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Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming II

ABSTRACT: We collected duplicate samples at the various time points, generated single cell suspensions and performed scRNA-Seq. We also collected samples from established iPSC lines reprogrammed from the same MEFs, maintained in either 2i or serum conditions. Overall, we profiled ~250,000 cells.

ORGANISM(S): Mus musculus

PROVIDER: GSE115943 | GEO | 2019/01/31

REPOSITORIES: GEO

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Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming I

Project description:We collected single or duplicate samples at the various time points, generated single cell suspensions and performed scRNA-Seq. We also collected samples from established iPSC lines reprogrammed from the same MEFs, maintained in either 2i or serum conditions. Overall, we profiled 68,339 cells to an average depth of 38,462 reads per cell. After discarding cells with less than 1,000 genes detected, we retained a total of 65,781 cells, with a median of 2,398 genes and 7,387 unique transcripts per cell.

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Optimal transport analysis reveals trajectories in steady-state systems.

Project description:Understanding how cells change their identity and behaviour in living systems is an important question in many fields of biology. The problem of inferring cell trajectories from single-cell measurements has been a major topic in the single-cell analysis community, with different methods developed for equilibrium and non-equilibrium systems (e.g. haematopoeisis vs. embryonic development). We show that optimal transport analysis, a technique originally designed for analysing time-courses, may also be applied to infer cellular trajectories from a single snapshot of a population in equilibrium. Therefore, optimal transport provides a unified approach to inferring trajectories that is applicable to both stationary and non-stationary systems. Our method, StationaryOT, is mathematically motivated in a natural way from the hypothesis of a Waddington's epigenetic landscape. We implement StationaryOT as a software package and demonstrate its efficacy in applications to simulated data as well as single-cell data from Arabidopsis thaliana root development.

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Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming

Project description:This SuperSeries is composed of the SubSeries listed below.

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Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming.

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