# Normalize & Denoise Droplet Single Cell Protein Data (CITE-Seq)

## Documentation for package ‘dsb’ version 2.0.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

cells_citeseq_mtx | small example CITE-seq protein dataset for 87 surface protein in 2872 cells
DSBNormalizeProtein | DSBNormalizeProtein R function: Normalize single cell antibody derived tag (ADT) protein data. This function corrects for both protein specific and cell to cell technical noise in antibody derived tag (ADT) data. For datasets without access to empty drops use dsb::ModelNegativeADTnorm. See <https://www.nature.com/articles/s41467-022-29356-8> for details of the algorithm.
empty_drop_citeseq_mtx | small example CITE-seq protein dataset for 87 surface protein in 8005 empty droplets
ModelNegativeADTnorm | ModelNegativeADTnorm R function: Normalize single cell antibody derived tag (ADT) protein data. This function defines the background level for each protein by fitting a 2 component Gaussian mixture after log transformation. Empty Droplet ADT counts are not supplied. The fitted background mean of each protein across all cells is subtracted from the log transformed counts. Note this is distinct from and unrelated to the 2 component mixture used in the second step of 'DSBNormalizeProtein' which is fitted to all proteins of each cell. After this background correction step, 'ModelNegativeADTnorm' then models and removes technical cell to cell variations using the same step II procedure as in the DSBNormalizeProtein function using identical function arguments. This is a experimental function that performs well in testing and is motivated by our observation in Supplementary Fig 1 in the dsb paper showing that the fitted background mean was concordant with the mean of ambient ADTs in both empty droplets and unstained control cells. We recommend using 'ModelNegativeADTnorm' if empty droplets are not available. See <https://www.nature.com/articles/s41467-022-29356-8> for details of the algorithm.