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

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.

Download Full-text

A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples

10.1101/2021.07.10.451910 ◽

2021 ◽

Author(s):

Wenpin Hou ◽

Zhicheng Ji ◽

Zeyu Chen ◽

E John Wherry ◽

Stephanie C Hicks ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Biological Processes ◽

Rna Seq ◽

Experimental Conditions ◽

Computational Framework ◽

Statistical Framework ◽

Gene Regulatory ◽

Multiple Samples ◽

False Discoveries

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many computational methods have been developed to infer the pseudo-temporal trajectories of cells within a biological sample, methods that compare pseudo-temporal patterns with multiple samples (or replicates) across different experimental conditions are lacking. Lamian is a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. It can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions, and also to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both simulations and real scRNA-seq data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

Download Full-text

Discrete Distributional Differential Expression (D3E) - A Tool for Gene Expression Analysis of Single-cell RNA-seq Data

10.1101/020735 ◽

2015 ◽

Cited By ~ 2

Author(s):

Mihails Delmans ◽

Martin Hemberg

Keyword(s):

Gene Expression ◽

Single Cell ◽

Burst Size ◽

Synthetic Data ◽

Driving Mechanism ◽

Rna Seq ◽

Experimental Conditions ◽

Distributional Method ◽

Average Burst Size ◽

Differential Gene

The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed (DE) between two experimental conditions. Here, we present a discrete, distributional method for differential gene expression (D3E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D3E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. D3E is based on an analytically tractable stochastic model, and thus it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D3E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.

Download Full-text

scImpute: Accurate And Robust Imputation For Single Cell RNA-Seq Data

10.1101/141598 ◽

2017 ◽

Cited By ~ 20

Author(s):

Wei Vivian Li ◽

Jingyi Jessica Li

Keyword(s):

Gene Expression ◽

Single Cell ◽

Differential Expression Analysis ◽

Embryonic Stem ◽

Rna Seq ◽

Human Blood Cells ◽

Gene Expression Dynamics ◽

Rare Cells ◽

Downstream Analysis ◽

Zero Counts

The emerging single cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at single-cell resolution. The analysis of scRNA-seq data is complicated by excess zero or near zero counts, the so-called dropouts due to the low amounts of mRNA sequenced within individual cells. Downstream analysis of scRNA-seq would be severely biased if the dropout events are not properly corrected. We introduce scImpute, a statistical method to accurately and robustly impute the dropout values in scRNA-seq data. ScImpute automatically identifies gene expression values affected by dropout events, and only perform imputation on these values without introducing new bias to the rest data. ScImpute also detects outlier or rare cells and excludes them from imputation. Evaluation based on both simulated and real scRNA-seq data on mouse embryos, mouse brain cells, human blood cells, and human embryonic stem cells suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropout events. scImpute is shown to correct false zero counts, enhance the clustering of cell populations and subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics.

Download Full-text

Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations

Science Advances ◽

10.1126/sciadv.aav2249 ◽

2019 ◽

Vol 5 (5) ◽

pp. eaav2249 ◽

Cited By ~ 22

Author(s):

Dongju Shin ◽

Wookjae Lee ◽

Ji Hyun Lee ◽

Duhee Bang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cost Effective ◽

Specific Gene ◽

Rna Seq ◽

Experimental Conditions ◽

Cost Effective Method ◽

Treatment Experiment ◽

Single Cell Profiling ◽

Multiple Samples

The development of high-throughput single-cell RNA sequencing (scRNA-seq) has enabled access to information about gene expression in individual cells and insights into new biological areas. Although the interest in scRNA-seq has rapidly grown in recent years, the existing methods are plagued by many challenges when performing scRNA-seq on multiple samples. To simultaneously analyze multiple samples with scRNA-seq, we developed a universal sample barcoding method through transient transfection with short barcode oligonucleotides. By conducting a species-mixing experiment, we have validated the accuracy of our method and confirmed the ability to identify multiplets and negatives. Samples from a 48-plex drug treatment experiment were pooled and analyzed by a single run of Drop-Seq. This revealed unique transcriptome responses for each drug and target-specific gene expression signatures at the single-cell level. Our cost-effective method is widely applicable for the single-cell profiling of multiple experimental conditions, enabling the widespread adoption of scRNA-seq for various applications.

Download Full-text

Deep learning of gene relationships from single cell time-course expression data

10.1101/2020.09.21.306332 ◽

2020 ◽

Author(s):

Ye Yuan ◽

Ziv Bar-Joseph

Keyword(s):

Time Series ◽

Deep Learning ◽

Single Cell ◽

Time Course ◽

Expression Profiles ◽

Regulatory Gene ◽

Supplementary Information ◽

Expression Data ◽

Rna Seq ◽

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text

SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

BMC Bioinformatics ◽

10.1186/s12859-021-04282-6 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Verônica R. de Melo Costa ◽

Julianus Pfeuffer ◽

Annita Louloupi ◽

Ulf A. V. Ørom ◽

Rosario M. Piro

Keyword(s):

Gene Expression ◽

Cancer Progression ◽

Time Course ◽

Progressive Increase ◽

Rna Seq ◽

Transcript Processing ◽

Aberrant Transcript ◽

Genome Wide ◽

Splicing Efficiency ◽

Nascent Rna

Abstract Background Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

Download Full-text