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 sam-algorithm
Development version install:
git clone https://github.com/atarashansky/self-assembling-manifold.git
cd self-assembling-manifold
python setup.py install
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.
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()
load_data
function
Using the 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()
h5ad
file:
Loading an existing AnnData 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.