Outline of the SupirFactor framework. The SupirFactor model is constructed like an autoencoder where we embed gene expression data on the transcription factor manifold, exploring two architectures, the hierarchical A, and the shallow B architecture. The output of the first layer defines the latent features marked as TFs (Transcription Factors) and the activation is the transcription factor activity (TFA). The prior connect the evidence of TF to a set of informative downstream genes, with learnable weights . For A, connects the TFs to the latent features, here called the meta TFs (mTFs). weights the mTF activity (mTFA) to predict genome wide gene expression profiles.
