PMID- 30696980 OWN - NLM STAT- MEDLINE VI - 20 IP - 5 TI - Integrative single-cell analysis. PG - 257-272 LA - eng PT - Journal Article PT - Research Support, N.I.H., Extramural PT - Research Support, Non-U.S. Gov't PT - Review PL - England TA - Nat Rev Genet JT - Nature reviews. Genetics JID - 100962779 IS - 1471-0064 (Electronic) LID - 10.1038/s41576-019-0093-7 [doi] FAU - Stuart, Tim AU - Stuart T AUID- ORCID: http://orcid.org/0000-0002-3044-0897 AD - New York Genome Center, New York, NY, USA. FAU - Satija, Rahul AU - Satija R AUID- ORCID: http://orcid.org/0000-0001-9448-8833 AD - New York Genome Center, New York, NY, USA. rsatija@nygenome.org. AD - Center for Genomics and Systems Biology, New York University, New York, NY, USA. rsatija@nygenome.org. IS - 1471-0056 (Linking) RN - 0 (Proteins) RN - 63231-63-0 (RNA) MH - Computational Biology/*methods MH - Data Mining/*statistics & numerical data MH - Datasets as Topic MH - Epigenesis, Genetic MH - High-Throughput Nucleotide Sequencing MH - Humans MH - Proteins/genetics/metabolism MH - RNA/chemistry/*genetics/metabolism MH - Single-Cell Analysis/methods/*statistics & numerical data DCOM- 20190724 LR - 20200107 DP - 201905 AB - 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.