network_file_name = "network.tsv"
The network.tsv is a long-format TSV file containing Regulator -> Target edges. This TSV file is sorted by the confidence score of the regulator (TF) -> target (gene) edge, from largest to smallest.:
target regulator combined_confidences gold_standard precision recall MCC F1
BSU24750 BSU04730 0.999986 1 1 0.00165 0.04057 0.003295
BSU13020 BSU04730 0.999984
BSU09690 BSU04730 0.99998
BSU06590 BSU04730 0.999978
BSU18510 BSU04730 0.999976
BSU25800 BSU25810 0.999975If the gene and TF are in the gold standard, the gold standard for this edge is reported (1 if present, 0 if not present), and the model performance is calculated. The Precision, Recall, MCC, and F1 scores are calculated assuming that all edges above a row (with greater confidence scores) are predicted TF -> Gene interactions, and all values below are predicted to not be TF -> Gene interactions. Rows which do not contain any gold standard (either 1 or 0) indicate that the regulator or the target are not in the Genes x TFs gold standard matrix. These rows will not be scored.
Also included is a column indicating if the network edge was in the prior (1, 0, or not present if the gene or TF were not present in the prior network). The beta.sign.sum column is the number of times the model coefficient occurred and the sign (positive model coefficients will be reported as a positive value, and negative model coefficients will be reported as a negative value). The var.exp.median column reports the median amount of variance in the gene explained by the regulator.
inferelator.postprocessing.InferelatorResults
name
network
betas_sign
betas_stack
combined_confidences
tasks