DeepPT: A deep learning model for predicting transcriptomics from histopathology images Code associated with “A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics”, Nature Cancer 2024, by Danh-Tai Hoang et al. 1. Introduction DeepPT (Deep Pathology for Transcriptomics) is a deep learning framework that predicts gene expression from histopathology images. DeepPT consists of 4 main components: (i) Image pre-processing: Split each whole slide image into tiles/patches and select only tiles that contain tissue and exclude them from background. Color normalization was included to minimize staining variation (heterogeneity and batch effects). (ii) Feature extraction: Use the pre-trained ResNet50 CNN model to extract image features from the tiles. Through this process, each image tile is represented by a vector of 2,048 derived features (pre-trained ResNet features). (iii) Feature compression: Compress the 2,048 pre-trained ResNet features to 512 features using an autoencoder network. This helps to exclude noise, to avoid overfitting, and finally to reduce the computational demands. (iv) Prediction: This component takes the AE features as input and gene expressions as output. 2. Installations: To install DeepPT, please install the following requirements: python 3.9.7 numpy 1.20.3 pandas 1.3.4 matplotlib 3.4.3 sklearn 1.1.1 openslide 1.1.2 opencv 4.5.4 torch 1.12.1 3. DeepPT computational pipeline: - Step 1: Run “11slide_processing/1main_processing.py” to perform image pre-processing and feature extraction. This code will run on each slide simultaneously. - Step 2: Run “11slide_processing/collect_mask.py” to collect mask files into a single file “mask.pdf” that will be used to evaluate slide quality. - Step 3: Run “11slide_processing/collect_features.py” to create a file that contains features of image tiles. - Step 4: Run “12AE/1main_AE.py” to compress the 2,048 pre-trained features to 512 AE features. - Step 5: Run “13DeepPT_train/1main_train.py” to train and predict gene expression from the AE features. 4. License and Terms of use This model and its associated code have been filed for a US patent (application No. 63/349,829, United States, 2022) and are permitted solely for non-commercial, academic research purposes. Commercial use, sale, or any form of monetization of the DeepPT model is strictly prohibited without prior approval. Commercial entities interested in utilizing the model should contact the corresponding authors for authorization.