Tybalt: training and evaluating a variational autoencoder for pan-cancer gene expression data
Tybalt employs Keras deep learning framework to implement a variational autoencoder (VAE), capable of generating meaningful latent spaces for image and text data. Tybalt has been trained on The Cancer Genome Atlas (TCGA) pan-cancer RNA-seq data and used to identify specific patterns in the VAE encoded features.
References:
- Gregory P. Way, USACasey S. Greene
Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders
Pacific Simposium on Biocomputing 2014, 23: 80–91
Documentation
Important Notes
- Module Name: Tybalt (see the modules page for more information)
- Unusual environment variables set
- TYBALT_HOME installation directory
- TYBALT_BIN executable directory
- TYBALT_DATA sample data directory
Interactive job
Interactive jobs should be used for debugging, graphics, or applications that cannot be run as batch jobs.
Allocate an interactive session and run the program. Sample session:
[user@biowulf]$ sinteractive --mem=16g --gres=gpu:v100,lscratch:10 [user@cn3200 ~]$module load Tybalt [+] Loading libarchive 3.3.2 [+] Loading singularity on cn4470 [+] Loading python 3.6 ... [+] Loading cuDNN 7.0 libraries... [+] Loading CUDA Toolkit 9.0.176 ... [+] Loading gcc 7.2.0 ... [+] Loading GSL 2.4 for GCC 7.2.0 ... [+] Loading openmpi 3.0.0 for GCC 7.2.0 [+] Loading R 3.5.0_build2 [+] Loading Tybalt 0.1.3 ...Tybalt includes a script vae_pancancer.py which performs training of a variational autoencoder (VAE) on pre-processed data downloaded from the TCGA. To visualize a usege mesaage for this script, type:
[user@biowulf]$ vae_pancancer.py -h
...
usage: vae_pancancer.py [-h] [-l LEARNING_RATE] [-b BATCH_SIZE] [-e EPOCHS]
[-k KAPPA] [-d DEPTH] [-c FIRST_LAYER]
[-f OUTPUT_FILENAME] [-n NUM_COMPONENTS]
optional arguments:
-h, --help show this help message and exit
-l LEARNING_RATE, --learning_rate LEARNING_RATE
learning rate of the optimizer
-b BATCH_SIZE, --batch_size BATCH_SIZE
Number of samples to include in each learning batch
-e EPOCHS, --epochs EPOCHS
How many times to cycle through the full dataset
-k KAPPA, --kappa KAPPA
How fast to linearly ramp up KL loss
-d DEPTH, --depth DEPTH
Number of layers between input and latent layer
-c FIRST_LAYER, --first_layer FIRST_LAYER
Dimensionality of the first hidden layer
-f OUTPUT_FILENAME, --output_filename OUTPUT_FILENAME
The name of the file to store results
-n NUM_COMPONENTS, --num_components NUM_COMPONENTS
The latent space dimensionality to test
Here is an example of training command:
[user@biowulf]$ vae_pancancer.py -l 0.001 -b 100 -e 40 -k 0.05 -d 2 -c 100 -f res ... Using TensorFlow backend. ... 1071 14:12 Epoch 1/40 1072 14:12 9413/9413 [==============================] - 3s 355us/step - loss: 2955.5256 - val_loss: 2837.6293 1073 14:12 Epoch 2/40 1074 14:12 9413/9413 [==============================] - 2s 204us/step - loss: 2796.2215 - val_loss: 2782.0771 1075 14:12 Epoch 3/40 1076 14:12 9413/9413 [==============================] - 2s 204us/step - loss: 2764.0638 - val_loss: 2766.8539 ... Epoch 38/40 9413/9413 [==============================] - 2s 204us/step - loss: 2668.6827 - val_loss: 2676.4443 Epoch 39/40 9413/9413 [==============================] - 2s 206us/step - loss: 2668.2820 - val_loss: 2674.2052 Epoch 40/40 9413/9413 [==============================] - 2s 207us/step - loss: 2667.7264 - val_loss: 2674.6204Tybalt employs a variational autoencoder by default, but optionally it can also perform Analysis using a Denoising Autoencoder applied to Gene Expression (ADAGE), which can be trained on the same data:
[user@biowulf]$ adage_pancancer.py -l 0.001 -b 100 -e 40 -f res_adage -n 0.1 -s 0.001 ... Using TensorFlow backend. ... Epoch 1/40 ... 9413/9413 [==============================] - 4s 402us/step - loss: 0.0987 - val_loss: 0.0729 Epoch 2/40 9413/9413 [==============================] - 2s 210us/step - loss: 0.0608 - val_loss: 0.0478 Epoch 3/40 9413/9413 [==============================] - 2s 208us/step - loss: 0.0407 - val_loss: 0.0366 ... Epoch 39/40 9413/9413 [==============================] - 2s 226us/step - loss: 0.0349 - val_loss: 0.0349 Epoch 40/40 9413/9413 [==============================] - 2s 224us/step - loss: 0.0349 - val_loss: 0.0349End the interactive session:
[user@cn3200 ~]$ exit salloc.exe: Relinquishing job allocation 46116226 [user@biowulf ~]$