https://github.com/mzguntalan/zytome
Zytome is declarative library for handling gene expression data for various data portals or datasets.
https://github.com/mzguntalan/zytome
bioinformatics cellxgene data-engineering data-science dataset gene-expression gtex
Last synced: 11 months ago
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Zytome is declarative library for handling gene expression data for various data portals or datasets.
- Host: GitHub
- URL: https://github.com/mzguntalan/zytome
- Owner: mzguntalan
- Created: 2025-08-10T11:04:23.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-08-22T05:15:26.000Z (11 months ago)
- Last Synced: 2025-08-22T05:39:03.255Z (11 months ago)
- Topics: bioinformatics, cellxgene, data-engineering, data-science, dataset, gene-expression, gtex
- Language: Python
- Homepage:
- Size: 19.5 KB
- Stars: 0
- Watchers: 0
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# zytome
Zytome is work-in-progress / work-as-i-go library for conveniently downloading data from several sources, filtering, modifying, and manipulating gene expression datasets. This library will grow as I continue to work with gene expression data and will contain essential functionality that are used frequently.
## Sample Usage
```python
# portal is static library that records basic information about datasets
# like name, tissues, assays, etc
# explorer is the main library for dealing with data
from zytome import portal, explorer
# cellxgene and gtex will be the primary target for now
from zytome.portal.cellxgene.human_oral_and_craniofacial_cell_atlas import oral_and_craniofacial_atlas
# downloads the data to your ENV VAR called Z_ZYTOME_DIR
# by default it creates a .zytome dir at `./` your current working directory
dX = explorer.load_data_from_portal(oral_and_craniofacial_atlas.Dataset())
# dX.filter takes filter functions as parameter
# explorer.make_filter is a utility function for making common filters
# you can make your own filters which are Callable[[AnnData], AnnData]
dX = dX.filter(explorer.make_filter(feature_types=["protein_coding"])
# common attributes are available such as tissues, assays, organism, disease, etc
tissues = dX.tissues
# .raw_normalized_by_feature_length gives you a numpy array
# .raw is also available (raw counts)
cells_by_types = [
dX.filter(
explorer.make_filter(
max_cells=5_00,
tissues=[tissue]
)
).raw_normalized_by_feature_length
for tissue in tissues
]
# these will give you gene names
gene_names = dX.feature_names
# this will give you the convention name like ensembl_id
gene_name_convention = dX.feature_name_name
```
## Installation
```bash
pip install --upgrade z-zytome
```