LARRY-dataset 0.1.0.dev5
LARRY Dataset: lineage and RNA recovery
License: MIT
Requires Python: >3.10

# <img alt="LARRY-dataset-logo" src="/docs/imgs/LARRY_logo.svg" width="300">

This is a Python package that downloads and preprocesses the LARRY dataset from the AllonKleinLab GitHub repository. The LARRY dataset is a group of single-cell lineage-traced datasets that have been used to study transcriptional landscapes and cell fate during differentiation [[1](#References)]. There are three datasets within this group of datasets, all using hematopoietic progenitor cells from mouse bone marrow and the LARRY lentiviral barcoding strategy.

1. ***in vitro***
2. ***in vivo*** - transplanted into mice
3. **Cytokine-perturbed** (*in vitro*) - split between various cytokine culture conditions.

The package includes functions to download the LARRY dataset, format the files into [`AnnData`](https://anndata.readthedocs.io/en/latest/) and perform preprocessing / gene-filtering steps. The preprocessed data can then be split into test and training sets for use in different machine learning tasks. Dimension reduction can be performed after data splitting to respect information leakage or on all of the data.

[![PyPI pyversions](https://img.shields.io/pypi/pyversions/larry-dataset.svg)](https://pypi.python.org/pypi/larry-dataset/)
[![PyPI version](https://badge.fury.io/py/larry-dataset.svg)](https://badge.fury.io/py/larry-dataset)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

## Installation

#### [`pip`]() distribution
```BASH
pip install larry-dataset
```

#### Development version
```BASH
git clone https://github.com/mvinyard/LARRY-dataset.git; cd LARRY-dataset

pip install -e .
```

## Quickstart
Downloads pre-processed data from [**AllonKleinLab**/paper-data](https://github.com/AllonKleinLab/paper-data/tree/master/Lineage_tracing_on_transcriptional_landscapes_links_state_to_fate_during_differentiation) to `./KleinLabData` (by default). The data is formatted into [`AnnData`](https://anndata.readthedocs.io/en/latest/) 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.

```python
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'
```

```python
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()` 

```python
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
* [**AllonKleinLab**/paper-data](https://github.com/AllonKleinLab/paper-data/tree/master/Lineage_tracing_on_transcriptional_landscapes_links_state_to_fate_during_differentiation)
* [**AllonKleinLab**/LARRY](https://github.com/AllonKleinLab/LARRY)

#### References
1. Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F.D., Klein, A.M. <a href="https://science.sciencemag.org/content/367/6479/eaaw3381">**Lineage tracing on transcriptional landscapes links state to fate during differentiation**</a>. *Science* **80** (2020). https://doi.org/10.1126/science.aaw3381

---

Please email Michael E. Vinyard (**mvinyard@broadinstitute.org**) with any questions or interests.