BLRun Details

The BLRun.py creates a BLRun object for each algorithm specified in the config file. Each BLRun object should contain three modules

  1. generateInputs() : This function reads the three input data files, and processes them into the format required by the given algorithm
  2. run() : A function to construct a system command with the appropriate command line parameters to be passed to the docker container in order to run a given algorithm
  3. parseOutput() : A function to read the algorithm-specific output and reformat it into a standard format

The evaluation scripts in the final step of the pipeline expect the inferred networks from each algorithm to be a comma-separated file with the following format:

Gene1,Gene2,EdgeWeight
reg1,targ1,edgeweight

where the first line are the column names, and the subsequent lines contain the edges predicted by the network. The Gene1 column should contain regulators, the Gene2 column the targets, and EdgeWeight column the absolute value of the weight predicted for edge (regulator, target).

BLEval Details

This is the list of options of currently implemented evaluation functions

Command line arguments:
-h, --help show this help message and exit
-c CONFIG, --config CONFIG
 Configuration file containing list of datasets algorithms and output specifications.
-a, --auc Compute median of areas under Precision-Recall and ROC curves. Calls BLEval.computeAUC.
-j, --jaccard Compute median Jaccard index of predicted top-k networks for each algorithm for a given set of datasets generated from the same ground truth network. Calls BLEval.computeJaccard.
-r, --spearman Compute median Spearman Corr. of predicted edges for each algorithm for a given set of datasets generated from the same ground truth network. Calls BLEval.computeSpearman.
-t, --time Analyze time taken by each algorithm for a. Calls BLEval.parseTime.
-e, --epr Compute median early precision. Calls BLEval.computeEarlyPrec.
-s, --sepr Analyze median (signed) early precision for activation and inhibitory edges. BLEval.computeSignedEPrec.
-m, --motifs Compute network motifs in the predicted top-k networks. Calls BLEval.computeNetMotifs.