We provide a computational tool - PathExt, which identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the TopNet, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract characteristic genes and pathways even when only a single sample is available, and (ii) it can be used to study a system even in the absence of an appropriate control.
The inputs to PathExt are (a) a directed gene network and (b) gene-centric omics data for the conditions of interest. The omics data can represent a variety of quantities pertaining to the node, such as gene expression level, differential expression, protein, metabolite level, etc., in one or more conditions. The output of PathExt is a sub-network, that we refer to as the TopNet, consisting of the most significant differential or active paths, and is interpreted based on the application context.
Executing a command without any arguments will generate a list of required inputs.
$ bash get_Activated_Response_TopNet.sh
argv[1] = microarray data file
argv[2] = name of perturbation sample to study
argv[3] = name of control sample
argv[4] = unweighted network file
argv[5] = percentile threshold
argv[6] = path length threshold
argv[7] = q-score cutoff
argv[8] = number of randomizations
argv[9] = output directory
argv[10] = file name for base response network (we'll put it in the output directory)
argv[11] = file name for TopNet (we'll put it in the output directory)
Inputs:
-
microarray data file
Tab-delimited file of normalized signal intensities, with header. Each column can correspond to a different condition (eg: individual patient samples), or a summarised value (eg: median expression across a cohort). -
name of perturbation sample to study
-
name of control sample
Column names for the perturbation of interest (eg: a particular patient ID) and control are given as inputs. -
unweighted network file
Knowledge-based network -
percentile threshold
Paths whose cost is below this percentile threshold will be used to compute the TopNet. -
path length threshold
Minimum number of edges a path should have for it to be considered for TopNet creation. A good default value is 2. -
q-score cutoff
FDR-corrected cutoff to be used to consider a path significantly different from a randomized background.
Output files generated:
-
Activated Response base network
Base network after integrating omics data with knowledge based network. Node weight used is N_i = SI x FC where N_i is the weight of node i, and SI is the normalized signal intensity, or expression level, of a particular gene. FC = SI_perturbed/SI_control is the fold change in expression values. Edge cost = 1/sqrt(N_i x N_j). Tab-delimited file. -
Activated Response TopNet
TopNet comprising of edges from highly up-regulated and statistically significant paths. Tab-delimited file. -
Pij_zscores_fdr.txt
Tab-delimited file with details of paths considered in TopNet creation (after applying path length and percentile thresholds). This file shows the z-score of the actual path cost vs randomized path costs, as well as the FDR-corrected q-score for each such path.
Example:
$ bash get_Activated_Response_TopNet.sh test_data/GSE71200_SI.txt GSM1829740 GSM1829696 test_data/small_Mtb_network.txt 0.5 2 0.05 100 test_data/results/ Activated_Response_base_network.txt Activated_Response_TopNet.txt
The example input omics data, test_data/GSE71200_SI.txt, is taken from GEO accession number GSE71200 [1]. It contains transcriptomic data for mycobacterium tuberculosis (M.tb) exposed for 16 hours to various concentrations of antibiotics.
GSM1829740 corresponds to M.tb exposed to twice the minimum inhibitory concentration of isoniazid.
The example input knowledge-based network, test_data/small_Mtb_network.txt, is a sub-network of the network published in [2].
$ bash get_Repressed_Response_TopNet.sh
argv[1] = microarray data file
argv[2] = name of perturbation sample to study
argv[3] = name of control sample
argv[4] = unweighted network file
argv[5] = percentile threshold
argv[6] = path length threshold
argv[7] = q-score cutoff
argv[8] = number of randomizations
argv[9] = output directory
argv[10] = file name for base response network (we'll put it in the output directory)
argv[11] = file name for TopNet (we'll put it in the output directory)
Example:
$ bash get_Repressed_Response_TopNet.sh test_data/GSE71200_SI.txt GSM1829740 GSM1829696 test_data/small_Mtb_network.txt 0.5 2 0.05 100 test_data/results/ Repressed_Response_base_network.txt Repressed_Response_TopNet.txt
Response TopNet - union of Activated and Repressed Response TopNets, provides a holistic view of the active, altered genes and processes.
$ python get_union_response_TopNet.py
argv[1] = activated response TopNet (weighted)
argv[2] = repressed respone TopNet (weighted)
argv[3] = output file for union response TopNet (unweighted)
Example:
$ python get_union_response_TopNet.py test_data/results/Activated_Response_TopNet.txt test_data/results/Repressed_Response_TopNet.txt test_data/results/Response_TopNet.txt
$ python get_highest_activity_TopNet.py
argv[1] = microarray data file (tab-delimited, with header)
argv[2] = name of sample to study
argv[3] = unweighted (directed) network file
argv[4] = percentile threshold
argv[5] = path length threshold
argv[6] = output file for highest activity base network
argv[7] = output file for HA TopNet
Example:
$ python get_highest_activity_TopNet.py test_data/GSE71200_SI.txt GSM1829740 test_data/small_Mtb_network.txt 0.5 2 test_data/results/HA_base_network.txt test_data/results/HA_TopNet.txt
Python 3.6.9
Pandas 0.25.3
Networkx 1.11
Numpy 1.17.4
Random
Sys
Math
If you find this code useful, please cite
Sambaturu, Narmada, et al. "PathExt: a general framework for path-based mining of omics-integrated biological networks." bioRxiv (2020).
[1] Ma S, Minch KJ, Rustad TR, Hobbs S et al. Integrated Modeling of Gene Regulatory and Metabolic Networks in Mycobacterium tuberculosis. PLoS Comput Biol 2015 Nov;11(11):e1004543. PMID: 26618656
[2] Mishra, S., Shukla, P., Bhaskar, A., Anand, K., Baloni, P., Jha, R. K., Mohan, A., Rajmani, R. S., Nagaraja, V., Chandra, N., et al. (2017). Efficacy of β-lactam/β-lactamase inhibitor combination is linked to whib4-mediated changes in redox physiology of mycobacterium tuberculosis. Elife, 6, e25624.