 model architecture of MUSE</strong></p>
<p>MUSE combines features from transcripts (x) and morphology (y) into a joint latent representation z.The self-reconstruction loss encourages the learned joint feature representation (z) to faithfully retain information from the original individual input feature modalities (x and y). The self-supervised learning exploits triple-loss functions to encourage cells with the same cluster label (i.e. with the same pseudo label in either l<sub>x</sub> or l<sub>y</sub>) to 