LIONESS(Linear Interpolation to Obtain Network Estimates for Single Samples) is a method to estimate sample-specific regulatory networks. [(LIONESS publication)]).
Usage
lionessPy(
expr_file,
motif_file = NULL,
ppi_file = NULL,
computing = "cpu",
precision = "double",
save_tmp = TRUE,
modeProcess = "union",
remove_missing = FALSE,
start_sample = 1,
end_sample = "None",
save_single_network = FALSE,
save_dir = "lioness_output",
save_fmt = "npy"
)Arguments
- expr_file
Character string indicating the file path of expression values file, with each gene(in rows) across samples(in columns).
- motif_file
An optional character string indicating the file path of a prior transcription factor binding motifs dataset. When this argument is not provided, analysis will continue with Pearson correlation matrix.
- ppi_file
An optional character string indicating the file path of protein-protein interaction edge dataset. Also, this can be generated with a list of proteins of interest by
sourcePPI.- computing
'cpu' uses Central Processing Unit (CPU) to run PANDA; 'gpu' use the Graphical Processing Unit (GPU) to run PANDA. The default value is "cpu".
- precision
'double' computes the regulatory network in double precision (15 decimal digits); 'single' computes the regulatory network in single precision (7 decimal digits) which is fastaer, requires half the memory but less accurate. The default value is 'double'.
- save_tmp
'TRUE' saves middle data like expression matrix and normalized networks; 'FALSE' deletes the middle data. The default value is 'TURE'.
- modeProcess
'legacy' refers to the processing mode in netZooPy<=0.5, 'union': takes the union of all TFs and genes across priors and fills the missing genes in the priors with zeros; 'intersection': intersects the input genes and TFs across priors and removes the missing TFs/genes. Default values is 'union'.
- remove_missing
Only when modeProcess='legacy': remove_missing='TRUE' removes all unmatched TF and genes; remove_missing='FALSE' keeps all tf and genes. The default value is FALSE.
- start_sample
Numeric indicating the start sample number, The default value is 1.
- end_sample
Numeric indicating the end sample number. The default value is 'None' meaning no end sample, i.e. print out all samples.
- save_single_network
Boolean vector, "TRUE" wirtes out the single network in npy/txt/mat formats, directory and format are specifics by params "save_dir" and "save_fmt". The default value is 'FALSE'
- save_dir
Character string indicating the folder name of output lioness networks for each sample by defined. The default is a folder named "lioness_output" under current working directory. This paramter is valid only when save_single_network = TRUE.
- save_fmt
Character string indicating the format of lioness network of each sample. The dafault is "npy". The option is txt, npy, or mat. This paramter is valid only when save_single_network = TRUE.
Value
A data frame with columns representing each sample, rows representing the regulator-target pair in PANDA network generated by pandaPy.
Each cell filled with the related score, representing the estimated contribution of a sample to the aggregate network.
Examples
# refer to the input datasets files of control in inst/extdat as example
control_expression_file_path <- system.file("extdata", "expr10_reduced.txt", package = "netZooR",
mustWork = TRUE)
motif_file_path <- system.file("extdata", "chip_reduced.txt", package = "netZooR", mustWork = TRUE)
ppi_file_path <- system.file("extdata", "ppi_reduced.txt", package = "netZooR", mustWork = TRUE)
# Run LIONESS algorithm
# \donttest{
control_lioness_result <- lionessPy(expr_file = control_expression_file_path,
motif_file = motif_file_path, ppi_file = ppi_file_path,
modeProcess="union",start_sample=1, end_sample=1, precision="single")
# }