An interpretable and adaptive autoencoder for efficient tissue deconvolution (Q6723516)

An interpretable and adaptive autoencoder for efficient tissue deconvolution. This paper is available in bioRxiv (https://arxiv.org/pdf/2311.11991) and is currently under review. The github repository is: (https://github.com/ML4BM-Lab/Sweetwater/tree/main) Here we provide the 4 datasets used along the Sweetwater paper. In order to reproduce the results and run Sweetwater with every dataset: Call load_X.py, being X the dataset/subdataset used. e.g. for the PBMC GS, load_pbmc_gs_data.py This will return 4 elements:scRNA-seq, bulkRNA-seq, common_genes, bulkrna_props scRNA-seq: scRNA-seq reference expression matrix. bulkRNA-seq: bulkRNA-seq matrix to be deconvolved. common_genes: genes that both matrix have in common, hence defining the input size of the model. bulkrna_props: proportions of the bulkrna-seq matrix to be deconvolved. run python3 src/main.py with the path to both the scRNA-seq and bulkRNA-seq path. (see github readme) Get the deconvolved proportions. Afterwards, you can evaluate the performance using bulkrna_props.