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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
        • Make sure you have loaded the latest version. If you are not sure visit here: https://pypi.org/project/favapy/
      • 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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