ThresholdR: An Automated Method to DenoiseCITE-Seq Data for Immune Cells

Abstract

Cellular Indexing of Transcriptomes and Epitopes (CITE-Seq) is a potent addition to single-cell RNA sequencing (scRNA-Seq). This method enriches transcriptomic insights by incorporating information about the cell surface phenotype through the application of oligonucleotide-tagged monoclonal antibodies. Similar to observations in flow cytometry, the CITE-Seq signal (ADT) contains technical noise originating from ambient antibodies within the reaction compartment, non-specific binding and imperfect titration. Here, we present ThresholdR, an R-based, automated tool, to reliably and systematically find the threshold that separates the signal from the noise for each antibody. We assess the performance of ThresholdR across different datasets and platforms and benchmark it against two alternative methods, DSB (denoised and scaled by background) and CellBendR (CBR). We show that ThresholdR remedies the high false negative rates of DSB and CBR. We show that using ThresholdR after CellBender significantly improves the performance of CellBender.

Keywords: CITE-seq, scRNA-seq, PBMC, Gaussian Mixture Models, Denoising