Solo: doublet identification via semi-supervised deep learning

We developed a semi-supervised deep learning framework for the identification of doublets in scRNA-seq analysis called Solo. To validate our method, we used MULTI-seq, cholesterol modified oligos (CMOs), to experimentally identify doublets in a solid tissue with diverse cell types, mouse kidney, and showed Solo recapitulated experimentally identified doublets. To better understand the ability to computationally identify doublets we ran two wells on the 10X v3 platform to generate single cell gene expression profiles and cholesterol-modified oligo count profiles for two mouse kidneys from one mouse. CMO_groups.csv: How CMO barcodes were pooled together. To ensure redundancy during MULTI-seq we used three CMOs per sample for identification. We used the combined counts to demultiplex cells and identify doublets. feature_barcodes.csv: CMO barcodes used and their names.