scholarly journals Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming

2017 ◽  
Author(s):  
Geoffrey Schiebinger ◽  
Jian Shu ◽  
Marcin Tabaka ◽  
Brian Cleary ◽  
Vidya Subramanian ◽  
...  
Keyword(s):  
Single Cell ◽  
Cellular Differentiation ◽  
Large Scale ◽  
Time Course ◽  
Optimal Transport ◽  
Cellular Reprogramming ◽  
Modern Biology ◽  
Cell Class ◽  
High Resolution Map ◽  
Cell Gene Expression

AbstractUnderstanding the molecular programs that guide cellular differentiation during development is a major goal of modern biology. Here, we introduce an approach, WADDINGTON-OT, based on the mathematics of optimal transport, for inferring developmental landscapes, probabilistic cellular fates and dynamic trajectories from large-scale single-cell RNA-seq (scRNA-seq) data collected along a time course. We demonstrate the power of WADDINGTON-OT by applying the approach to study 65,781 scRNA-seq profiles collected at 10 time points over 16 days during reprogramming of fibroblasts to iPSCs. We construct a high-resolution map of reprogramming that rediscovers known features; uncovers new alternative cell fates including neuraland placental-like cells; predicts the origin and fate of any cell class; highlights senescent-like cells that may support reprogramming through paracrine signaling; and implicates regulatory models in particular trajectories. Of these findings, we highlight Obox6, which we experimentally show enhances reprogramming efficiency. Our approach provides a general framework for investigating cellular differentiation.

SCOTCluster: Deep Clustering with Optimal Transport for Large-scale Single-cell RNA-seq Data

2021 ◽  
Author(s):  
Faning Long ◽  
Xiaojun Ding ◽  
Xiaoqing Peng ◽  
Jianxin Wang ◽  
Xiaoshu Zhu
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Optimal Transport ◽  
Rna Seq

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

Cell ◽  
2019 ◽  
Vol 176 (4) ◽  
pp. 928-943.e22 ◽  
Author(s):  
Geoffrey Schiebinger ◽  
Jian Shu ◽  
Marcin Tabaka ◽  
Brian Cleary ◽  
Vidya Subramanian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Optimal Transport ◽  
Developmental Trajectories ◽  
Cell Gene Expression ◽  
Transport Analysis ◽  
Cell Gene

scholarly journals destiny – diffusion maps for large-scale single-cell data in R

2015 ◽  
Author(s):  
Philipp Angerer ◽  
Laleh Haghverdi ◽  
Maren Büttner ◽  
Fabian J. Theis ◽  
Carsten Marr ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Cellular Reprogramming ◽  
Noise Model ◽  
Diffusion Maps ◽  
Time Resolved ◽  
Describing Functions ◽  
Link Type ◽  
Cell Expression ◽  
Cell Data

ABSTRACTSummaryDiffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single cell expression data. Here we present destiny, an efficient R implementation of the diffusion map algorithm. Our package includes a single-cell specific noise model allowing for missing and censored values. In contrast to previous implementations, we further present an efficient nearest-neighbour approximation that allows for the processing of hundreds of thousands of cells and a functionality for projecting new data on existing diffusion maps. We exemplarily apply destiny to a recent time-resolved mass cytometry dataset of cellular reprogramming.Availability and implementationdestiny is an open-source R/Bioconductor package http://bioconductor.org/packages/ destiny also available at https://www.helmholtz-muenchen.de/icb/destiny. A detailed vignette describing functions and workflows is provided with the [email protected], [email protected]

scholarly journals Scanpy for analysis of large-scale single-cell gene expression data

2017 ◽  
Author(s):  
F. Alexander Wolf ◽  
Philipp Angerer ◽  
Fabian J. Theis
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Expression Data ◽  
Cell Gene Expression ◽  
Gene Regulatory ◽  
Cell Gene

We present Scanpy, a scalable toolkit for analyzing single-cell gene expression data. It includes preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing and simulation of gene regulatory networks. The Python-based implementation efficiently deals with datasets of more than one million cells and enables easy interfacing of advanced machine learning packages. Code is available fromhttps://github.com/theislab/scanpy.

scholarly journals Massive single-cell RNA-seq analysis and imputation via deep learning

2018 ◽  
Author(s):  
Yue Deng ◽  
Feng Bao ◽  
Qionghai Dai ◽  
Lani F. Wu ◽  
Steven J. Altschuler
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Rna Seq ◽  
Fine Grained ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Gene Expression

Recent advances in large-scale single cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states within heterogeneous tissues. We present scScope, a scalable deep-learning based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.

scholarly journals Aligning single-cell developmental and reprogramming trajectories identifies molecular determinants of reprogramming outcome

2017 ◽  
Author(s):  
Davide Cacchiarelli ◽  
Xiaojie Qiu ◽  
Sanjay Srivatsan ◽  
Michael Ziller ◽  
Eliah Overbey ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Human Fibroblasts ◽  
Cell Types ◽  
Cellular Reprogramming ◽  
Bmp Signaling ◽  
Great Promise ◽  
Scale Production ◽  
Branch Points ◽  
Molecular Determinants

AbstractCellular reprogramming through manipulation of defined factors holds great promise for large-scale production of cell types needed for use in therapy, as well as for expanding our understanding of the general principles of gene regulation. MYOD-mediated myogenic reprogramming, which converts many cell types into contractile myotubes, remains one of the best characterized model system for direct conversion by defined factors. However, why MYOD can efficiently convert some cell types into myotubes but not others remains poorly understood. Here, we analyze MYOD-mediated reprogramming of human fibroblasts at pseudotemporal resolution using single-cell RNA-Seq. Successfully reprogrammed cells navigate a trajectory with two branches that correspond to two barriers to reprogramming, with cells that select incorrect branches terminating at aberrant or incomplete reprogramming outcomes. Differential analysis of the major branch points alongside alignment of the successful reprogramming path to a primary myoblast trajectory revealed Insulin and BMP signaling as crucial molecular determinants of an individual cell’s reprogramming outcome, that when appropriately modulated, increased efficiency more than five-fold. Our single-cell analysis reveals that MYOD is sufficient to reprogram cells only when the extracellular milieu is favorable, supporting MYOD with upstream signaling pathways that drive normal myogenesis in development.

NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport

2021 ◽  
Author(s):  
Noa Moriel ◽  
Enes Senel ◽  
Nir Friedman ◽  
Nikolaus Rajewsky ◽  
Nikos Karaiskos ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Optimal Transport ◽  
Spatial Reconstruction ◽  
Cell Gene Expression ◽  
Cell Gene

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

Cell ◽  
2019 ◽  
Vol 176 (6) ◽  
pp. 1517 ◽  
Author(s):  
Geoffrey Schiebinger ◽  
Jian Shu ◽  
Marcin Tabaka ◽  
Brian Cleary ◽  
Vidya Subramanian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Optimal Transport ◽  
Developmental Trajectories ◽  
Cell Gene Expression ◽  
Transport Analysis ◽  
Cell Gene
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