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https://github.com/iosonofabio/singlet
Single cell RNA Seq data analysis with quantitative phenotypes
https://github.com/iosonofabio/singlet
Last synced: 23 days ago
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Single cell RNA Seq data analysis with quantitative phenotypes
- Host: GitHub
- URL: https://github.com/iosonofabio/singlet
- Owner: iosonofabio
- License: mit
- Created: 2017-08-08T15:33:52.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-11-17T03:30:13.000Z (over 3 years ago)
- Last Synced: 2024-05-20T02:03:21.775Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 8.54 MB
- Stars: 12
- Watchers: 6
- Forks: 7
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome_single_cell - singlet - [Python] - Single cell RNA-Seq analysis with phenotypes. (Software packages / RNA-seq)
- awesome-single-cell - singlet - [Python] - Single cell RNA-Seq analysis with phenotypes. (Software packages / RNA-seq)
- awesome-single-cell - singlet - [Python] - Single cell RNA-Seq analysis with phenotypes. (Software packages / RNA-seq)
README
[](https://travis-ci.org/iosonofabio/singlet)
[](https://singlet.readthedocs.io/en/master)
[](https://coveralls.io/github/iosonofabio/singlet?branch=master)
[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/singlet/)
# Singlet
Single cell RNA-Seq analysis with quantitative phenotypes.
Examples:
- [oligo-barcoded antibodies](https://doi.org/10.1038/nmeth.4380)
- [electrophysiology](https://doi.org/10.1101/555110)
- [intracellular pathogen abundance](https://doi.org/10.7554/eLife.32942)
## Documentation
Hosted on [readthedocs](https://singlet.readthedocs.io/en/master).
## Features
The vision is to let you **explore** your data **your way** while providing support for repetitive tasks. Here a few things I do pretty regularly:
- quality control and filtering
- sample and feature filtering (e.g. querying by quantitative phenotypes in certain ranges)
- dataset splitting (e.g. by metadata) and merging
- bootstrapping
- normalization
- log/unlog transform
- summary statistics (mean expression, std, cv, fano index)
- feature selection
- clustering (e.g. k-means, affinity propagation)
- dimensionality reduction and feature weighting including phenotypes (e.g. PCA, tSNE, umap, SAM)
- k nearest neighbors (knn) graphs
- plotting dimensionality reductions colored by categorical or quantitative metadata
- plotting hierarchical clustering
- correlations of gene expression to gene expression or to quantitative phenotypes
- differential expression at the distribution level (e.g. Mann-Whitney test)
- load/write to loom files
- support for custom plugins to expand the list of features at runtime
## Requirements
Python 3.5+ is required. Moreover, you will need:
- [pyyaml](https://pyyaml.org/)
- [numpy](http://www.numpy.org/)
- [scipy](https://www.scipy.org/)
- [pandas](http://pandas.pydata.org/)
- [xarray](http://xarray.pydata.org/en/stable/)
- [scikit-learn](http://scikit-learn.org)
Optional dependencies:
- **plotting**:
- [matplotlib](https://matplotlib.org/)
- [seaborn](https://seaborn.pydata.org/)
- **dimensionality reduction/knn graphs**:
- [numba](https://numba.pydata.org/)
- [umap](https://github.com/lmcinnes/umap)
- [lshknn](https://github.com/iosonofabio/lshknn)
- **I/O of loom files**:
- [loompy](http://loompy.org/)
Get those from your Linux distribution, `pip`, `conda`, or any other source.
Singlet is pure Python for the time being. So it should work on any platform supported by its dependencies, in particular various Linux distributions, recent-ish OSX, and Windows. It is *tested* on Linux and OSX, but if you are a Windows user and know how to use AppVeyor let's set it up!
## Install
To get the latest **stable** version, use pip:
```bash
pip install singlet
```
To get the latest **development** version, clone the git repo and then call:
```bash
python3 setup.py install
```
## Usage example
You can have a look inside the `test` folder for examples. To start using the example dataset:
- Set the environment variable `SINGLET_CONFIG_FILENAME` to the location of the example YAML file
- Open a Python/IPython shell or a Jupyter notebook and type:
```python
import matplotlib.pyplot as plt
from singlet.dataset import Dataset
ds = Dataset(
samplesheet='example_PBMC2',
counts_table='example_PBMC2',
featuresheet='example_PBMC2',
)
ds.counts.log(inplace=True)
ds.samplesheet['cluster'] = ds.cluster.kmeans(axis='samples', n_clusters=5)
vs = ds.dimensionality.tsne(perplexity=15)
ax = ds.plot.scatter_reduced_samples(
vs,
color_by='cellType',
figsize=(5, 4),
)
plt.show()
```
This will calculate a t-SNE embedding of the log-transformed features and then show your samples in the reduced space, colored by cluster. It should look more or less like this:
## Similar packages
Singlet is similar to other packages like ``scanpy`` or ``seurat``. However, there are differences too:
- ``scanpy`` focuses on huge datasets and graphical methods. Singlet is not opinionated about graphs and works best with smaller datasets that include quantitative phenotypes (e.g. single cell size)
- ``seurat`` focuses on emanating a simple user experience. Singlet does try to take over repetitive tasks (e.g. data filtering) but refuses to perform strongly opinionated operations without explicit user consent (e.g. normalization using a particular statistical model).
- ``singlet`` tries to use object oriented programming to keep clean interfaces and has an open plugin structure.