RT Journal Article SR Electronic T1 Normalizing and denoising protein expression data from droplet-based single cell profiling JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.02.24.963603 DO 10.1101/2020.02.24.963603 A1 Matthew P. Mulè A1 Andrew J. Martins A1 John S. Tsang YR 2020 UL http://biorxiv.org/content/early/2020/02/25/2020.02.24.963603.abstract AB Recent methods enable simultaneous measurement of protein expression with the transcriptome in single cells by combining protein labeling with DNA barcoded antibodies followed by droplet based single cell capture and sequencing (e.g. CITE-seq). While data normalization and denoising have received considerable attention for single cell RNA-seq data, such methods for protein data have been less explored. Here we showed that a major source of noise in CITE-seq data originated from unbound antibody encapsulated in droplets. We also found that the counts of isotype controls and those of the “negative” population inferred from all protein counts of each cell are significantly correlated, suggesting that their covariation likely reflects cell-to-cell differences due to technical factors such as non-specific antibody binding and droplet-to-droplet differences in capture efficiency of the DNA tags. Motivated by these observations, we developed a normalization method for CITE-seq protein expression data called Denoised and Scaled by Background (DSB). DSB corrects for 1) protein-specific background noise as reflected by empty droplets, 2) the technical cell-to-cell variation as captured by the latent noise component described above. DSB normalization improves separation between positive and negative populations for each protein, centers the negative-staining population around zero, and can improve unbiased protein expression-based clustering. DSB is available through the open source R package “DSB” via a single function call and can be readily integrated with existing single cell analysis workflows, including those in Bioconductor and Seurat.