An explosion in single-cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell-state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single-cell techniques have extended to multi-omics approaches and now enable the simultaneous measurement of data modalities and spatial cellular context. Data are now available for millions of cells, for whole-genome measurements and for multiple modalities. Although analyses of such multimodal datasets have the potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single-cell dataset. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell-type classification, regulatory network modelling and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multi-omics data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.
With the development of single-cell multi-omics techniques, tools and models for data integration are critically important.
Integration problems in single-cell biology can be divided into those associated with the integration of matched and unmatched data.
Strategies for integrating matched data include joint latent space inference, consensus of individual inferences and biological causal modelling.
Strategies for integrating unmatched data include annotated group matching, matching with common features and aligning spaces.
Visualization methods for integrated multimodal single-cell data are still underdeveloped.
Future challenges include accounting for specific noise related to each modality, overcoming the need for computing efficiency and developing biologically interpretable integration strategies.
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This work was supported in part by UC2DK126024 grant to J.K., B.D.H. and A.P.M. as well as by a Health Research Formula Fund of the Commonwealth of Pennsylvania, which did not have a direct role in the work.
A.P.M. is a scientific adviser to Novartis, eGENESIS, TRESTLE Therapeutics and IVIVA Medical. The other authors declare no competing interests.
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GenitoUrinary Development Molecular Anatomy Project: https://www.gudmap.org/
HuBMAP portal: https://portal.hubmapconsortium.org/
ReBuilding a Kidney: https://www.rebuildingakidney.org/
- Assay for transposase-accessible chromatin using sequencing
(ATAC-seq). A technique that profiles the accessibility of DNA elements based on the principle that the Tn5 transposase can insert a transposon only at accessible parts of the chromosome. The insertion location is identified through DNA sequencing.
- Cis-regulatory elements
DNA elements proximal to a gene that are required for controlling gene expression. Such elements usually include promoters and enhancers, and often contain transcription factor-binding sites.
- Molecule recovery efficiency
Single-cell assays capture molecules, such as mRNAs or transposon-interrupted DNA fragments, and amplify them for readout. Different protocols recover a given pool of molecules with different efficiencies; for example, a single podocyte might have 300,000 mRNA molecules and an RNA sequencing protocol with a 10% recovery efficiency would recover ~30,000 of these.
- Joint snRNA-seq and snATAC-seq
Single-cell RNA sequencing (scRNA-seq) attempts to recover RNA from the whole cell, whereas single-nucleus RNA sequencing (snRNA-seq) only isolates the nuclear fraction of the RNA; the two transcriptomes are related but different. Multi-omics methods involving assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq typically isolate the nucleus first, resulting in snRNA-seq and snATAC-seq.
- Feature space
In machine learning, measured variables are often called features and the set of features comprise a feature space.
- Sequential fluorescence in situ hybridization
(seqFISH). A technique that measures mRNA quantity through sequential fluorescent probes that have combinatorially encoded information for each targeted mRNA. For example, a sequence signal, probe A then B, might encode gene X, whereas the sequence probe A then C might encode gene Y.
- Read depth
A quantity that measures the number of times that sequencing reads cover a given genomic region. The region of interest may be a base pair or an entire transcribed region.
- Canonical correlation analysis
A multivariate statistical technique that computes the correlation between two sets of variables, say X and Y. Canonical correlation analysis finds the linear combination of X and the linear combination of Y that maximizes correlation.
- Non-negative matrix factorization
A group of algorithms that decompose one matrix into a product of two (or more) matrices, such that the elements in each matrix are non-negative. Typically, each matrix has a model interpretation; for example, a data matrix factorizes the matrix into one representing latent space features and another representing latent space features to cells.
A metagene is some (mathematical) function of a group of genes (for example, linear combination), often relating some shared properties. For example, methods like non-negative matrix factorization compute matrices as the product between a gene-by-metagene matrix and a metagene-by-cell matrix.
- Dimension reduction
A data transformation method that reduces the number of dimensions in the original feature space to a lower-dimensional space (usually much lower than the original one) while certain properties (for example, the distance measures between observations) of the original data are preserved.
In contrast to real time, pseudotime represents computationally inferred temporal stages of a collection of cells.
- Principal component analysis
A common dimension reduction method that aims to project the original data to a fixed smaller dimension while minimizing the squared error during data reduction. This approach can be viewed as maximizing the variance in the projected data.
In mathematics, embedding is a map from one set X to another set Y, where some characteristic of X is preserved. In single-cell studies, the term embedding has been used for methods that ‘place’ cells in a new feature space, possibly of a lower dimension, such that notions of cell-to-cell distances are approximately preserved.
In single-cell biology, dropouts are usually the transcripts that were present in the cell but were not captured during sequencing.
- Ambient RNA
In droplet-based single-cell RNA sequencing approaches, the measured mRNA molecules can be contaminated by mRNAs from other cells present in the suspension, for example, owing to cell rupture. These contaminating mRNAs are termed ambient RNA.
During high-throughput single-cell (or single-nucleus) isolation in droplets or similar vessels, two or more cells might be captured together creating a mixture of molecules. Computational methods have been developed to detect and remove such unwanted observations from the dataset.
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Miao, Z., Humphreys, B.D., McMahon, A.P. et al. Multi-omics integration in the age of million single-cell data. Nat Rev Nephrol 17, 710–724 (2021). https://doi.org/10.1038/s41581-021-00463-x
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