Comprehensive integration of single-cell data

Single-cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we developed a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single-cell measurements not only across scRNA-seq technologies, but different modalities as well. As one demonstration of the method, we anchor single-cell protein measurements with a human bone marrow atlas to annotate and characterize lymphocyte populations. The multimodal data, generated using CITE-seq, is provided here alongside a corresponding bulk validation experiment. Overall design: Human PBMCs were profiled using CITE-seq and bulk RNA-seq.