Time-Series Analysis of Gene Correlation Networks based on Single-Cell Transcriptome Data

2021 ◽  
Author(s):  
Yasuhito Asano ◽  
Tatsuro Ogawa ◽  
Shigeyuki Shichino ◽  
Satoshi Ueha ◽  
Koji Matsushima ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Single Cell ◽  
Transcriptome Data ◽  
Series Analysis ◽  
Correlation Networks ◽  
Gene Correlation ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

scholarly journals Reconstructing cell cycle pseudo time-series via single-cell transcriptome data

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Zehua Liu ◽  
Huazhe Lou ◽  
Kaikun Xie ◽  
Hao Wang ◽  
Ning Chen ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Time Series ◽  
Single Cell ◽  
Transcriptome Data ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

scholarly journals Super-cells untangle large and complex single-cell transcriptome networks

2021 ◽  
Author(s):  
Mariia Bilous ◽  
Loc Tran ◽  
Chiara Cianciaruso ◽  
Santiago J Carmona ◽  
Mikael J Pittet ◽  
...  
Keyword(s):  
Single Cell ◽  
Coarse Graining ◽  
Cell Populations ◽  
Cell Type ◽  
Large Numbers ◽  
Single Cell Rna Sequencing ◽  
Gene Correlation ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Here we develop a network-based coarse-graining framework where highly similar cells are merged into super-cells. We demonstrate that super-cells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, super-cells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

scholarly journals De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data

2016 ◽  
Vol 19 (2) ◽  
pp. 266-277 ◽  
Author(s):  
Dominic Grün ◽  
Mauro J. Muraro ◽  
Jean-Charles Boisset ◽  
Kay Wiebrands ◽  
Anna Lyubimova ◽  
...  
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
De Novo ◽  
Transcriptome Data ◽  
Cell Identity ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

scholarly journals Dissecting Transition Cells from Single-cell Transcriptome Data through Multiscale Stochastic Dynamics

2021 ◽  
Author(s):  
Peijie Zhou ◽  
Shuxiong Wang ◽  
Tiejun Li ◽  
Qing Nie
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Stochastic Dynamics ◽  
Multiple Scales ◽  
Coarse Grained ◽  
Rate Theory ◽  
Transcriptome Data ◽  
Stochastic Dynamical Systems ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractAdvances of single-cell technologies allow scrutinizing of heterogeneous cell states, however, analyzing transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique for the underlying stochastic dynamical systems that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transition, and distinguishes meta-stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using the coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. Mathematical analysis reveals consistency of the method with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms and benchmarking with seven existing tools, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.

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