Joint analysis of heterogeneous single-cell RNA-seq dataset collections
Nature methods, 2019•nature.com
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.
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.
Abstract
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.
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