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Single-cell explorative data analysis for RNA-Seq

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# Scedar

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`Scedar` (Single-cell exploratory data analysis for RNA-Seq) is a reliable and easy-to-use Python package for efficient *quality control*, *visualization* and *clustering* of large-scale single cell RNA-seq (scRNA-seq) datasets.

## Install

Use PyPI:

`pip install scedar`

## Demo

![demo](docs/images/demo.png)

Workflow of using `scedar` to analyze an scRNA-seq dataset with 3005 mouse brain cells and 19,972 genes generated using the STRT-Seq UMI protocol by Zeisel et al. (2015). Procedures and parameters that are not directly related to data analysis are omitted. The full version of the demo is available at [docs/notebooks/mb3k-demo.ipynb](docs/notebooks/mb3k-demo.ipynb).

Data sources:

- Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., Rolny, C., Castelo-Branco, G., Hjerling-Leffler, J., and Linnarsson, S. (2015). Brain structure. [Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq](http://science.sciencemag.org/content/347/6226/1138). *Science*, **347**(6226), 1138–1142.
- [Hemberg Group scRNA-seq datasets](https://hemberg-lab.github.io/scRNA.seq.datasets/mouse/brain/#zeisel)

## Citation

Zhang, Y. and Taylor, D. M. (2018) Scedar: a scalable Python package for single-cell RNA-seq data analysis, *In preparation*.


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