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Introduction
Installation
Using FAVA as a Python library
Command Line Interface
API
VAE
cook()
Tutorials
First case: Using counts matrix as input for creating co-expression network
The input in this case should be a tab-separated matrix with observations as columns and variables as rows. In other words, in the rows should be the entities that will be the nodes of our network.
Let’s import FAVA
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Our data is a single-cell RNAseq dataset.
Our matrix looks like this:
Run FAVA
Second case: Using anndata as input for creating co-expression network
If input is an AnnData object, it contains an expression matrix X, which stores n_obs observations (cells) of n_vars variables (genes).
Here we follow the steps as described in the scanpy tutorial here: https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html
Run FAVA
Now we have our network! Here we remove the AB-BA interactions (keeping only AB)
Finding modules on the netwrok
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cook() (in module fava)
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fava
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VAE (class in fava)
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