Reconstructing spatial proteomics through transfer learning
Project description
Flow2Spatial reconstructs spatial proteomics through transfer learning
Flow2Spatial is the computational part of SPRING (global spatial proteomics with thousands of high-resolution pixels by microfluidics and transfer learning).
It aims to reconstruct spatial proteomics from the values of parallel-flow projections in SPRING. Leveraging transfer learning, Flow2Spatial can restore fine structure of protein spatial distribution in different tissue types.
Overview of Flow2Spatial.
Prerequisites
"torch", "shapely", "scikit-image", "cvxpy",
"scanpy", "anndata", "scipy", "numpy", "pandas"
Further tutorials please refer to https://Flow2Spatial.readthedocs.io/.
Citation
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Flow2Spatial-0.1.tar.gz
(194.6 kB
view hashes)
Built Distribution
Flow2Spatial-0.1-py3-none-any.whl
(15.1 kB
view hashes)
Close
Hashes for Flow2Spatial-0.1-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 84d36fffaf4adc33c4158e08ed0ec322d08560a29ba0ea49493ea4bfe13fe12b |
|
| MD5 | 3b8ad64f43f969f116cc550ef540a3c9 |
|
| BLAKE2b-256 | 555a0420083c1203a789ffbd79d51f6599c1a4609f478590091e96028810d409 |
