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drugstone-0.3.2
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The python package for the https://drugst.one/ platform.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | drugstone-0.3.2 |
| نام | drugstone |
| نسخه کتابخانه | 0.3.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Ugur Turhan |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/t-ugur/drugstone.git |
| آدرس اینترنتی | https://pypi.org/project/drugstone/ |
| مجوز | - |
# Drugstone
This is the python package for the drugst.one platform.
This package offers tools for drug-repurposing and
is a programmatic approach to the functionality of the web portal.
For more information visit: https://drugst.one/
## Installation
Drugstone depends on a few packages to work.
You can use pip to install them.
```console
pip install urllib3 requests pandas pyvis upsetplot
```
Then you can install drugstone.
```console
pip install drugstone
```
Finally, it should be possible to import drugstone to your python script.
````python
import drugstone
````
You can use
```python
import drugstone as ds
```
to access the complete drugstone API with the `ds.` notation.
Drugstone officially supports Python 3.6+.
## Supported features
Drugstone offers a toolbox for drug repurposing applications.
- Search for drugs, interacting with a list of genes
- Search for drug targets, for a list of genes
- Visualize data in common formats like JSON or CSV
- create interaction graphs for drug and gene interactions
## Available Datasets
Protein-protein interactions (ppi_dataset):
``` NeDRex, BioGRID, IID, IntAct, STRING, APID```
Protein-drug interactions (pdi_dataset):
```NeDRex, DrugBank, Drug Central, ChEMBL, DGIdb```
Please note that some of the datasets require you to accept their terms and conditions before usage. ```DrugBank``` can only be used if the license has been agreed to and since ```NeDRex``` includes ```DrugBank``` data, only a part of ```NeDRex``` is available without agreeing to our license.
The terms and conditions can be read by calling
```drugstone.print_license()```
and can be accepted after reading with
```drugstone.accept_license()```.
## Start a new task
With Drugstone it is easy and convenient to search for drugs or drug-targets,
starting with a list of genes.
```python
from drugstone import new_task
genes = [
"CFTR", "TGFB1", "SCNN1B",
"DCTN4", "SCNN1A", "SCNN1G",
"CLCA4", "TNFRSF1A", "FCGR2A"
]
parameters = {
"target": "drug",
"algorithm": "trustrank"
}
task = new_task(genes, parameters)
r = task.get_result()
genes = r.get_genes()
drugs = r.get_drugs()
# save directly to files
r.download_json()
r.download_graph()
```
## Start multiple tasks
You can start multiple tasks at once, either with
completely independent parameters or
with same parameters and different algorithms.
### Multiple algorithms
By defining an *algorithms* value in the parameters dictionary,
you can pass a list of algorithm values.
For every algorithm, a task will be started, with
otherwise same parameter values.
````python
from drugstone import new_tasks
genes = [
"CFTR", "TGFB1", "SCNN1B",
"DCTN4", "SCNN1A", "SCNN1G",
"CLCA4", "TNFRSF1A", "FCGR2A"
]
parameters = {
"target": "drug",
"algorithms": ["trustrank", "closeness", "degree"]
}
tasks = new_tasks(genes, parameters)
r = task.to_dict()
r.download_json()
````
### Independent parameters
`new_tasks()` accepts a list of parameter dictionaries.
For every dictionary a task will be started.
````python
from drugstone import new_tasks
genes = [
"CFTR", "TGFB1", "SCNN1B",
"DCTN4", "SCNN1A", "SCNN1G",
"CLCA4", "TNFRSF1A", "FCGR2A"
]
p1 = {
"target": "drug",
"ppiDataset": 'nedrex',
"pdiDataset": "drugcentral"
}
p2 = {
"target": "drug",
"ppiDataset": 'IID',
"pdiDataset": "chembl"
}
p3 = {
"target": "drug",
"ppiDataset": 'apid',
"pdiDataset": "dgidb"
}
tasks = new_tasks(genes, [p1, p2, p3])
r = tasks.get_result()
r.to_dict()
r.download_json()
# only with Python 3.6
r.create_upset_plot()
````
### Union and intersection of tasks
You can get the union or intersection of tasks.
That returns a TaskResult with the according result.
