SiFT - Biological signal filtering in single-cell data
Signal FilTering is a tool for uncovering hidden biological processes in single-cell data. It can be applied to a wide range of tasks, from the removal of unwanted variation as a pre-processing step, through revealing hidden biological structure by utilizing prior knowledge with respect to existing signal, to uncovering trajectories of interest using reference data to remove unwanted variation.
Visit our documentation for installation, tutorials, examples and more.
Manuscript
Please see our manuscript Zoe Piran and Mor Nitzan (2022).
Installation
Install SiFT via PyPI by running:
pip install sift-sc
1 maintainer
Metadata
1 maintainer
Release files for sift-sc 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| sift_sc-0.1.0.tar.gz | 15.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sift_sc-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 32.6 kB
Release files / sift_sc-0.1.0.tar.gz
| Download URL | sift_sc-0.1.0.tar.gz |
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| Size | 15.8 kB |
| Tags | Source |
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Release files / sift_sc-0.1.0-py3-none-any.whl
| Download URL | sift_sc-0.1.0-py3-none-any.whl |
|---|---|
| Size | 16.9 kB |
| Tags | Python 3 |
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twine/4.0.2 CPython/3.9.7
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