Pyscenic

pySCENIC is a python implementation of the SCENIC pipeline to infer gene regulatory networks from single-cell RNA-seq data.

Citation

Citation: 10.1038/nmeth.4463

Environment

This tool uses the following conda environment:

Channels: - conda-forge - bioconda

Dependencies: - python=3.10 - numpy<1.24 - numba<0.57 - setuptools<80 - list(pip = “pyscenic”)

Installation

This tool will be automatically installed when first used:

library(ShennongTools)

# Tool will be installed automatically on first use
result <- sn_run("pyscenic", "grn", ...)

Available Commands

grn

Infer co-expression modules using GRNBoost2

Basic Usage:

result <- sn_run("pyscenic", "grn",
  # Add your parameters here
)

ctx

Prune GRNs using motif enrichment (cisTarget)

Basic Usage:

result <- sn_run("pyscenic", "ctx",
  # Add your parameters here
)

aucell

Score regulons using AUCell

Basic Usage:

result <- sn_run("pyscenic", "aucell",
  # Add your parameters here
)

Examples

Grn Example

library(ShennongTools)

result <- sn_run("pyscenic", "grn",
  expression = "example_file",
  tf_list = "results.txt",
  adjacencies = "data.csv",
  threads = 4
)

# Check if successful
if (sn_is_toolcall_success(result)) {
  cat("Command completed successfully!\n")
} else {
  cat("Command failed. Check logs:\n")
  cat(readLines(result@log_file), sep = "\n")
}

Ctx Example

library(ShennongTools)

result <- sn_run("pyscenic", "ctx",
  adjacencies = "data.csv",
  motifs = "example_file",
  regulons = "data.csv",
  threads = 4
)

# Check if successful
if (sn_is_toolcall_success(result)) {
  cat("Command completed successfully!\n")
} else {
  cat("Command failed. Check logs:\n")
  cat(readLines(result@log_file), sep = "\n")
}

Aucell Example

library(ShennongTools)

result <- sn_run("pyscenic", "aucell",
  expression = "example_file",
  regulons = "data.csv",
  auc_matrix = "example_file",
  threads = 4
)

# Check if successful
if (sn_is_toolcall_success(result)) {
  cat("Command completed successfully!\n")
} else {
  cat("Command failed. Check logs:\n")
  cat(readLines(result@log_file), sep = "\n")
}

Parameters Reference

grn Parameters

Inputs:

Parameter	Type	Required	Description
`expression`	loom, csv	Yes	The name of the file that contains the expression matrix for the single cell experiment. Two file formats are supported: csv (rows=cells x columns=genes) or loom (rows=genes x columns=cells).
`tf_list`	txt	Yes	The name of the file that contains the list of transcription factors (TXT; one TF per line).

Outputs:

Parameter	Type	Required	Description
`adjacencies`	csv	Yes	Output file/stream, i.e. a table of TF-target genes (CSV).

Parameters:

Parameter	Type	Default	Description
`transpose`	boolean	FALSE	Transpose the expression matrix (rows=genes x columns=cells).
`seed`	integer	42	RSeed value for regressor random state initialization. Applies to both GENIE3 and GRNBoost2. The default is to use a random seed.
`method`	string	“grnboost2”	Method for GRN inference (grnboost2 or genie3)
`threads`	integer	4	Number of threads to use
`extras`	string	“”	Additional arguments to pass to the pyscenic command.

ctx Parameters

Inputs:

Parameter	Type	Required	Description
`adjacencies`	csv	Yes	Adjacency matrix from GRN step
`motifs`	feather	Yes	Motif ranking databases

Outputs:

Parameter	Type	Required	Description
`regulons`	csv	Yes	Output file containing regulons

Parameters:

Parameter	Type	Default	Description
`threads`	integer	4	Number of threads to use
`extras`	string	“”	Additional arguments to pass to the pyscenic command.

aucell Parameters

Inputs:

Parameter	Type	Required	Description
`expression`	loom	Yes	Expression matrix (loom)
`regulons`	csv	Yes	File containing regulons

Outputs:

Parameter	Type	Required	Description
`auc_matrix`	loom	Yes	AUCell matrix saved as loom

Parameters:

Parameter	Type	Default	Description
`threads`	integer	4	Number of threads to use
`extras`	string	“”	Additional arguments to pass to the pyscenic command.