Metadata-Version: 2.2
Name: scself
Version: 0.4.8
Summary: Self Supervised Tools for Single Cell Data
Home-page: https://github.com/GreshamLab/sc_self_supervised
Author: Chris Jackson
Author-email: cj59@nyu.edu
Maintainer: Chris Jackson
Maintainer-email: cj59@nyu.edu
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: anndata>=0.8
Requires-Dist: scanpy
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: tqdm
Requires-Dist: sparse_dot_mkl
Requires-Dist: numba
Dynamic: author
Dynamic: author-email
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: maintainer
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# scself

[![PyPI version](https://badge.fury.io/py/scself.svg)](https://badge.fury.io/py/scself)

Self Supervised Tools for Single Cell Data

Molecular Cross-Validation for PCs [arXiv manuscript](https://www.biorxiv.org/content/10.1101/786269v1)

```
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](https://arxiv.org/abs/1901.11365)

```
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](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008569)

Feature module and submodule determination using pearson correlation distance, kNN embedding, and leiden clustering
```
get_correlation_modules(
    adata,
    layer='X',
    n_neighbors=10,
    leiden_kwargs={},
    output_key='gene_module',
    obs_mask=None

)

get_correlation_submodules(
    adata,
    layer='X',
    n_neighbors=10,
    leiden_kwargs={},
    input_key='gene_module',
    output_key='gene_submodule',
    obs_mask=None
)
```

Feature module and submodule scoring
```
score_all_modules(
    adata,
    modules=None,
    module_column='gene_module',
    output_key_suffix='_score',
    obs_mask=None,
    layer='X',
    scaler=TruncMinMaxScaler(),
    fit_scaler=True,
    clipping=None
)

score_all_submodules(
    adata,
    modules=None,
    submodules=None,
    module_column='gene_module',
    submodule_column='gene_submodule',
    output_key_suffix='_score',
    obs_mask=None,
    layer='X',
    scaler=TruncMinMaxScaler(),
    fit_scaler=True,
    clipping=None
)
```
