Multimodal hierarchical classification allows for efficient annotation of CITE-seq data
Single-cell RNA sequencing (scRNA-seq) is an invaluable tool for profiling cells  in complex tissues and dissecting activation states that lack well-defined surface   protein expression. For immune cells, the transcriptomic profile captured by scRNA-  seq cannot always identify cell states and subsets defined by conventional flow   cytometry. Emerging technologies have enabled multimodal sequencing of single   cells, such as paired sequencing of the transcriptome and surface proteome by   CITE-seq, but integrating these high dimensional modalities for accurate cell type   annotation remains a challenge in the field. Here, we describe a machine learning   tool called MultiModal Classifier Hierarchy (MMoCHi) for the cell-type annotation of   CITE-seq data. Our classifier involves several steps: 1) we use landmark registration  to remove batch-related staining artifacts in CITE-Seq protein expression, 2) the   user defines a hierarchy of classifications based on cell type similarity and ontology   and provides markers (protein or gene expression) for the identification of ground   truth populations within the dataset by threshold gating, 3) progressing through this  user-defined hierarchy, we train a random forest classifier using all available   modalities (surface proteome and transcriptome data), and 4) we use these forests   to predict cell types across the entire dataset. Applying MMoCHi to CITE-seq data of   immune cells isolated from eight distinct tissue sites of two human organ donors   yields high-purity cell type annotations encompassing the broad array of immune   cell states in the dataset. This includes T and B cell memory subsets, macrophages   and monocytes, and natural killer cells, as well as rare populations of plasmacytoid   dendritic cells, innate T cells, and innate lymphoid cell subsets. We validate the use   of feature importances extracted from the classifier hierarchy to select robust genes  for improved identification of T cell memory subsets by scRNA-seq. Together,   MMoCHi provides a comprehensive system of tools for the batch-correction and cell-  type annotation of CITE-seq data. Moreover, this tool provides flexibility in   classification hierarchy design allowing for cell type annotations to reflect a   researcher's specific experimental design. This flexibility also renders MMoCHi   readily extendable beyond immune cell annotation, and potentially adaptable to   other sequencing modalities. Overall design: We performed CITE-seq on immune cell populations from human blood and different human organ donor tissues.