This repository is intended to replicate and show the results of the paper: https://spj.science.org/doi/10.34133/plantphenomics.0113
Clone the repository with:
git clone https://github.com/Fedjurrui/GenoDrawing.gitMove inside the folder:
cd GenoDrawingThen install the requirements:
pip install -r requirements.txtGet the weights for the autoencoder from:
- Decoder: https://huggingface.co/aranzana-lab/genodrawing/resolve/main/decoder_7_2_2023_15h.h5?download=true
- Encoder: https://huggingface.co/aranzana-lab/genodrawing/resolve/main/encoder_7_2_2023_15h.h5?download=true
Place the both files at: GenoDrawing/AE_model/64_encoders_35_epochs/model_data_7_2_2023_15h/
Open using Visual Studio Code the folder and open the autoencoder notebook file. Be mindful that it is a jupyter notebook, so if you have not worked with notebooks you might find the documentation on jupyter notebook in VSCode usefull. From there you can open the GenoDrawing file and last the GenoDrawing_stats.
It is highly encouraged to use a GPU. Inference times are greatly improved doing so eventhough it is not strictly required and the code has been prepared to run on CPU.
Advancements in genome sequencing have facilitated whole genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks, particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, a neural network model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrated proficiency in its task while using a small dataset of shape-related SNPs, and multiple experiments were conducted to evaluate the impact of SNP selection and shape relation. Results indicated that the correct relationship of SNPs with visual traits had a significant impact on the generated images, consistent with biological interpretation. While using significant SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs. The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping, particularly beneficial for small to medium breeding companies to predict economically significant heritable traits. Although GenoDrawing has limitations, it sets the groundwork for future research in image prediction from genomic markers.
