Learning from the past and predicting the future

Youngs, Robin; Fisher, Edward

doi:10.1017/s0022215115001383

Cited by 1 publication

(2 citation statements)

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“…We expect our strategy to be broadly applicable to integrate and transfer a broad spectrum of single cell data and phenotypes across experiments. These include additional epigenomic [10][11][12][13][14] , chromosome conformation 61,62 , and RNA modification 63 measurements that are increasingly being profiled at the single cell level, and even computationally derived phenotypes such as RNA velocity 64 . We believe that the integration of sequencing and imaging datasets represents a particularly promising application in the near future.…”

Section: Discussionmentioning

confidence: 99%

“…Recent advances in molecular biology, microfluidics, and computation have transformed the growing field of single cell sequencing beyond routine transcriptomic profiling with single cell RNA-seq (scRNA-seq) 1,2 . Indeed, new approaches now encompass diverse characterization of a single cell's immunophenotype 3,4 , genome sequence 5,6 , lineage origins [7][8][9] , DNA methylation landscape 10,11 , chromatin accessibility [12][13][14] , and even spatial positioning [15][16][17] . However, each technology has unique strengths and weaknesses, and measures only particular aspects of of cellular identity, motivating the need to leverage information in one dataset to improve the interpretation of another.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive integration of single cell data

Stuart

Butler

Hoffman

et al. 2018

Preprint

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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 develop 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. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets.Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat effective, they can also struggle in cases where only a subset of cell types are shared across datasets, or significant technical variation masks shared biological signal. Additionally, these methods focus on scRNA-seq and are not designed to integrate information across different modalities, nor do they enable the transfer of information from one dataset to another.Here, we present a unified strategy for reference assembly and transfer learning for transcriptomic, epigenomic, proteomic, and spatially-resolved single cell data. Through the identification of cell pairwise correspondences between single cells across datasets, termed "anchors", we can transform datasets into a shared space, even in the presence of extensive technical and/or biological differences. This enables the construction of harmonized atlases at the tissue or organismal scale. These anchors also enable effective transfer of discrete or continuous data from a reference onto a query dataset. This allows for the transfer of cell labels learned from scRNA-seq onto scATAC-seq data to explore differences in the regulatory landscape between distinct interneuron subsets, and the transfer of protein measurements 3 onto massive public resources to characterize lymphoid populations in human bone marrow. Finally, the anchoring of STARmap and scRNA-seq datasets enables the transcriptome-wide imputation of spatial gene expression pattern...

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