LARRY dataset
Installation
pip distribution
pip install larry-dataset
Development version
git clone https://github.com/mvinyard/LARRY-dataset.git; cd LARRY-dataset
pip install -e .
Quickstart
Downloads pre-processed data from AllonKleinLab/paper-data to ./KleinLabData (by default). The data is formatted into AnnData and returned to the user. A .h5ad file is also saved, locally. The data downloading and conversion step take several minutes due to the large expression normed_counts matrices though this only happens once.
import larry
dataset = "in_vitro" # can also choose from: "in_vivo" or "cytokine_perturbation"
adata = larry.fetch(dataset)
AnnData object with n_obs × n_vars = 130887 × 25289
obs: 'Library', 'Cell barcode', 'Time point', 'Starting population', 'Cell type annotation', 'Well', 'SPRING-x', 'SPRING-y'
var: 'gene_name'
obsm: 'X_clone'
import larry
LARRY_LightningData = larry.LARRY_LightningDataModule()
LARRY_LightningData.prepare_data()
AnnData object with n_obs × n_vars = 130887 × 25289
obs: 'Library', 'Cell barcode', 'Time point', 'Starting population', 'Cell type annotation', 'Well', 'SPRING-x', 'SPRING-y'
var: 'gene_name'
uns: 'dataset', 'h5ad_path'
obsm: 'X_clone'
Preprocessing performed previously. Loading...done.
Under the hood, the LARRY_LightningData calls larry.fetch() and larry.pp.Yeo2021_recipe(), and if task == "fate_prediction", larry.pp.annotate_fate_test_train()
LARRY_LightningData.adata
Print the updated adata:
AnnData object with n_obs × n_vars = 130887 × 25289
obs: 'Library', 'Cell barcode', 'Time point', 'Starting population', 'Cell type annotation', 'Well', 'SPRING-x', 'SPRING-y', 'cell_idx', 'clone_idx'
var: 'gene_name', 'highly_variable', 'corr_cell_cycle', 'pass_filter'
uns: 'dataset', 'h5ad_path', 'highly_variable_genes_idx', 'n_corr_cell_cycle', 'n_hv', 'n_mito', 'n_pass', 'n_total', 'pp_h5ad_path'
obsm: 'X_clone', 'X_pca', 'X_scaled', 'X_umap'
Sources
Repositories
Reference
- Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F.D., Klein, A.M. Lineage tracing on transcriptional landscapes links state to fate during differentiation. Science 80 (2020). https://doi.org/10.1126/science.aaw3381
Please email Michael E. Vinyard (mvinyard@broadinstitute.org) with any questions or interests.
Metadata
Release files for LARRY-dataset 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| LARRY-dataset-0.0.1.tar.gz | 1.7 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| LARRY_dataset-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.5 MB
Release files / LARRY-dataset-0.0.1.tar.gz
| Download URL | LARRY-dataset-0.0.1.tar.gz |
|---|---|
| Size | 1.7 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
84335021f50bec30095f29b3b050c37285817bf56c76f7d577950d24b75fad76
|
|
BLAKE2b-256 checksum How to use checksums |
b4838b27b81f16eafbb327604487babe28922f8248040166563b85993d290db9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.10.6
|
Release files / LARRY_dataset-0.0.1-py3-none-any.whl
| Download URL | LARRY_dataset-0.0.1-py3-none-any.whl |
|---|---|
| Size | 1.8 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
920a84b335ddef8e783f0f66adc3366b60b453993289bdf7136e83eb993f6f0b
|
|
BLAKE2b-256 checksum How to use checksums |
1ca14822d6b091275f29a4a65b807122f568b690fbe80fb1b8c93d961164f18c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.10.6
|