Metadata-Version: 2.1
Name: Sagittarius-api
Version: 0.16
Home-page: https://github.com/addiewc/Sagittarius
Author: Sheng Wang Lab
Author-email: mingxz8@uw.edu
License: MIT
Keywords: example project
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
Requires-Dist: torch (==1.9.0)
Requires-Dist: numpy (>=1.21.2)
Requires-Dist: pandas (>=1.3.3)
Requires-Dist: scikit-learn (>=0.24.2)
Requires-Dist: matplotlib (>=3.4.3)
Requires-Dist: seaborn (>=0.11.2)
Requires-Dist: umap-learn (>=0.5.1)
Requires-Dist: anndata (>=0.8.0)
Requires-Dist: statsmodels (>=0.13.0)
Requires-Dist: tqdm (>=4.62.3)

# Sagittarius
Gene expression time-series extrapolation for heterogeneous data

## Introduction
Sagittarius is a model for temporal gene expression extrapolation simulate unmeasured gene expression data from unaligned, heterogeneous time series data. This is a python repository to simulate transcriptomic profiles at time points outside of the range of time points available in the measured data.

https://github.com/addiewc/Sagittarius

## Installation Tutorial

```
pip install Sagittarius-api
```


### System Requirements
Sagittarius is implemented using Python 3.9 on LINUX. Sagittairus expects torch==1.9.1+cu11.1, numpy==1.21.2, pandas==1.3.3, scikit-learn=0.24.2, matplotlib==3.4.3, seaborn==0.11.2, umap-learn=0.5.1, anndata=0.8.0, statsmodels==0.13.0, tqdm==4.62.3, and so on. For best performance, Sagittarius can be run on a GPU. However, all experiments can also be run on a CPU by not setting the `--gpu` flag. Typical installation requires approximately 5 minutes.

## How to use our code

```
# Use pretrained_model
>>> from Sagittarius import simulate_measurements_webserver as smw
>>> smw.EvoDevoSimulation('Chicken', 'Heart', 2.0).head()
"""
            DPM1     GCLC      NFYA    NIPAL3     WNT16      ICA1    DBNDD1     ALS2     CFLAR     TFPI  ...      MRC1       GAN     MMP12   OTUD7B    STRADA     NCOA4   RASL10B    MMP28    H0YAA0    GRIN2B
timepoint                                                                                                   ...                                                                                                  
2.0        1.583706  1.51412  1.448546  1.438742  0.432965  1.305412  1.182693  1.27424  1.346429  1.37519  ...  0.369062  0.726691 -0.025032  1.20409  1.493279  1.722517  1.391992  1.04687  1.558734  0.317554
"""
```

```
# Use model and config
>>> from Sagittarius import simulate_measurements
>>> model_path = 'model.pth'
>>> config_file = 'cfg.json'
>>> species = ['chicken', 'human']
>>> organ = ['brain', 'cerebellum']
>>> timepoint = 2.0
>>> adata_res = simulate_measurements.simulate_single_EvoDevo(
    model_path, config_file, species, organ, timepoint)
```