````python
from drugstone import new_tasks
genes = [
"CFTR", "TGFB1", "SCNN1B",
"DCTN4", "SCNN1A", "SCNN1G",
"CLCA4", "TNFRSF1A", "FCGR2A"
]
parameters = {
"target": "drug",
"algorithms": ["trustrank", "closeness", "degree"]
}
tasks = new_tasks(genes, parameters)
u = tasks.get_union()
u.download_json()
i = tasks.get_intersection()
i.download_json()
````
## Combine a drug-target search with a drug search
This will perform a drug-target search for the seed genes
and then use the drug-target search results and the seed genes
to perform a drug-search.
Finally, a Task with the drug-search results will be returned.
````python
from drugstone import deep_search
genes = [
"CFTR", "TGFB1", "SCNN1B",
"DCTN4", "SCNN1A", "SCNN1G",
"CLCA4", "TNFRSF1A", "FCGR2A"
]
parameters = {
"algorithm": "trustrank"
}
task = deep_search(genes, parameters)
r = tasks.get_result()
r.to_dict()
r.download_json()
# only with Python 3.6
r.create_upset_plot()
````
## Available Parameters
````
parameters = {
"identifier": "symbol", #("entrez" | "uniprot" | "ensg" will be supported in future versions)
"algorithm": "adjacentDrugs", "trustrank" | "multisteiner" | "keypathwayminer" | "closeness" | "degree" | "proximity" | "betweenness",
"ppiDataset": "NeDRex",
"pdiDataset": "NeDRex",
"resultSize": 20,
"target": "drug" | "drug-target",
"includeIndirectDrugs": True | False,
"includeNonApprovedDrugs": True | False,
"maxDeg": sys.maxsize, # filter out nodes with high degrees
"hubPenalty": 0.0, # penalize hub nodes
"filterPaths": True | False, # include only shortest connections in the result
"damping_factor": 0.85, # only in trustrank
"num_trees": 5, # only in multisteiner
"tolerance": 10, # only in multisteiner
"k": 5, # only in keypathwayminer
}
````
For more information about the algorithms, please refer to <a href="https://drugst.one/doc#implementation_algorithms">https://drugst.one/doc#implementation_algorithms</a>.
For more information abouyt the available dataset types, please refer to <a href="https://drugst.one/doc#implementation_datasources">https://drugst.one/doc#implementation_datasources</a>.
## class Task
Represents a task.
`get_result() -> TaskResult` \
Returns a TaskResult for the result of the task.
`get_info() -> dict` \
Returns a dict with information about the task.
`get_parameters() -> dict` \
Returns a dict with the parameters of the task.
## class TaskResult
Represents the results of a task.
`get_genes() -> dict` \
Returns a dict with the genes.
`get_drugs() -> dict` \
Returns a dict with the drugs.
`to_dict() -> dict` \
Returns a dict with the result.
`to_pandas_dataframe() -> DataFrame` \
Returns a pandas DataFrame of the result.
`download_json(path: str, name: str) -> None` \
Downloads a json file with the result.
`download_genes_csv(path: str, name: str) -> None` \
Downloads a csv file with the genes of the result.
`download_drugs_csv(path: str, name: str) -> None` \
Downloads a csv file with the drugs of the result.
`download_edges_csv(path: str, name: str) -> None` \
Downloads a csv file with the edges of the result.
`download_graph(path: str, name: str) -> None` \
Downloads a html file with a graph of the nodes.
## class Tasks
Wraps a list of Task objects.
`get_result() -> TasksResult` \
Returns a TasksResult for the list of tasks.
`get_union() -> TaskResult` \
Returns a TaskResult with the union of the tasks.
`get_intersection() -> TaskResult` \
Returns a TaskResult with the intersection of the tasks.
## class TasksResult
Represents the results of a list of Task objects.
`get_tasks_list() -> List[Task]` \
Returns the list of tasks.
`to_dict() -> dict` \
Returns a dict with the results of the tasks.
`download_json(path: str, name: str) -> None` \
Downloads a json file with the results.
`create_upset_plot() -> None` \
Opens a new window with an upset plot of the results.
Copyright: 2023 - Institute for Computational Systems Biology
by Prof. Dr. Jan Baumbach \
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl drugstone-0.3.2:
pip install drugstone-0.3.2.whl
نصب پکیج tar.gz drugstone-0.3.2:
pip install drugstone-0.3.2.tar.gz