Efficient and precise single-cell reference atlas mapping with Symphony

Abstract

Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony (https://github.com/immunogenomics/symphony), an algorithm for building large-scale, integrated reference atlases in a convenient, portable format that enables efficient query mapping within seconds. Symphony localizes query cells within a stable low-dimensional reference embedding, facilitating reproducible downstream transfer of reference-defined annotations to the query. We demonstrate the power of Symphony in multiple real-world datasets, including (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells.

Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases

“…Identification of shared cell states across tissues with scRNA-seq has recently become possible with advances in statistical methods for integrative clustering [22][23][24] and reference mapping. [25][26][27] Integrative clustering identifies similar cell states across a range of scRNA-seq datasets even when the datasets come from different donors, species, or tissues. For example, using integrative clustering, Zhang et al 28 identified shared macrophage activation states across five tissues, and Butler et al 24 identified shared pancreatic islet cells between mouse and human datasets.…”

“…We used a novel type of analysis from single-cell analysis, Symphony reference mapping, 27 to compare human dermal fibroblasts and mouse lung, synovial, and lung fibroblasts with our annotated cross-tissue atlas. Reference mapping let us avoid intensive and error-prone manual interpretation steps in de novo analysis of the external datasets.…”

An Atlas of Cells in the Human Tonsil

“…In this setting, the inherent discriminative power of single-cell RNA-sequencing (scRNA-seq) has catalyzed the creation of cellular taxonomies of lymphoid organs, such as the thymus (Park et al, 2020) and the bone marrow (Baccin et al, 2020). In the context of the Human Cell Atlas (HCA) (Regev et al, 2017), these taxonomies identified previously uncharacterized cell types and provided a reference to annotate cell types and states by training classifiers (Kang et al, 2021;Lotfollahi et al, 2022) and through curated cell ontologies (Osumi-Sutherland et al, 2021). While the transcriptome allows precise cellular phenotyping, recent atlases also incorporate additional layers, such as the epigenome or spatial profiles for mechanistic and structural insights, respectively.…”

A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history

“…Recently an adaptation to Harmony, called Symphony, was released which is capable of harmonizing data based on a comprehensive atlas, thus only requiring the sharing of a representative sample of data to construct said atlas, after which downstream analyses could be contained to individual sites. 29 Second, we utilize elastic net regularization, while we aim to eliminate any assumption of linearity throughout the pipeline, as we are convinced that the disease history associations of interest go beyond linear 1-on-1 connections. We nonetheless chose to rely on EN for the identification of the top 10 most discerning PheCodes of a cluster, as it is but one aspect of the downstream analysis of identified clusters.…”