panda.py.RdPANDA(Passing Attributes between Networks for Data Assimilation) is a message-passing model to reconstruct gene regulatory network, which integrates multiple sources of biological data-including protein-protein interaction data, gene expression data, and transcription factor binding motifs data to reconstruct genome-wide, condition-specific regulatory networks. [(Glass et al. 2013)]) This function is designed to run the a derived PANDA implementation in Python Library "netZooPy" netZooPy.
panda.py( expr_file, motif_file = NULL, ppi_file = NULL, computing = "cpu", precision = "double", save_memory = FALSE, save_tmp = TRUE, keep_expression_matrix = FALSE, modeProcess = "union", remove_missing = FALSE )
| 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_memory | 'TRUE' removes temporary results from memory. The result network is weighted adjacency matrix of size (nTFs, nGenes); 'FALSE' keeps the temporary files in memory. The result network has 4 columns in the form gene - TF - weight in motif prior - PANDA edge. PANDA indegree/outdegree of panda network, only if save_memory = FALSE. The default value is 'FALSE'. |
| save_tmp | 'TRUE' saves middle data like expression matrix and normalized networks; 'FALSE' deletes the middle data. The default value is 'TURE'. |
| keep_expression_matrix | 'TRUE' keeps the input expression matrix as an attribute in the result Panda object.'FALSE' deletes the expression matrix attribute in the Panda object. The default value is 'FALSE'. |
| 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'. |
When save_memory=FALSE(default), this function will return a list of three items:
Use $panda to access the standard output of PANDA as data frame, which consists of four columns:
"TF", "Gene", "Motif" using 0 or 1 to indicate if this edge belongs to prior motif dataset, and "Score".
Use $indegree to access the indegree of PANDA network as data frame, which consists of two columns: "Gene", "Score".
Use $outdegree to access the outdegree of PANDA network as data frame, which consists of two columns: "TF", "Score".
When save_memory=TRUE, this function will return a weigheted adjacency matirx of size (nTFs, nGenes), use $WAMpanda to access.
# take the treated TB dataset as example here. # refer to the datasets files path in inst/extdat treated_expression_file_path <- system.file("extdata", "expr4_matched.txt", package = "netZooR", mustWork = TRUE) treated_expression_file_path <- system.file("extdata", "expr4_matched.txt", package = "netZooR", mustWork = TRUE) motif_file_path <- system.file("extdata", "chip_matched.txt", package = "netZooR", mustWork = TRUE) ppi_file_path <- system.file("extdata", "ppi_matched.txt", package = "netZooR", mustWork = TRUE) # Run PANDA for treated and control network treated_all_panda_result <- panda.py(expr_file = treated_expression_file_path, motif_file = motif_file_path, ppi_file = ppi_file_path, modeProcess="legacy", remove_missing = TRUE ) # access PANDA regulatory network treated_net <- treated_all_panda_result$panda # access PANDA regulatory indegree network. indegree_net <- treated_all_panda_result$indegree # access PANDA regulatory outdegree networks outdegree_net <- treated_all_panda_result$outdegree