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Integrative single-cell analysis


The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement.

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This work was supported by the US National Institutes of Health through a New Innovator Award (1DP2HG009623-01) and an R01 (5R01MH071679-12) to R.S.

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Both authors contributed to all aspects of the manuscript.

Competing interests

The authors declare no competing interests.

Correspondence to Rahul Satija.


Single-cell RNA sequencing

(scRNA-seq). Sequencing of cDNAs derived from RNA molecules (usually polyadenylated mRNAs) from a single cell. It is typically performed for many hundreds to thousands of cells in a single experiment.


Data of multiple types, for example, of RNA and protein.

Index sorting

Fluorescence-activated sorting of cells into known plate locations.

In vitro transcription

Transcription of a DNA sequence in vitro using the T7 RNA polymerase.

CITE-seq and REAP-seq

Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and RNA expression and protein sequencing assay (REAP-seq) are methods that are capable of detecting cell surface protein abundance and gene expression within the same single cell. They achieve this through the use of barcoded antibodies captured alongside mRNA transcripts in single-cell RNA sequencing (scRNA-seq) experiments.


A protein–RNA complex that allows targeted mutation or binding of DNA sequences as determined by a guide RNA sequence.

Pooled genetic screens

Screening experiments in which each individual cell may receive a different perturbation at random without prior separation of groups of cells and perturbation treatments.

Lineage tracing

The identification of lineage relationships between groups of cells through shared DNA mutations.

Single-molecule fluorescence in situ hybridization

(smFISH). A fluorescence in situ hybridization method capable of detecting the presence of a single molecule (usually RNA) through the recruitment of many fluorophores to the same area. It enables a quantitative readout of the number of molecules present in a cell.

Expression quantitative trait loci

(eQTLs). Genomic loci that explain variation in the RNA expression levels of genes.

Intron retention

The presence of intronic RNA bases in an RNA transcript. These bases are usually removed by RNA splicing shortly after or during transcription.


The ordering of cells along a one-dimensional axis describing a continuous differentiation process.

Joint clustering

Grouping cells on the basis of measurements from multiple data modalities.

Canonical correlation analysis

(CCA). A statistical method for investigating relationships between two data sets. CCA aims to identify shared sources of variation in a pair of data sets.

Dynamic time warping

A method for locally stretching or compressing two one-dimensional vectors to correct for lag in one vector relative to another.

Mutual nearest neighbours

(MNNs). Cells that are mutually nearest to one another in normalized gene expression space.

Cell-type classifications

Biologically meaningful labels given to groups of cells on the basis of common molecular profiles and prior knowledge of the cell types.

Gradient boosting

A statistical method that produces a prediction model for classification or regression on the basis of an ensemble of weaker prediction models.

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Further reading

Fig. 1: Multimodal and integrative methods for single-cell analyses.
Fig. 2: Experimental methods for performing single-cell multimodal measurements.
Fig. 3: Computational methods for the analysis of multimodal single-cell data.
Fig. 4: Computational approaches for integrating multiple single-cell data sets.
Fig. 5: Clustering and classification of cells.
Fig. 6: Integration of spatial single-cell data.