CITE-seq ======== ``muon`` features a module to work with protein measurements: :: from muon import prot as pt CITE-seq is a method for cellular indexing of transcriptomes and epitopes by sequencing. It's single-cell data comprising transcriptome-wide measurements for each cell (gene expression) as well as surface protein level information, typically for a few dozens of proteins. The method is described in `Stoeckius et al., 2017 `_ and also `on the cite-seq.com website `_. .. contents:: :local: :depth: 3 .. toctree:: :maxdepth: 10 * Normalisation ------------- dsb +++ Various methods can be used to normalise protein counts in CITE-seq data. ``muon`` brings one of the methods developed specifically for CITE-seq — *denoised and scaled by background* — to Python CITE-seq workflows. This method uses background droplets defined by low RNA content in order to estimate background protein signal and remove it from the data. The method is described in `Korliarov, Sparks et al., 2020 `_ and its original implementation `is available on GitHub `_. :: pt.pp.dsb(adata_prot, adata_prot_raw, empty_counts_range=...) # will use cell calling from the filtered matrix # or adata_prot = pt.pp.dsb(adata_prot_raw, cell_counts_range=..., empty_counts_range=...) # will use provided cell_counts_range for cell calling CLR +++ The centered log ratio (CLR) transformation is one of the strategies to normalise protein counts (see e.g. `Stoeckius et al., 2017 `_): :: pt.pp.clr(mdata['prot'])