sc-sam 2.0.2
The Self-Assembling-Manifold algorithm for single-cell RNA sequencing analysis
Requires Python: >=3.11
Keywords: analysis, manifold, reconstruction, scrnaseq, single-cell

[![Build Status](https://travis-ci.com/atarashansky/self-assembling-manifold.svg?branch=master)](https://travis-ci.com/atarashansky/self-assembling-manifold)

# self-assembling-manifold -- SAM version 1.0.1
The Self-Assembling-Manifold (SAM) algorithm.


## Requirements
 - `numpy`
 - `scipy`
 - `pandas`
 - `scikit-learn`
 - `umap-learn`
 - `numba`
 - `anndata`
 - `harmony`

### Optional dependencies
 - Interactive GUI (Jupyter notebooks)
   - `plotly==4.0.0`
   - `ipythonwidgets`
   - `jupyter`
   - `colorlover`
   - `ipyevents`
 
 - Plots
   - `matplotlib`

 - Clustering
   - `louvain`
   - `leidenalg`
   - `hdbscan`
   - `cython`

 - `scanpy`


## Installation

### Docker

Build the Docker image with:
```
git clone https://github.com/atarashansky/self-assembling-manifold.git
cd Docker
bash build_image.sh
```

Run the Docker image with:
```
bash run_image.sh
```
It will ask you to provide the image name, container name, port to run the Jupyter notebook server on, and the path to a directory that will be mounted onto the Docker container's file system.


### Anaconda
SAM requires python>=3.7. Python can be installed using Anaconda.

Download Anaconda from here:
    https://www.anaconda.com/download/

Create and activate a new environment with python3.7 as follows:
```
conda create -n environment_name python=3.7
conda activate environment_name
```

Having activated the environment, SAM can be downloaded from the PyPI repository using pip or, for the development version, downloaded from the github directly.

PIP install:
```
pip install sc-sam
```

Development version install:
```
git clone https://github.com/atarashansky/self-assembling-manifold.git
cd self-assembling-manifold
pip install -e .
```
For plotting, install `matplotlib`:

```
pip install matplotlib
```

For interactive data exploration (in the `SAMGUI.py` module), `jupyter`, `ipythonwidgets`, `colorlover`, `ipyevents`, and `plotly` are required. Install them in the previously made environment like so:

```
conda install -c conda-forge -c plotly jupyter ipywidgets plotly=4.0.0 colorlover ipyevents
```

### Enabling the SAM GUI in JupyterLab

If you use Jupyter Notebooks, these steps are not needed. If you would like to be able to run SAMGUI in JupyterLab, please do the following:

First install nodejs with:
`conda install nodejs`

To enable ipythonwidgets in Jupyter lab, please run the following:
```
jupyter labextension install @jupyter-widgets/jupyterlab-manager@1.0 --no-build
jupyter labextension install plotlywidget@1.1.0 --no-build
jupyter labextension install jupyterlab-plotly@1.1.0 --no-build
jupyter lab build
```

SAMGUI should now work in JupyterLab.

## Running the SAM GUI

The SAM GUI interface can be run in Jupyer notebooks with the following:

```
from samalg.gui import SAMGUI
sam_gui = SAMGUI(sam) # sam is your SAM object
sam_gui.SamPlot
```
Please see the plotting tutorial for more information about the GUI interface.

![SAM GUI example image](samgui.png)

# Basic usage

There are a number of different ways to load data into the SAM object. 

## Using the SAM constructor
### Using preloaded scipy.sparse or numpy expression matrix, gene IDs, and cell IDs:
```
from samalg import SAM #import SAM
sam=SAM(counts=(matrix,geneIDs,cellIDs))
sam.preprocess_data() # log transforms and filters the data
sam.run() #run with default parameters
sam.scatter()
```
### Using preloaded pandas.DataFrame (cells x genes):
```
from samalg import SAM #import SAM
sam=SAM(counts=dataframe)
sam.preprocess_data() # log transforms and filters the data
sam.run() #run with default parameters
sam.scatter()
```

### Using an existing AnnData object:
```
from samalg import SAM #import SAM
sam=SAM(counts=adata)
sam.preprocess_data() # log transforms and filters the data
sam.run() #run with default parameters
sam.scatter()
```

## Using the `load_data` function
### Loading data from a tabular file (e.g. csv or txt):
```
from samalg import SAM #import SAM
sam=SAM() #initialize SAM object
sam.load_data('/path/to/expression_data_file.csv') #load data from a csv file
#sam.load_data('/path/to/expression_data_file.txt', sep='\t') #load data from a txt file with tab delimiters
sam.preprocess_data() # log transforms and filters the data
sam.load_annotations('/path/to/annotations_file.csv')
sam.run()
sam.scatter()
```
### Loading an existing AnnData `h5ad` file: 

If loading tabular data (e.g. from a `csv`), `load_data` by default saves the sparse data structure to a `h5ad` file in the same location as the tabular file for faster loading in subsequent analyses. This file can be loaded as:

```
from samalg import SAM #import SAM
sam=SAM() #initialize SAM object
sam.load_data('/path/to/h5ad_file.h5ad') #load data from a h5ad file
sam.preprocess_data() # log transforms and filters the data
sam.run()
sam.scatter()
```

### Saving/Loading SAM
If you wish to save the SAM outputs and raw and filtered data, you can write `sam.adata` to a `h5ad` file as follows:
`sam.save_anndata(filename)`.

You can load this data back with `sam.load_data`:
`sam.load_data(filename)`

## Citation
If using the SAM algorithm, please cite the following eLife paper:
https://elifesciences.org/articles/48994

```
Tarashansky, A. J. et al. Self-assembling manifolds in single-cell RNA sequencing data. eLife 8, e48994 (2019).
```

## Adding extra functionality
As always, please submit a new issue if you would like to see any functionalities / convenience functions / etc added.