Joint analysis of heterogeneous single-cell RNA-seq dataset collections

TY - JOUR

T1 - Joint analysis of heterogeneous single-cell RNA-seq dataset collections

AU - Barkas, Nikolas

AU - Petukhov, Viktor

AU - Nikolaeva, Daria

AU - Lozinsky, Yaroslav

AU - Demharter, Samuel

AU - Khodosevich, Konstantin

AU - Kharchenko, Peter V

PY - 2019/8/1

Y1 - 2019/8/1

N2 - Single-cell RNA sequencing is often applied in study designs that include multiple individuals, conditions or tissues. To identify recurrent cell subpopulations in such heterogeneous collections, we developed Conos, an approach that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells. The graph enables identification of recurrent cell clusters and propagation of information between datasets in multi-sample or atlas-scale collections.

AB - Single-cell RNA sequencing is often applied in study designs that include multiple individuals, conditions or tissues. To identify recurrent cell subpopulations in such heterogeneous collections, we developed Conos, an approach that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells. The graph enables identification of recurrent cell clusters and propagation of information between datasets in multi-sample or atlas-scale collections.

U2 - 10.1038/s41592-019-0466-z

DO - 10.1038/s41592-019-0466-z

M3 - Journal article

C2 - 31308548

SN - 1548-7091

VL - 16

SP - 695

EP - 698

JO - Nature Methods

JF - Nature Methods

IS - 8

ER -