ponyo 0.1

Install functions to simulate gene expression compendia

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 repository.

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
scaler_transform_file	str: File to store mapping from normalized to raw gene expression range
dataset_name	str: Name for analysis directory containing notebooks using ponyo
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
project_id*	int: If using template-based approach, experiment id to use as template experiment
metadata_colname	str: Column header that contains sample id that maps expression data and metadata