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LIONESS(Linear Interpolation to Obtain Network Estimates for Single Samples) is a method to estimate sample-specific regulatory networks. [(LIONESS publication)]).


  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"



Character string indicating the file path of expression values file, with each gene(in rows) across samples(in columns).


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.


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.


'cpu' uses Central Processing Unit (CPU) to run PANDA; 'gpu' use the Graphical Processing Unit (GPU) to run PANDA. The default value is "cpu".


'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'.


'TRUE' saves middle data like expression matrix and normalized networks; 'FALSE' deletes the middle data. The default value is 'TURE'.


'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'.


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.


Numeric indicating the start sample number, The default value is 1.


Numeric indicating the end sample number. The default value is 'None' meaning no end sample, i.e. print out all samples.


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'


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.


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.


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.


# 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")
# }