Experimental analysis and modelling of single-cell time-course data

AbstractMotivationTime-course gene expression data has been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offers several advantages including the large number of expression profiles available and the ability to focus on individual cells rather than averages. However, this data also raises new computational challenges.ResultsUsing a novel encoding for scRNA-Seq expression data we develop deep learning methods for interaction prediction from time-course data. Our methods use a supervised framework which represents the data as a 3D tensor and train convolutional and recurrent neural networks (CNN and RNN) for predicting interactions. We tested our Time-course Deep Learning (TDL) models on five different time series scRNA-Seq datasets. As we show, TDL can accurately identify causal and regulatory gene-gene interactions and can also be used to assign new function to genes. TDL improves on prior methods for the above tasks and can be generally applied to new time series scRNA-Seq data.Availability and ImplementationFreely available at https://github.com/xiaoyeye/[email protected] informationSupplementary data are available at XXX online.

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SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

10.1101/088856 ◽

2016 ◽

Cited By ~ 1

Author(s):

Hirotaka Matsumoto ◽

Hisanori Kiryu ◽

Chikara Furusawa ◽

Minoru S.H. Ko ◽

Shigeru B.H. Ko ◽

...

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Time Course ◽

Network Inference ◽

High Time ◽

High Time Resolution ◽

Rna Seq ◽

Functional Relationships ◽

Inference Algorithms ◽

Time Course Data

AbstractThe analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.In this study, we developed the novel and efficient algorithm SCODE to infer regulatory networks, based on ordinary differential equations. We applied SCODE to three single-cell RNA-Seq datasets and confirmed that SCODE can reconstruct observed expression dynamics. We evaluated SCODE by comparing its inferred networks with use of a DNaseI-footprint based network. The performance of SCODE was best for two of the datasets and nearly best for the remaining dataset. We also compared the runtimes and showed that the runtimes for SCODE are significantly shorter than for alternatives. Thus, our algorithm provides a promising approach for further single-cell differentiation analyses.The R source code of SCODE is available at https://github.com/hmatsu1226/SCODE.

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Alignment of time-course single-cell RNA-seq data with CAPITAL

10.1101/859751 ◽

2019 ◽

Author(s):

Reiichi Sugihara ◽

Yuki Kato ◽

Tomoya Mori ◽

Yukio Kawahara

Keyword(s):

Gene Expression ◽

Single Cell ◽

Time Course ◽

Rna Seq ◽

Experimental Conditions ◽

Tree Alignment ◽

Public Data ◽

Gene Expression Dynamics ◽

Time Course Data ◽

Cell Trajectory

AbstractRecent techniques on single-cell RNA sequencing have boosted transcriptome-wide observation of gene expression dynamics of time-course data at a single-cell scale. Typical examples of such analysis include inference of a pseudotime cell trajectory, and comparison of pseudotime trajectories between different experimental conditions will tell us how feature genes regulate a dynamic cellular process. Existing methods for comparing pseudotime trajectories, however, force users to select trajectories to be compared because they can deal only with simple linear trajectories, leading to the possibility of making a biased interpretation. Here we present CAPITAL, a method for comparing pseudotime trajectories with tree alignment whereby trajectories including branching can be compared without any knowledge of paths to be compared. Computational tests on time-series public data indicate that CAPITAL can align non-linear pseudotime trajectories and reveal gene expression dynamics.

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Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

BMC Bioinformatics ◽

10.1186/s12859-021-04052-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Arika Fukushima ◽

Masahiro Sugimoto ◽

Satoru Hiwa ◽

Tomoyuki Hiroyasu

Keyword(s):

Gene Expression ◽

Time Course ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Response To Therapy ◽

Hcv Infection ◽

Entire Treatment Period ◽

Time Points ◽

Time Course Data ◽

Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

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Identification of the macrophage-specific promoter signature in FANTOM5 mouse embryo developmental time course data

Journal of Leukocyte Biology ◽

10.1189/jlb.1a0417-150rr ◽

2017 ◽

Vol 102 (4) ◽

pp. 1081-1092 ◽

Cited By ~ 17

Author(s):

Kim M. Summers ◽

David A. Hume

Keyword(s):

Mouse Embryo ◽

Time Course ◽

Developmental Time ◽

Time Course Data

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An empirical Bayes change-point model for transcriptome time-course data

The Annals of Applied Statistics ◽

10.1214/20-aoas1403 ◽

2021 ◽

Vol 15 (1) ◽

Author(s):

Tian Tian ◽

Ruihua Cheng ◽

Zhi Wei

Keyword(s):

Change Point ◽

Empirical Bayes ◽

Time Course ◽

Point Model ◽

Time Course Data ◽

Change Point Model

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Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

Journal of Integrative Bioinformatics ◽

10.1515/jib-2017-0011 ◽

2017 ◽

Vol 14 (2) ◽

Author(s):

Qihua Tan ◽

Mads Thomassen ◽

Mark Burton ◽

Kristian Fredløv Mose ◽

Klaus Ejner Andersen ◽

...

Keyword(s):

Gene Expression ◽

Correlation Coefficient ◽

Parametric Analysis ◽

Time Course ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Microarray Time Course ◽

Time Course Data ◽

Generalized Correlation ◽

Non Parametric

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

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