## Overview

### Description

The latest breakthrough in single-cell omics is on multi-modal profiling of different biomolecule species in individual cells. Among the fast evolving biotechnologies developed for multi-modal profiling, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is attracting attention, especially in the field of immunology, given its ability to simultaneously quantify global gene expression and cellular proteins using RNA-sequencing and antibody-derived tags (ADTs) on single cells. While the additional protein marker information brought about by ADTs is extremely valuable, new biological insights can only be gained by developing analytic methods that fully take advantage of the complementarity between mRNA and ADT expression measured in CITE-seq.

To address this, we developed a streamlined pipeline–CiteFuse–that consists of a suite of tools for the integration and the downstream analysis of CITE-seq data. In this workshop, we will provide a hands-on experience to the CiteFuse package and cover all the steps including doublet detection, modality integration, cell type clustering, differential RNA and ADT expression analysis, ADT evaluation, and ligand–receptor interaction analysis on a publicly available CITE-seq dataset. We also demonstrate the applicability of CiteFuse package on other multi-modal data types by applying our pipeline on the recently developed ASAP-seq data.

This vignette provides a more complete description of the various tools in CiteFuse and will serve as the basis of our workshop.

### Pre-requisites

Software:

• Basic knowledge of R syntax
• Familiarity with single-cell RNA-sequencing
• Familiarity with the SingleCellExperiment class

### Participation

Participants will learn how to…

### Time outline

An example for a 55-minute workshop:

Activity Time
Introduction 15m
Data processing and integration 15m
Downstream analysis 15m
Application of CiteFuse on ASAP-seq 5m
Wrap-up and Conclusions 5m

### Learning goals

Some examples:

• describe how to…
• identify methods for…
• understand the difference between…

### Learning objectives

• analyze xyz data to produce…
• create xyz plots
• evaluate xyz data for artifacts