https://github.com/grosstor/identiflow
Identifiability and experimental design in perturbation studies
https://github.com/grosstor/identiflow
experimental-design identifiability matroid maximum-flow network-inference reverse-engineering systems-biology
Last synced: about 1 month ago
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Identifiability and experimental design in perturbation studies
- Host: GitHub
- URL: https://github.com/grosstor/identiflow
- Owner: GrossTor
- License: mit
- Created: 2019-12-14T13:19:02.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-09-25T15:19:02.000Z (over 4 years ago)
- Last Synced: 2025-02-10T01:17:46.274Z (3 months ago)
- Topics: experimental-design, identifiability, matroid, maximum-flow, network-inference, reverse-engineering, systems-biology
- Language: Python
- Homepage:
- Size: 92.8 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# IdentiFlow
Interaction strengths between nodes in directed networks can be quantified from observations of the network's response to perturbations. This package reveals the identifiability of the inferred network parameters and optimizes experimental design for network perturbation studies. See our [publication](https://doi.org/10.1093/bioinformatics/btaa404) for details.
You can install the IdentiFlow package with [pip](https://pypi.org/project/pip/):
```
pip install git+https://github.com/GrossTor/IdentiFlow#egg=identiflow
```
The package is easy to use and we demonstrate its most relevant features in the example below. You find the example script in [identiflow/examples](identiflow/examples). This folder also contains the scripts that were used to analyse the KEGG pathways, as described in our [paper](https://www.biorxiv.org/content/10.1101/2020.02.03.931816v1).
After successful installation of the package you are able to import it in your Python session.
```python
import identiflow
```
### Input
First we define the network topology as a networkx Digraph and specify perturbations and their targets in a dictionary.
```python
import networkx as nx
edges = [('node 0', 'node 1'),
('node 0', 'node 2'),
('node 0', 'node 3'),
('node 1', 'node 3'),
('node 2', 'node 3'),
('node 3', 'node 0'),
('node 4', 'node 3'),
('node 4', 'node 5'),
('node 5', 'node 4')]
perturbations = {'P0': {'node 0', 'node 3'},
'P1': {'node 2'},
'P2': {'node 3', 'node 4'}}
net = nx.DiGraph(edges)
#There must be no self_loops. The next line ensures it.
net.remove_edges_from(nx.classes.selfloop_edges(net))
```
## Identifiability
Next, we investigate which of the interaction and perturbation strengths are identifiable in this perturbation experiment. This can be done with the function `infer_identifiability` (or `infer_identifiability_by_simulation`, see function documentation for details). It returns dictionaries that specify the dimensionality of the associated solution spaces and the identifiability status. The latter can be depicted by the plotting function `draw_identifiability_graph`.
```python
sol_space_dims, identifiability = \
identiflow.infer_identifiability(net,perturbations)
sol_space_dims_simu, identifiability_simu = \
identiflow.infer_identifiability_by_simulation(net,perturbations)
identiflow.draw_identifiability_graph(identifiability)
```
To elucidate the identifiability relationships between groups of network parameters we can can determine the cyclic flats using the function `infer_identifiability_relationships` and plot them with `draw_lattice`.
```python
cyclic_flats_dict = \
identiflow.infer_identifiability_relationships(net, perturbations)
for node in cyclic_flats_dict:
if sol_space_dims[node]>0:
fig,ax=identiflow.draw_lattice(cyclic_flats_dict[node])
```
## Experimental design
IdentiFlow also allows to identify perturbation sequences that maximize the number of identifiable parameters with a minimal number of perturbations. The function `optimize_experimental_design` has different options to do so, that are described in detail in its documentation. Here we try a few and compare their performances.
```python
nodes = list(net.nodes)
#we allow all single target perturbations
perturbations = {'P{0}'.format(i):{nodes[i]} for i in range(len(nodes))}
exhaustive = identiflow.optimize_experimental_design(net, perturbations,
strategy='exhaustive',sampling=False)
greedy = identiflow.optimize_experimental_design(net, perturbations,
strategy='greedy',sampling=False)
multi_target = identiflow.optimize_experimental_design(net, perturbations,
strategy='multi_target',sampling=False)
naive = identiflow.optimize_experimental_design(net, perturbations,
strategy='naive',sampling=False)
random = identiflow.optimize_experimental_design(net, perturbations,
strategy='random',sampling=True, n_samples=1)
import pprint
print('\nPerformance:\n\n exhaustive: {0}\n greedy: {1}\n multi_target: {2}\n naive: {3}\n random: {4}'.format(
exhaustive['ident_AUC'],greedy['ident_AUC'],multi_target['ident_AUC'],naive['ident_AUC'], random['ident_AUC']))
print('\nBest perturbation sequences:\n\n greedy:\n')
pprint.pprint(greedy['best_pert_seqs'])
print('\n multi-target:\n')
pprint.pprint([tuple(set(combi) for combi in seq) for seq in multi_target['best_pert_seqs']])
```
Performance:
exhaustive: 0.7592592592592593
greedy: 0.7592592592592593
multi_target: 0.8148148148148148
naive: 0.7407407407407407
random: 0.48148148148148145
Best perturbation sequences:
greedy:
[('P5', 'P0', 'P1', 'P2', 'P4'),
('P5', 'P0', 'P2', 'P1', 'P4'),
('P4', 'P0', 'P1', 'P2', 'P5'),
('P4', 'P0', 'P2', 'P1', 'P5')]
multi-target:
[({'P5'}, {'P0', 'P4'}, {'P2'}, {'P1'}),
({'P5'}, {'P0', 'P4'}, {'P1'}, {'P2'}),
({'P4'}, {'P0', 'P5'}, {'P2'}, {'P1'}),
({'P4'}, {'P0', 'P5'}, {'P1'}, {'P2'})]