Inference of Single-Cell Phylogenies from Lineage Tracing Data

Jones, Matthew G.; Khodaverdian, Alex; Quinn, Jeffrey J.; Chan, M.K.; Hussmann, Jeffrey A.; Wang, Robert; Xu, Chenling; Weissman, Jonathan S.; Yosef, Nir

doi:10.1101/800078

Cited by 2 publications

(2 citation statements)

References 59 publications

(283 reference statements)

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“…LINNAEUS 102 and Chan et al 85 have therefore developed custom tree-building algorithms to minimize the influence of drop-outs and have also incorporated empirical likelihood estimates for each barcode in order to minimize the influence of barcode homoplasy on the final inferred tree topology. Inference of lineage relationships from DNA-barcoding data is an active area of research, with several additional groups now favouring maximum-likelihood approaches and ground-truth benchmarking of algorithm performance against empirical [120][121][122] or simulated 123 datasets.…”

Section: Computational Tools For State-lineage Mappingmentioning

confidence: 99%

Lineage tracing meets single-cell omics: opportunities and challenges

2020

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A fundamental goal of developmental and stem cell biology is to map the developmental history (ontogeny) of differentiated cell types. Recent advances in high-throughput single-cell sequencing technologies have enabled the construction of comprehensive transcriptional atlases of adult tissues and of developing embryos from measurements of up to millions of individual cells. Parallel advances in sequencing-based lineage-tracing methods now facilitate the mapping of clonal relationships onto these landscapes and enable detailed comparisons between molecular and mitotic histories. Here we review recent progress and challenges, as well as the opportunities that emerge when these two complementary representations of cellular history are synthesized into integrated models of cell differentiation.Cellular differentiation in composition, organization and function represents one of the major innovations of multicellular life. Determining the molecular mechanisms that govern how cells differentiate in their state is thus a long-standing focus in stem cell and developmental biology 1 . A comprehensive record of changes in cell states as tissues and organs develop can give insights into the molecular mechanisms and order of events by which cells choose their terminal identities during embryogenesis or regeneration. It can provide clues as to how to manipulate cell fates in vivo, to predict the origins of developmental pathologies and cancer, and to re-create cell differentiation processes in vitro.Recent advances in single-cell transcriptomics provide a powerful approach to mapping differentiation dynamics by densely sampling cells at different stages. These sampled cells together can be used to construct a continuum of cell states, or a 'landscape', a term

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Section: Computational Tools For State-lineage Mappingmentioning

confidence: 99%

Lineage tracing meets single-cell omics: opportunities and challenges

2020

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“…For this analysis, the sample corresponding to Embryo 3 was used. The developmental lineage was inferred by running the Cassiopeia-Greedy algorithm [36] with priors determined from the frequency of indels across all observed embryos (as done in the original study [14]). Hotspot was run using two different metrics to compare outputs: the 'tree' metric in which the KNN graph was formed from the 30 nearest neighbors according to the inferred lineage and the 'transcription' metric in which PCA was run (on log 2 (x + 1)-transformed counts-per-10, 000 expression values) and the KNN graph was formed from the 30 nearest neighbors in the reduced (top 20 component) transformed space.…”

Section: Analysis Of Mouse Embryogenesis Lineage-tracing Datamentioning

confidence: 99%

Identifying Informative Gene Modules Across Modalities of Single Cell Genomics

DeTomaso

Yosef

2020

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Two fundamental aims that emerge when analyzing single-cell RNAseq data are that of identifying which genes vary in an informative manner and determining how these genes organize into modules. Here we propose a general approach to these problems that operates directly on a given metric of cell-cell similarity, allowing for its integration with any method (linear or non linear) for identifying the primary axes of transcriptional variation between cells. Additionally, we show that when using multimodal data, our procedure can be used to identify genes whose expression reflects alternative notions of similarity between cells, such as physical proximity in a tissue or clonal relatedness in a cell lineage tree. In this manner, we demonstrate that while our method, called Hotspot, is capable of identifying genes that reflect nuanced transcriptional variability between T helper cells, it can also identify spatially-dependent patterns of gene expression in the cerebellum as well as developmentally-heritable expression signatures during embryogenesis.

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Inference of Single-Cell Phylogenies from Lineage Tracing Data

Cited by 2 publications

References 59 publications

Lineage tracing meets single-cell omics: opportunities and challenges

Lineage tracing meets single-cell omics: opportunities and challenges

Identifying Informative Gene Modules Across Modalities of Single Cell Genomics

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