DSBNormalizeProtein     DSBNormalizeProtein R function: Normalize
                        single cell antibody derived tag (ADT) protein
                        data. This function implements both step I
                        (ambient protein background correction) and
                        step II. (defining and removing cell to cell
                        technical variation) of the dsb normalization
                        method. See
                        <https://www.biorxiv.org/content/10.1101/2020.02.24.963603v3>
                        for details of the algorithm.
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.biorxiv.org/content/10.1101/2020.02.24.963603v3>
                        for details of the algorithm.
cells_citeseq_mtx       small example CITE-seq protein dataset for 87
                        surface protein in 2872 cells
empty_drop_citeseq_mtx
                        small example CITE-seq protein dataset for 87
                        surface protein in 8005 empty droplets
