Creates a k-nearest neighbors network for biological data and prepare it to be exported to Helios-Web (heliosweb.io).
We first apply a typical single-cell analysis preprocessing on the data. In particular, first we find and filter data by the highly variable genes (using Seurat approach), apply log1p transformation, and then apply PCA or TruncatedSVD to the data.
We are currently using 10000 highly variable genes and 100 PCA dimensions. [These values can be tuned for better cell type separation or better visualizations]
For highly variable genes, we are using Seurat approach (implemented in ScanPy): https://doi.org/10.1016/j.cell.2019.05.031
Next, we use approximate nearest neighbors according to cosine similarity to obtain the edges of the network. The method is described here: https://doi.org/10.1145/1963405.1963487 We are using the implementation from the PyNNDescent package.
We use Helios-Web (http://heliosweb.io) to visualize the obtained network. Currently we have a javascript implementation of a force-directed layout algorithm, which uses a single thread. The current implementation is based on the forceAtlas2 algorithm (https://doi.org/10.1371/journal.pone.0098679).
An alternative implementation of that in C is available and can be used for larger networks (The layout won't be interactive).
Code is available from: https://github.com/filipinascimento/bioknn
First download the data. Currently we are using the GTEx 8 tissues snRNA-Seq Data, which can be downloaded from:
https://gtexportal.org/home/downloads/adult-gtex/single_cell (GTEx_8_tissues_snRNAseq_atlas_071421.public_obs.h5ad)
Create a folder named Data Move the data file to that folder. Data should be in h5ad format.
Packages from requirements.txt should be installed with pip install -r requirements.txt
The current version uses a single script: generateKNN.py
After setting the parameters inside the script, run it with python Scripts/generateKNN.py
Parameters can be changed inside the script.
The script will generate a .xnet file in the Networks folder.
To run helios-web for visualization:
Preferentially using Chrome or Safari. Firefox should work but it remains untested.
Open heliosweb.io and choose a configuration (light, dark, density, etc)
Add ?use2d as argument to the heliosweb url to use the 2d mode. E.g. http://heliosweb.io/docs/example/?advanced&use2d
Drag and drop the generated .xnet file to the window. It should be replaced by the new network.
If the layout didn't start by itself, press space to run the algorithm. You can press space again to stop or resume.
- Convert into a package
- Will update Helios-Web with beta version which supports ForceAtlas2 without the degree normalization
- Include helios-web server (ailens), which should launch the browser with the network visualization
- Use the Helios-Core (https://github.com/filipinascimento/helios) library for layouting pre visualization