scholarly journals Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics

2017 ◽  
Author(s):  
Kelly Street ◽  
Davide Risso ◽  
Russell B. Fletcher ◽  
Diya Das ◽  
John Ngai ◽  
...  
Keyword(s):  
Single Cell ◽  
Branching Processes ◽  
Cell Lineage ◽  
Flexible Tool ◽  
Cell Lineages ◽  
Differentiated Cells ◽  
Multipotent Progenitors ◽  
Cell Gene Expression ◽  
Novel Method ◽  
Cell Gene

AbstractSingle-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. These methods can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a number of statistical and computational methods have been proposed for analyzing cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. Here, we introduce a novel method, Slingshot, for inferring multiple developmental lineages from single-cell gene expression data. Slingshot is a uniquely robust and flexible tool for inferring developmental lineages and ordering cells to reflect continuous, branching processes.

scholarly journals Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

2020 ◽  
Author(s):  
Nan Papili Gao ◽  
Olivier Gandrillon ◽  
András Páldi ◽  
Ulysse Herbach ◽  
Rudiyanto Gunawan
Keyword(s):  
Gene Expression ◽  
Cell Differentiation ◽  
Single Cell ◽  
Burst Size ◽  
Mechanistic Model ◽  
Cell Lineage ◽  
Transcriptional Analysis ◽  
Cell Gene Expression ◽  
Branching Point ◽  
Cell Gene

ABSTRACTWe employed our previously-described single-cell gene expression analysis CALISTA (Clustering And Lineage Inference in Single-Cell Transcriptional Analysis) to evaluate transcriptional uncertainty at the single-cell level using a stochastic mechanistic model of gene expression. We reconstructed a transcriptional uncertainty landscape during cell differentiation by visualizing single-cell transcriptional uncertainty surface over a two dimensional representation of the single-cell gene expression data. The reconstruction of transcriptional uncertainty landscapes for ten publicly available single-cell gene expression datasets from cell differentiation processes with linear, single or multi-branching cell lineage, reveals universal features in the cell differentiation trajectory that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceeding the increase in the cell transcriptional uncertainty. Finally, we provided biological interpretations of the universal rise-then-fall profile of the transcriptional uncertainty landscape, including a link with the Waddington’s epigenetic landscape, that is generalizable to every cell differentiation system.

scholarly journals Robust Lineage Reconstruction from High-Dimensional Single-Cell Data

2016 ◽  
Author(s):  
Gregory Giecold ◽  
Eugenio Marco ◽  
Lorenzo Trippa ◽  
Guo-Cheng Yuan
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Quantitative Estimate ◽  
Cell Lineage ◽  
Computational Method ◽  
Expression Data ◽  
Cell Gene Expression ◽  
Cell Data ◽  
Cell Gene

Single-cell gene expression data provide invaluable resources for systematic characterization of cellular hierarchy in multi-cellular organisms. However, cell lineage reconstruction is still often associated with significant uncertainty due to technological constraints. Such uncertainties have not been taken into account in current methods. We present ECLAIR, a novel computational method for the statistical inference of cell lineage relationships from single-cell gene expression data. ECLAIR uses an ensemble approach to improve the robustness of lineage predictions, and provides a quantitative estimate of the uncertainty of lineage branchings. We show that the application of ECLAIR to published datasets successfully reconstructs known lineage relationships and significantly improves the robustness of predictions. In conclusion, ECLAIR is a powerful bioinformatics tool for single-cell data analysis. It can be used for robust lineage reconstruction with quantitative estimate of prediction accuracy.

scholarly journals CALISTA: Clustering and Lineage Inference in Single-Cell Transcriptional Analysis

2018 ◽  
Author(s):  
Nan Papili Gao ◽  
Thomas Hartmann ◽  
Tao Fang ◽  
Rudiyanto Gunawan
Keyword(s):  
Cell Differentiation ◽  
Single Cell ◽  
Transcriptional Profiling ◽  
Cell Lineage ◽  
Transcriptional Analysis ◽  
Lineage Specification ◽  
Cell Clustering ◽  
Cell Gene Expression ◽  
Cell Expression ◽  
Cell Gene

SummaryWe present CALISTA (Clustering and Lineage Inference in Single-Cell Transcriptional Analysis), a numerically efficient and highly scalable toolbox for an end-to-end analysis of single-cell transcriptomic profiles. CALISTA includes four essential single-cell analyses for cell differentiation studies, including single-cell clustering, reconstruction of cell lineage specification, transition gene identification, and pseudotemporal cell ordering. In these analyses, we employ a likelihood-based approach where single-cell mRNA counts are described by a probabilistic distribution function associated with stochastic gene transcriptional bursts and random technical dropout events. We evaluated the performance of CALISTA by analyzing single-cell gene expression datasets from in silico simulations and various single-cell transcriptional profiling technologies, comprising a few hundreds to tens of thousands of cells. A comparison with existing single-cell expression analyses, including MONOCLE 2 and SCANPY, demonstrated the superiority of CALISTA in reconstructing cell lineage progression and ordering cells along cell differentiation paths. CALISTA is freely available on https://www.cabselab.com/calista.

scholarly journals Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst

2010 ◽  
Vol 18 (4) ◽  
pp. 675-685 ◽  
Author(s):  
Guoji Guo ◽  
Mikael Huss ◽  
Guo Qing Tong ◽  
Chaoyang Wang ◽  
Li Li Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Cell Fate Decisions ◽  
Cell Gene Expression ◽  
Cell Gene
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