SCANet: A workflow for single-cell co-expression based analysis
SCANet, a new python package that incorporates the inference of gene co-expression networks from single-cell gene expression data and a complete analysis of the identified modules through trait and cell type associations, hub genes detection, deciphering of co-regulatory signals in co-expression, and drug-gene interactions identification. This will likely accelerate network analysis pipelines and advance systems biology research.
Installation
To install SCANet.
pip install scanet
Finally, it should be possible to import drugstone to your python script.
import scanet
You can use
import scanet as sn
SCANet officially supports Python 3.6+.
Usage
A full detailed example of SCANet : Refer to this Jupyter Notebook.
In this Jupyter Notebook, we explored all aspects of gene coexpression networks (GCNs) using SCANet through a full analysis of the 3k PBMCs from 10x Genomics.
Metadata
Release files for scanet 0.1.11
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scanet-0.1.11.tar.gz | 11.5 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| scanet-0.1.11-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 23.3 MB
Release files / scanet-0.1.11.tar.gz
| Download URL | scanet-0.1.11.tar.gz |
|---|---|
| Size | 11.5 MB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/5.0.0 CPython/3.11.7
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Release files / scanet-0.1.11-py3-none-any.whl
| Download URL | scanet-0.1.11-py3-none-any.whl |
|---|---|
| Size | 11.8 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.11.7
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