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!

There are two options:

• Locally

Note: This requires standard scientific Python 3 environment. A simple way of getting that is installing Anaconda.

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 .

The notebook NMFreg_Tutorial_cerebellum_puck180430_6.ipynb goes over a cerebellum example. The basic steps are:

1. Run NMF 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 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.

This work is featured in the flagship paper for Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.