The Self-Assembling-Manifold algorithm

scrnaseq, analysis, manifold, reconstruction
pip install sam-algorithm==0.8.4


Build Status

self-assembling-manifold -- SAM version 0.8.4

The Self-Assembling-Manifold (SAM) algorithm.

SAM is now on Scanpy!

import scanpy as sc
import scanpy.external as sce

#returns the SAM object if `inplace=True` and (SAM,AnnData) otherwise
sam =, inplace=True) #adata is your AnnData object


  • numpy
  • scipy
  • pandas
  • scikit-learn
  • umap-learn
  • numba
  • anndata

Optional dependencies

  • Interactive GUI (Jupyter notebooks)

    • plotly==4.0.0
    • ipythonwidgets
    • jupyter
    • colorlover
    • ipyevents
  • Plots

    • matplotlib
  • Clustering

    • louvain
    • leidenalg
    • hdbscan
    • cython
  • scanpy



Build the Docker image with:

git clone
cd Docker

Run the Docker image with:


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.


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

Download Anaconda from here:

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 sam-algorithm

Development version install:

git clone
cd self-assembling-manifold
python install

For plotting, install matplotlib:

pip install matplotlib

For interactive data exploration (in the 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

Please see the plotting tutorial for more information about the GUI interface.

SAM GUI example image


Please see the Jupyter notebooks in the 'tutorial' folder for basic tutorials. If you installed a fresh environment, do not forget to install jupyter into that environment! Please run

pip install jupyter

in your conda environment. The tutorial assumes that all optional dependencies are installed.

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.preprocess_data() # log transforms and filters the data #run with default parameters

Using preloaded pandas.DataFrame (cells x genes):

from samalg import SAM #import SAM
sam.preprocess_data() # log transforms and filters the data #run with default parameters

Using an existing AnnData object:

from samalg import SAM #import SAM
sam.preprocess_data() # log transforms and filters the data #run with default parameters

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

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

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)


If using the SAM algorithm, please cite the following eLife paper:

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.