The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce ‘weighted-nearest neighbor analysis”, an unsupervised framework to learn the information content of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 surface proteins, constructing a multimodal reference atlas of the circulating immune system, which you can explore here.

For more information, you can explore our preprint, Seurat v4 software release,
and Azimuth - a web app for mapping your datasets to this reference.