Self Supervised Tools for Single Cell Data
Project description
scself
Self Supervised Tools for Single Cell Data
Molecular Cross-Validation for PCs arXiv manuscript
mcv(
count_data,
n=1,
n_pcs=100,
random_seed=800,
p=0.5,
metric='mse',
standardization_method='log',
metric_kwargs={},
silent=False,
verbose=None,
zero_center=False
)
Noise2Self for kNN selection arXiv manuscript
def noise2self(
count_data,
neighbors=None,
npcs=None,
metric='euclidean',
loss='mse',
loss_kwargs={},
return_errors=False,
connectivity=False,
standardization_method='log',
pc_data=None,
chunk_size=10000,
verbose=None
)
Implemented as in DEWÄKSS
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
scself-0.4.5.tar.gz
(28.8 kB
view details)
Built Distribution
scself-0.4.5-py3-none-any.whl
(40.2 kB
view details)
File details
Details for the file scself-0.4.5.tar.gz
.
File metadata
- Download URL: scself-0.4.5.tar.gz
- Upload date:
- Size: 28.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.11.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1accb7466b6824f92c8ca4d0df618bfa23bcb2e531496e46a2b81c9806ac839b |
|
MD5 | 45465224a523883b29be50cf6ce557df |
|
BLAKE2b-256 | 080daebf3e6d6486a0409466a5d64c85d8ce413a6773c7a41d7d71024b776c4e |
File details
Details for the file scself-0.4.5-py3-none-any.whl
.
File metadata
- Download URL: scself-0.4.5-py3-none-any.whl
- Upload date:
- Size: 40.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.11.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | a6bca5400bc40b66a8b60b3bc851e84d96f413979a405a51c458a86ab3c937a4 |
|
MD5 | 42396acda2cf5872a0fc00af95eaef4a |
|
BLAKE2b-256 | 2fb5f55369c8525cf1865d41a2102b0a77902f3f8d06f1512063f4e67b9122e7 |