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https://github.com/atarashansky/self-assembling-manifold

The Self-Assembling-Manifold (SAM) algorithm.

https://github.com/atarashansky/self-assembling-manifold

Science Score: 36.0%

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Keywords from Contributors

transcriptomics bioinformatics
Last synced: 10 months ago · JSON representation

Repository

The Self-Assembling-Manifold (SAM) algorithm.

Basic Info
  • Host: GitHub
  • Owner: atarashansky
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 29.6 MB
Statistics
  • Stars: 44
  • Watchers: 3
  • Forks: 11
  • Open Issues: 3
  • Releases: 0
Created almost 8 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License

README.md

Build Status

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.

SAM GUI example image

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.loaddata('/path/to/expressiondata_file.csv') #load data from a csv file

sam.loaddata('/path/to/expressiondata_file.txt', sep='\t') #load data from a txt file with tab delimiters

sam.preprocessdata() # log transforms and filters the data sam.loadannotations('/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.

Owner

  • Login: atarashansky
  • Kind: user

GitHub Events

Total
  • Issues event: 2
  • Watch event: 2
Last Year
  • Issues event: 2
  • Watch event: 2

Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 677
  • Total Committers: 9
  • Avg Commits per committer: 75.222
  • Development Distribution Score (DDS): 0.415
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
atarashansky t****n@s****u 396
atarashansky a****y@g****m 242
atarashansky a****y@c****g 16
Fabio Zanini f****i@f****m 11
atarashans a****y@i****m 5
atarashansky a****y@c****m 2
atarashansky a****y@C****t 2
Alexander Tarashansky a****y@C****l 2
atarashansky a****y@a****l 1
Committer Domains (Top 20 + Academic)
czimacos3990.hsd1.ma.comcast.net: 1 chanzuckerberg.com: 1 illumina.com: 1 fastmail.fm: 1 czbiohub.org: 1 stanford.edu: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 41
  • Total pull requests: 6
  • Average time to close issues: about 1 month
  • Average time to close pull requests: about 23 hours
  • Total issue authors: 22
  • Total pull request authors: 3
  • Average comments per issue: 2.56
  • Average comments per pull request: 0.17
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 4
  • Pull requests: 1
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 4 days
  • Issue authors: 3
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • atarashansky (15)
  • Sayyam-Shah (3)
  • dnanes (2)
  • georgyFenix (2)
  • fenghuijian (2)
  • CisnerosFernandez (1)
  • andygxzeng (1)
  • avianalter (1)
  • xuesoso (1)
  • hl324 (1)
  • nkm47 (1)
  • qisun2 (1)
  • pythonhelp (1)
  • flying-sheep (1)
  • iosonofabio (1)
Pull Request Authors
  • iosonofabio (3)
  • atarashansky (2)
  • georgyFenix (2)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 230 last-month
  • Total dependent packages: 2
  • Total dependent repositories: 4
  • Total versions: 56
  • Total maintainers: 1
pypi.org: sam-algorithm

The Self-Assembling-Manifold algorithm

  • Versions: 56
  • Dependent Packages: 2
  • Dependent Repositories: 4
  • Downloads: 230 Last month
Rankings
Dependent packages count: 4.8%
Dependent repos count: 7.5%
Forks count: 10.5%
Stargazers count: 10.5%
Average: 11.3%
Downloads: 23.3%
Maintainers (1)
tarashan
Last synced: 10 months ago

Dependencies

setup.py pypi
  • anndata >=0.7.4
  • dill *
  • h5py <=2.10.0
  • harmonypy *
  • numba >=0.50.1
  • numpy >=1.19.0
  • packaging >=0.20.0
  • pandas >1.0.0
  • scikit-learn >=0.23.1
  • scipy >=1.3.1
  • umap-learn >=0.4.6
Docker/Dockerfile docker
  • debian buster-slim build