This is a tool for therapeutic target prioritization using network representation learning.
Download this repository, go to the directory it resides and run:
$ git clone https://github.com/guiltytargets/guiltytargets.git
$ cd guiltytargets
$ pip install -e .
After that, you can use it as a library in Python
from guiltytargets.pipeline import run
run(
input_directory,
targets_path,
ppi_graph_path,
dge_path,
auc_output_path,
probs_output_path,
max_adj_p=max_padj,
max_log2_fold_change=lfc_cutoff * -1,
min_log2_fold_change=lfc_cutoff,
entrez_id_header=entrez_id_name,
log2_fold_change_header=log_fold_change_name,
adj_p_header=adjusted_p_value_name,
base_mean_header=base_mean_name,
entrez_delimiter=split_char,
ppi_edge_min_confidence=confidence_cutoff,
)
This will create files in paths auc_output_path
and probs_output_path
, where the former shows the AUC values of cross validation and the latter shows the predicted targets.
The parameters are explained below. A use case can be found under https://github.com/GuiltyTargets/reproduction
There are 3 files which are necessary to run this program. All input files should be found under input_directory
ppi_graph_path
: A path to a file containing a protein-protein interaction network in the format of:EntrezID EntrezID CONFIDENCE
Such as:
216 216 0.76
3679 1134 0.73
55607 71 0.65
5552 960 0.63
2886 2064 0.9
5058 2064 0.73
1742 2064 0.87
An example of such a network can be found [here](http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/download.php)
dge_path
: A path to a file containing an experiment, in tsv format. Rows show individual entries, columns are the values of the following properties:
- Base mean
- Log fold change
- Adjusted p value
- Entrez id
The file may contain other columns too, but the indices and names of the above columns must be entered to the configuration file.
targets_path
: A path to a file containing a list of Entrez ids of known targets, in the format ofEntrezID1
EntrezID2
...
Such as:
1742
3996
150
152
151
The options that should be set are:
max_adj_p: Maximum value for adjusted p-value for a gene to be considered differentially expressed.
max_log2_fold_change: Maximum value for log2 fold change for a gene to be considered differentially expressed
min_log2_fold_change: Minimum value for log2 fold change for a gene to be considered differentially expressed
ppi_edge_min_confidence: Minimum confidence score for the edges in PPI network.
entrez_id_header: The column name for the Entrez id in the differential expression file.
log2_fold_change_header: The column name for the log2 fold change in the differential expression file.
adj_p_header: The column name for the adjusted p-value in the differential expression file.
base_mean_header: The column name for the base mean in the differential expression file.
entrez_delimiter: If there is more than one Entrez id per row in the diff. expr. file, the separator betweem them.