How to improve integration

[fe4960]

Hello,

Thanks for developing this great software. It has helped me a lot for integration of unpaired snRNA and snATAC. I recently run another dataset with my previous script following the scglue tutorial. The only difference between my script and the tutorial is that I used the 20% of top ranking variable peaks identified by episcanpy and 5000 hvg, which work quite well for all my previous scGlue analysis, since I have many cells and peaks. However, the result from the most recent analysis seems not optimal.

In the plot above, the "Unknown" label indicates the snATAC cells, while the rest labels are the snRNA cell types. It looks like snATAC doesn't integrate with snRNA well.

Here is the output of integration steps. Could you help take a look and suggest how to improve the integration? Thanks very much! @Jeff1995

[INFO] fit_SCGLUE: Pretraining SCGLUE model... [INFO] autodevice: Using GPU 1 as computation device. [INFO] check_graph: Checking variable coverage... [INFO] check_graph: Checking edge attributes... [INFO] check_graph: Checking self-loops... [INFO] check_graph: Checking graph symmetry... [INFO] SCGLUEModel: Setting graph_batch_size = 156794 [INFO] SCGLUEModel: Setting max_epochs = 48 [INFO] SCGLUEModel: Setting patience = 4 [INFO] SCGLUEModel: Setting reduce_lr_patience = 2 [INFO] SCGLUETrainer: Using training directory: "glue/pretrain" [INFO] SCGLUETrainer: [Epoch 10] train={'g_nll': 0.418, 'g_kl': 0.001, 'g_elbo': 0.419, 'x_rna_nll': 0.254, 'x_rna_kl': 0.005, 'x_rna_elbo': 0.259, 'x_atac_nll': 0.056, 'x_atac_kl': 0.0, 'x_atac_elbo': 0.056, 'dsc_loss': 0.693, 'vae_loss': 0.332, 'gen_loss': 0.298}, val={'g_nll': 0.417, 'g_kl': 0.001, 'g_elbo': 0.418, 'x_rna_nll': 0.254, 'x_rna_kl': 0.005, 'x_rna_elbo': 0.259, 'x_atac_nll': 0.057, 'x_atac_kl': 0.0, 'x_atac_elbo': 0.057, 'dsc_loss': 0.694, 'vae_loss': 0.333, 'gen_loss': 0.298}, 648.6s elapsed Epoch 00012: reducing learning rate of group 0 to 2.0000e-04. Epoch 00012: reducing learning rate of group 0 to 2.0000e-04. [INFO] LRScheduler: Learning rate reduction: step 1 Epoch 00019: reducing learning rate of group 0 to 2.0000e-05. Epoch 00019: reducing learning rate of group 0 to 2.0000e-05. [INFO] LRScheduler: Learning rate reduction: step 2 [INFO] SCGLUETrainer: [Epoch 20] train={'g_nll': 0.416, 'g_kl': 0.001, 'g_elbo': 0.417, 'x_rna_nll': 0.253, 'x_rna_kl': 0.005, 'x_rna_elbo': 0.258, 'x_atac_nll': 0.056, 'x_atac_kl': 0.0, 'x_atac_elbo': 0.056, 'dsc_loss': 0.692, 'vae_loss': 0.331, 'gen_loss': 0.296}, val={'g_nll': 0.416, 'g_kl': 0.001, 'g_elbo': 0.417, 'x_rna_nll': 0.253, 'x_rna_kl': 0.005, 'x_rna_elbo': 0.258, 'x_atac_nll': 0.057, 'x_atac_kl': 0.0, 'x_atac_elbo': 0.057, 'dsc_loss': 0.691, 'vae_loss': 0.332, 'gen_loss': 0.297}, 651.7s elapsed Epoch 00022: reducing learning rate of group 0 to 2.0000e-06. Epoch 00022: reducing learning rate of group 0 to 2.0000e-06. [INFO] LRScheduler: Learning rate reduction: step 3 Epoch 00025: reducing learning rate of group 0 to 2.0000e-07. Epoch 00025: reducing learning rate of group 0 to 2.0000e-07. [INFO] LRScheduler: Learning rate reduction: step 4 [INFO] EarlyStopping: Restoring checkpoint "21"... [INFO] EarlyStopping: Restoring checkpoint "21"... [INFO] fit_SCGLUE: Estimating balancing weight... [INFO] estimate_balancing_weight: Clustering cells... [INFO] estimate_balancing_weight: Matching clusters... [INFO] estimate_balancing_weight: Matching array shape = (28, 29)... [INFO] estimate_balancing_weight: Estimating balancing weight... [INFO] fit_SCGLUE: Fine-tuning SCGLUE model... [INFO] check_graph: Checking variable coverage... [INFO] check_graph: Checking edge attributes... [INFO] check_graph: Checking self-loops... [INFO] check_graph: Checking graph symmetry... [INFO] SCGLUEModel: Setting graph_batch_size = 156794 [INFO] SCGLUEModel: Setting align_burnin = 8 [INFO] SCGLUEModel: Setting max_epochs = 48 [INFO] SCGLUEModel: Setting patience = 4 [INFO] SCGLUEModel: Setting reduce_lr_patience = 2 [INFO] SCGLUETrainer: Using training directory: "glue/fine-tune" [INFO] SCGLUETrainer: [Epoch 10] train={'g_nll': 0.423, 'g_kl': 0.001, 'g_elbo': 0.424, 'x_rna_nll': 0.255, 'x_rna_kl': 0.005, 'x_rna_elbo': 0.26, 'x_atac_nll': 0.056, 'x_atac_kl': 0.0, 'x_atac_elbo': 0.056, 'dsc_loss': 0.675, 'vae_loss': 0.333, 'gen_loss': 0.3}, val={'g_nll': 0.422, 'g_kl': 0.001, 'g_elbo': 0.423, 'x_rna_nll': 0.254, 'x_rna_kl': 0.005, 'x_rna_elbo': 0.259, 'x_atac_nll': 0.057, 'x_atac_kl': 0.0, 'x_atac_elbo': 0.057, 'dsc_loss': 0.684, 'vae_loss': 0.333, 'gen_loss': 0.299}, 665.4s elapsed Epoch 00012: reducing learning rate of group 0 to 2.0000e-04. Epoch 00012: reducing learning rate of group 0 to 2.0000e-04. [INFO] LRScheduler: Learning rate reduction: step 1 Epoch 00018: reducing learning rate of group 0 to 2.0000e-05. Epoch 00018: reducing learning rate of group 0 to 2.0000e-05. [INFO] LRScheduler: Learning rate reduction: step 2 [INFO] EarlyStopping: Restoring checkpoint "16"... [INFO] EarlyStopping: Restoring checkpoint "16"...