scVAEIT

What's Changed

Major updates

• Fix bug about masking #3 when computing latent embeddings
• Add MMD loss for batch correction in the latent space
• Add options for continuing training
• Add options for skip connections to improve imputation quality
• Fix bug about activation function (sigmoid for Bernoulli, scaled for NB, which was used in the initial version)

Minor updates

• v0.1.0 slight improvement https://github.com/jaydu1/scVAEIT/pull/5
• v0.2.0 add max_vals argument, which can be provided for each block
• Improve efficiency by decorating tf.function
• Improve documentation comments

Test

Time consumption on integrating DOGMA-seq, CITE-seq, and ASAP-seq datasets (30987 cells and 42598 features):

• On CPU with 12 cores and 128GB of RAM, 80s per epoch, ~11h for 500 epochs
• On NVIDIA GeForce RTX 4090, 10s per epoch, ~1.4h for 500 epochs
• On NVIDIA A100 (Colab pro+), 30s per epoch, ~4h for 500 epochs

What's Changed

Major updates

• Fix bug about masking #3 when computing latent embeddings
• Add MMD loss for batch correction in the latent space
• Add options for continuing training
• Add options for skip connections to improve imputation quality
• Fix bug about activation function (sigmoid for Bernoulli, scaled for NB, which was used in the initial version)

Minor updates

• v0.1.0 slight improvement https://github.com/jaydu1/scVAEIT/pull/5
• v0.2.0 add max_vals argument, which can be provided for each block
• Improve efficiency by decorating tf.function
• Improve documentation comments

Test

Time consumption on integrating DOGMA-seq, CITE-seq, and ASAP-seq datasets (30987 cells and 42598 features):

• On CPU with 12 cores and 128GB of RAM, 80s per epoch, ~11h for 500 epochs
• On NVIDIA GeForce RTX 4090, 10s per epoch, ~1.4h for 500 epochs
• On NVIDIA A100 (Colab pro+), 30s per epoch, ~4h for 500 epochs

What's Changed

Major updates

• Fix bug about masking #3 when computing latent embeddings
• Add MMD loss for batch correction in the latent space
• Add options for continuing training
• Add options for skip connections to improve imputation quality
• Fix bug about activation function (sigmoid for Bernoulli, scaled for NB, which was used in the initial version)

Minor updates

• v0.1.0 slight improvement https://github.com/jaydu1/scVAEIT/pull/5
• v0.2.0 add max_vals argument, which can be provided for each block
• Improve efficiency by decorating tf.function
• Improve documentation comments

Test

Time consumption on integrating DOGMA-seq, CITE-seq, and ASAP-seq datasets (30987 cells and 42598 features):

• On CPU with 12 cores and 128GB of RAM, 80s per epoch, ~11h for 500 epochs
• On NVIDIA GeForce RTX 4090, 10s per epoch, ~1.4h for 500 epochs
• On NVIDIA A100 (Colab pro+), 30s per epoch, ~4h for 500 epochs

What's Changed

Major updates

• Fix bug about masking #3 when computing latent embeddings
• Add MMD loss for batch correction in the latent space
• Add options for continuing training
• Add options for skip connections to improve imputation quality
• Fix bug about activation function (sigmoid for Bernoulli, scaled for NB, which was used in the initial version)

Minor updates

• v0.1.0 slight improvement https://github.com/jaydu1/scVAEIT/pull/5
• v0.2.0 add max_vals argument, which can be provided for each block
• Improve efficiency by decorating tf.function
• Improve documentation comments

Test

Time consumption on integrating DOGMA-seq, CITE-seq, and ASAP-seq datasets (30987 cells and 42598 features):

• On CPU with 12 cores and 128GB of RAM, 80s per epoch, ~11h for 500 epochs
• On NVIDIA GeForce RTX 4090, 10s per epoch, ~1.4h for 500 epochs
• On NVIDIA A100 (Colab pro+), 30s per epoch, ~4h for 500 epochs

Version as supplements to the paper