lioness.py.RdLIONESS(Linear Interpolation to Obtain Network Estimates for Single Samples) is a method to estimate sample-specific regulatory networks. [(LIONESS arxiv paper)]).
lioness.py( 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" )
| expr_file | Character string indicating the file path of expression values file, with each gene(in rows) across samples(in columns). |
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| 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 |
| 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. |
A data frame with columns representing each sample, rows representing the regulator-target pair in PANDA network generated by panda.py.
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_matched.txt", package = "netZooR", mustWork = TRUE) motif_file_path <- system.file("extdata", "chip_matched.txt", package = "netZooR", mustWork = TRUE) ppi_file_path <- system.file("extdata", "ppi_matched.txt", package = "netZooR", mustWork = TRUE) # Run LIONESS algorithm control_lioness_result <- lioness.py(expr_file = control_expression_file_path, motif_file = motif_file_path, ppi_file = ppi_file_path, modeProcess="union",start_sample=1, end_sample=2)