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https://github.com/tudaga/NMFreg_tutorial
A tutorial on NMFreg applied to cerebellum data.
https://github.com/tudaga/NMFreg_tutorial
Last synced: 16 days ago
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A tutorial on NMFreg applied to cerebellum data.
- Host: GitHub
- URL: https://github.com/tudaga/NMFreg_tutorial
- Owner: tudaga
- License: mit
- Created: 2019-07-19T18:47:35.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-04-07T01:03:30.000Z (about 4 years ago)
- Last Synced: 2024-02-29T21:33:50.219Z (4 months ago)
- Language: Jupyter Notebook
- Size: 43.9 MB
- Stars: 11
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-single-cell - NMFreg - [Python] - The method is proposed in [Slide-seq](https://science.sciencemag.org/content/363/6434/1463) paper and reconstructs expression of each Slide-seq bead as a weighted combination of metagene factors, each corresponding to the expression signature of an individual cell type, defined from scRNA-seq. (Software packages / Spatial transcriptomics)
- awesome-single-cell - NMFreg - [Python] - The method is proposed in [Slide-seq](https://science.sciencemag.org/content/363/6434/1463) paper and reconstructs expression of each Slide-seq bead as a weighted combination of metagene factors, each corresponding to the expression signature of an individual cell type, defined from scRNA-seq. (Software packages / Spatial transcriptomics)
README
# NMFreg tutorial
Did you ever want to try NMFreg on your data? Here is the tutorial!
**Coming soon!** Examples of other applications :)
Do you have an application where NMFreg might help deconvolve your composite measurements aided by a labeled reference? Send me an email!
## How do I run this?
There are two options:
* **Locally**
Note: This requires standard scientific Python 3 environment. A simple way of getting that is installing [Anaconda](https://www.anaconda.com/distribution/#download-section).
Run the following commands in your terminal:
```
git clone https://github.com/tudaga/NMFreg_tutorial
cd NMFreg_tutorial
jupyter notebook NMFreg_Tutorial_cerebellum_puck180430_6.ipynb
```
* **Remotely** via Google Colab
Click on 
.
## Intro
The notebook [NMFreg_Tutorial_cerebellum_puck180430_6.ipynb](https://github.com/tudaga/NMFreg_tutorial/blob/master/NMFreg_Tutorial_cerebellum_puck180430_6.ipynb) goes over a cerebellum example. The basic steps are:
1. Run [NMF](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization) on a labeled single-cell RNA-seq cerebellum dataset to derive an interpretable basis.
2. Regress the Slide-seq beads onto the basis via [NNLS](https://en.wikipedia.org/wiki/Non-negative_least_squares) to deconvolve each bead into proportional contributins from each cell type.
3. *Bonus* Get a heuristic measure on the certainty that a bead contains mRNA from a single celltype.
If you want to learn more about NMF, watch my lecture on it [here](https://www.youtube.com/watch?v=9f4Rwt0yqr4).
## Reference
This work is featured in the flagship paper for [Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution](https://science.sciencemag.org/content/363/6434/1463).