CITE-seq optimization

Code and results from TotalSeqC antibody titration and pipeline benchmarking for CITE-seq experiments.

Data and Seurat V3 objects from the study is deposited at FigShare under this DOI: 10.6084/m9.figshare.c.5018987

This repository contains all the code used in the processing of the aligned data and data analysis (used for generating all figures) included in the manuscript at BioRxiv.org:

Manuscript

Improving oligo-conjugated antibody signal in multimodal single-cell analysis. Terkild Brink Buus, Alberto Herrera, Ellie Ivanova, Eleni Mimitou, Anthony Cheng, Thales Papagiannakopoulos, Peter Smibert, Niels Odum, Sergei B Koralov. bioRxiv 2020.06.15.153080; doi: https://doi.org/10.1101/2020.06.15.153080

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Pre-processing:

• Loading data, Demultiplexing, Preprocessing and down-sampling - Supplementary Figure S1
• Load unfiltered data and determine cell-containing vs. empty droplets - Supplementary Figure S6

Data analysis:

• Antibody concentration titration - Figure 1, 2 and Supplementary Figure S2
• Reducing staining volume - Figure 3 and Supplementary Figure S3
• Reducing cell number at staining - Figure 4 and Supplementary Figure S4
• Reducing cell number mitigates reduced staining volume - Supplementary Figure S5
• ADT signal in cell-containing vs. empty droplets - Figure 5 and Supplementary Figure S8
• 10X Datasets: UMI per marker plots - Supplementary Figure S7
• Comparison of ADT counting methods - Figure 6 and Supplementary Figure S9

We also included the Snakefiles used with Snakemake to generate the alignment and counting data from our dataset and for the 10X datasets.