# 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)
```
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"description": "# Sagittarius\nGene expression time-series extrapolation for heterogeneous data\n\n## Introduction\nSagittarius 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.\n\nhttps://github.com/addiewc/Sagittarius\n\n## Installation Tutorial\n\n```\npip install Sagittarius-api\n```\n\n\n### System Requirements\nSagittarius 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.\n\n## How to use our code\n\n```\n# Use pretrained_model\n>>> from Sagittarius import simulate_measurements_webserver as smw\n>>> smw.EvoDevoSimulation('Chicken', 'Heart', 2.0).head()\n\"\"\"\n DPM1 GCLC NFYA NIPAL3 WNT16 ICA1 DBNDD1 ALS2 CFLAR TFPI ... MRC1 GAN MMP12 OTUD7B STRADA NCOA4 RASL10B MMP28 H0YAA0 GRIN2B\ntimepoint ... \n2.0 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\n\"\"\"\n```\n\n```\n# Use model and config\n>>> from Sagittarius import simulate_measurements\n>>> model_path = 'model.pth'\n>>> config_file = 'cfg.json'\n>>> species = ['chicken', 'human']\n>>> organ = ['brain', 'cerebellum']\n>>> timepoint = 2.0\n>>> adata_res = simulate_measurements.simulate_single_EvoDevo(\n model_path, config_file, species, organ, timepoint)\n```\n\n",
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