Correcting Chimeric Crosstalk in Single Cell RNA-seq Experiments

As part of the process of preparing scRNA-seq libraries, a diverse template is typically amplified by PCR. During amplification, spurious chimeric molecules can be formed between molecules originating in different cells. While several computational and experimental strategies have been suggested to mitigate the impact of chimeric molecules, they have not been addressed in the context of scRNA-seq experiments. We demonstrate that chimeras become increasingly problematic as samples are sequenced deeply and propose two computational solutions. The first is unsupervised and relies only on cell barcode and UMI information. The second is a supervised approach built on labeled data and a set of molecule specific features. The classifier can accurately identify most of the contaminating molecules in a deeply sequenced species mixing scRNA-seq dataset. Code is publicly available at https://github.com/asncd/schimera.