RNA-Seq

Phenotype

Cell

Transcriptome

Exome sequencing

Pedagogy

Pooling

Biology

Gene

Operating system

Data mining

Gene expression

Data type

Artificial intelligence

Psychology

Single cell sequencing

Profiling (computer programming)

Programming language

Data science

Single-cell analysis

Computer science

Computational biology

Epigenetics

Genetics

Transformative learning

Data integration

Nature Reviews Genetics

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. The functional interpretation of single-cell RNA sequencing (scRNA-seq) data can be enhanced by integrating additional data types beyond RNA-based gene expression. In this Review, Stuart and Satija discuss diverse approaches for integrative single-cell analysis, including experimental methods for profiling multiple omics types from the same cells, analytical approaches for extracting additional layers of information directly from scRNA-seq data and computational integration of omics data collected across different cell samples.

Integrative single-cell analysis