dlim 0.0.3

Model genotype to fitness map

• Overview

D-LIM (Direct-Latent Interpretable Model) is a neural network that enhances genotype-fitness mapping by combining interpretability with predictive accuracy. It assumes independent phenotypic influences of genes on fitness, leading to advanced accuracy and insights into phenotype analysis and epistasis. The model includes an extrapolation method for better understanding of genetic interactions and integrates multiple data sources to improve performance in low-data biological research.

• Requirements

The implementation has been tested on a Linux system for: Python 3.10.9; Pytorch 2.0.1; numpy 1.23.5; pandas 2.0.2.

• Installation

#+begin_src bash

#+end_src

• Usage

The code snippet bellow shows how to use fit D-LIM for fitness prediction.

#+begin_src python :results output from dlim import DLIM from dlim.utils import Data_model, train from numpy import mean, linspace from numpy.random import choice from sklearn.metrics import r2_score from scipy.stats import pearsonr import matplotlib.pyplot as plt

# Read the data, there are 2 genes (or variables) here data = Data_model(“./data/data_env_1.csv”, 2) # Here, we used 2 latent phenotype, the data has 37 possible mutations. # For D-LIM, we use here 1 hidden layer of 32 neurons. model = DLIM(2, nb_state=37, hid=32, nb_layer=1)

# We manually split the data in training and validation. train_id = choice(range(data.data.shape[0]), int(data.data.shape[0]*0.2)) train_data = data[train_id, :] val_data = data[[i for i in range(data.data.shape[0]) if i not in train_data], :]

# We train the model with a learning rate of 1e-2 for 300 steps with batch size # 16 and regularization of 1e-2 losses = train(model, train_data, lr=1e-2, nb_epoch=300, bsize=16, val_data=val_data, wei_dec=1e-2)

# Now, we compute the validation prediction fit, var, _ = model(val_data[:, :-1].int(), detach=True) score = pearsonr(fit.flatten(), val_data[:, [-1]].flatten())[0] print(score)

# Here, we plot the trained landscape fig, ax = plt.subplots(figsize=(2, 2)) model.plot(ax, data) plt.show() #+end_src

#+RESULTS: : None

• Reproduction of the manuscript

Figures and analyses of the manuscript can be found in ~reproducibility.org~.

• License

MIT