Install functions to simulate gene expression compendia
Project description
ponyo
Alexandra Lee and Casey Greene 2020
University of Pennsylvania
This repository is named after the the character Ponyo, from Hayao Miyazaki's animated film Ponyo, as she uses her magic to simulate a human appearance after getting a sample of human blood. The method simulates a compendia of new gene expression data based on existing gene expression data to learn a representation of gene expression patterns.
Installation
This package can be installed using pip:
pip install ponyo
How to use
Example notebooks using ponyo on test data can be found in human_tests
Additionally, this method has been used in simulate-expression-compendia and generic-expression-patterns repositories.
Setting random seeds
To keep the VAE training deterministic, you will need to set multiple random seeds:
- numpy random
- python random
- tensorflow random
For an example of this, see human_tests
Configuration file
The tables lists the core parameters required to generate simulated data using modules from ponyo. Those marked with * indicate those parameters that will vary depending on the type of approach .
| Name | Description |
|---|---|
| local_dir | str: Parent directory on local machine to store intermediate results |
| dataset_name | str: Name for analysis directory containing notebooks using ponyo |
| raw_data_filename | str: File storing raw gene expression data |
| normalized_data_filename | str: File storing normalized gene expression data |
| metadata_filename* | str: File containing metadata associated with data |
| experiment_ids_filename* | str: File containing list of experiment ids that have gene expression data available |
| scaler_transform_filename | str: File to store mapping from normalized to raw gene expression range |
| simulation_type | str: Name of simulation approach directory to store results locally |
| NN_architecture | str: Name of neural network architecture to use. Format 'NN__' |
| learning_rate | float: Step size used for gradient descent. In other words, it's how quickly the methods is learning |
| batch_size | str: Training is performed in batches. So this determines the number of samples to consider at a given time |
| epochs | int: Number of times to train over the entire input dataset |
| kappa | float: How fast to linearly ramp up KL loss |
| intermediate_dim | int: Size of the hidden layer |
| latent_dim | int: Size of the bottleneck layer |
| epsilon_std | float: Standard deviation of Normal distribution to sample latent space |
| validation_frac | float: Fraction of input samples to use to validate for VAE training |
| num_simulated_samples* | int: If using random sampling approach, simulate a compendia with these many samples |
| num_simulated_experiments* | int: If using latent-transformation approach, simulate a compendia with these many experiments |
| num_simulated* | int: If using template-based approach, simulate these many experiments |
| metadata_delimiter* | str: Delimiter to parse metadata file |
| metadata_experiment_colname* | str: Column header that contains experiment id that maps expression data and metadata |
| metadata_sample_colname* | str: Column header that contains sample id that maps expression data and metadata |
| project_id* | int: If using template-based approach, experiment id to use as template experiment |
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