessing Outputs

Accessing results from run_full_scSignalMap_pipeline can be accomplished using the following code:

write.csv(results$enrichr_results, "<file_name.csv>")

Accessing results from create_master_interaction_list can be accomplished using the following code:

write.csv(master_interaction_list, "<file_name.csv>")

Maintainers

For issues or comments, please contact: Chris Plaiser

For other great packages from the Plaisier Lab, please check here: @plaisier_lab