CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data

AbstractRNA sequencing experiments generate large amounts of information about expression levels of genes. Although they are mainly used for quantifying expression levels, they contain much more biologically important information such as copy number variants (CNV). Here, we propose CaSpER, a signal processing approach for identification, visualization, and integrative analysis of focal and large-scale CNV events in multiscale resolution using either bulk or single-cell RNA sequencing data. CaSpER performs smoothing of the genome-wide RNA sequencing signal profiles in different multiscale resolutions, identifying CNV events at different length scales. CaSpER also employs a novel methodology for generation of genome-wide B-allele frequency (BAF) signal profile from the reads and utilizes it in multiscale fashion for correction of CNV calls. The shift in allelic signal is used to quantify the loss-of-heterozygosity (LOH) which is valuable for CNV identification. CaSpER uses Hidden Markov Models (HMM) to assign copy number states to regions. The multiscale nature of CaSpER enables comprehensive analysis of focal and large-scale CNVs and LOH segments. CaSpER performs well in accuracy compared to gold standard SNP genotyping arrays. In particular, analysis of single cell Glioblastoma (GBM) RNA sequencing data with CaSpER reveals novel mutually exclusive and co-occurring CNV sub-clones at different length scales. Moreover, CaSpER discovers gene expression signatures of CNV sub-clones, performs gene ontology (GO) enrichment analysis and identifies potential therapeutic targets for the sub-clones. CaSpER increases the utility of RNA-sequencing datasets and complements other tools for complete characterization and visualization of the genomic and transcriptomic landscape of single cell and bulk RNA sequencing data, especially in cancer research.

Download Full-text

Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue

Circulation ◽

10.1161/circulationaha.120.046528 ◽

2020 ◽

Vol 142 (14) ◽

pp. 1374-1388

Author(s):

Yanming Li ◽

Pingping Ren ◽

Ashley Dawson ◽

Hernan G. Vasquez ◽

Waleed Ageedi ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Aortic Wall ◽

Genome Wide Association ◽

Aortic Tissue ◽

Sequencing Data ◽

Genome Wide ◽

Single Cell Rna Sequencing ◽

Differential Gene

Background: Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues. Methods: We performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies. Results: We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene( ERG ) exerts an important role in maintaining normal aortic wall function. Conclusions: Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.

Download Full-text

A map of tumor–host interactions in glioma at single-cell resolution

GigaScience ◽

10.1093/gigascience/giaa109 ◽

2020 ◽

Vol 9 (10) ◽

Cited By ~ 3

Author(s):

Francesca Pia Caruso ◽

Luciano Garofano ◽

Fulvio D'Angelo ◽

Kai Yu ◽

Fuchou Tang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cross Talk ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Data ◽

Host Interaction ◽

Receptor Interactions ◽

Single Cell Rna Sequencing

ABSTRACT Background Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets. Results We present a novel method, single-cell Tumor–Host Interaction tool (scTHI), to identify significantly activated ligand–receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand–receptor interactions in glioma using 6 publicly available human glioma datasets encompassing 57,060 gene expression profiles from 71 patients. By leveraging this large-scale collection we show that unexpected cross-talk partners are highly conserved across different datasets in the majority of the tumor samples. This suggests that shared cross-talk mechanisms exist in glioma. Conclusions Our results provide a complete map of the active tumor–host interaction pairs in glioma that can be therapeutically exploited to reduce the immunosuppressive action of the microenvironment in brain tumor.

Download Full-text

SSCC: a novel computational framework for rapid and accurate clustering large single cell RNA-seq data

10.1101/344242 ◽

2018 ◽

Cited By ~ 2

Author(s):

Xianwen Ren ◽

Liangtao Zheng ◽

Zemin Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Random Projection ◽

Rna Seq ◽

Sequencing Data ◽

Computational Framework ◽

Human Blood Cells ◽

Single Cell Rna Sequencing ◽

Data Volume

ABSTRACTClustering is a prevalent analytical means to analyze single cell RNA sequencing data but the rapidly expanding data volume can make this process computational challenging. New methods for both accurate and efficient clustering are of pressing needs. Here we proposed a new clustering framework based on random projection and feature construction for large scale single-cell RNA sequencing data, which greatly improves clustering accuracy, robustness and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, our method reached 20% improvements for clustering accuracy and 50-fold acceleration but only consumed 66% memory usage compared to the widely-used software package SC3. Compared to k-means, the accuracy improvement can reach 3-fold depending on the concrete dataset. An R implementation of the framework is available from https://github.com/Japrin/sscClust.

Download Full-text

A bivariate zero-inflated negative binomial model for identifying underlying dependence with application to single cell RNA sequencing data

10.1101/2020.03.06.977728 ◽

2020 ◽

Author(s):

Hunyong Cho ◽

Chuwen Liu ◽

John S. Preisser ◽

Di Wu

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Latent Variable ◽

Large Scale ◽

Negative Binomial ◽

Model Fitting ◽

Sequencing Data ◽

Excess Zeros ◽

Binomial Distributions ◽

Single Cell Rna Sequencing

SummaryMeasuring gene-gene dependence in single cell RNA sequencing (scRNA-seq) count data is often of interest and remains challenging, because an unidentified portion of the zero counts represent non-detected RNA due to technical reasons. Conventional statistical methods that fail to account for technical zeros incorrectly measure the dependence among genes. To address this problem, we propose a bivariate zero-inflated negative binomial (BZINB) model constructed using a bivariate Poisson-gamma mixture with dropout indicators for the technical (excess) zeros. Parameters are estimated based on the EM algorithm and are used to measure the underlying dependence by decomposing the two sources of zeros. Compared to existing models, the proposed BZINB model is specifically designed for estimating dependence and is more flexible, while preserving the marginal zero-inflated negative binomial distributions. Additionally, it has a simple latent variable framework, allowing parameters to have clear and intuitive interpretations, and its computation is feasible with large scale data. Using a recent scRNA-seq dataset, we illustrate model fitting and how the model-based measures can be different from naive measures. The inferential ability of the proposed model is evaluated in a simulation study. An R package ‘bzinb’ is available on CRAN.

Download Full-text

The single-cell eQTLGen consortium

eLife ◽

10.7554/elife.52155 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 18

Author(s):

MGP van der Wijst ◽

DH de Vries ◽

HE Groot ◽

G Trynka ◽

CC Hon ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Cell Types ◽

Eqtl Analysis ◽

Sequencing Data ◽

Scale Population ◽

Trait Locus ◽

Different Cell Types ◽

Affect Gene Expression

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

Download Full-text

Comparison of high-throughput single-cell RNA sequencing data processing pipelines

Briefings in Bioinformatics ◽

10.1093/bib/bbaa116 ◽

2020 ◽

Author(s):

Mingxuan Gao ◽

Mingyi Ling ◽

Xinwei Tang ◽

Shun Wang ◽

Xu Xiao ◽

...

Keyword(s):

Data Processing ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Large Scale ◽

Evaluation Framework ◽

Integrated Analysis ◽

Sequencing Data ◽

Single Experiment ◽

Single Cell Rna Sequencing

Abstract With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

Download Full-text

Regulatory network-based imputation of dropouts in single-cell RNA sequencing data

10.1101/611517 ◽

2019 ◽

Cited By ~ 3

Author(s):

Ana Carolina Leote ◽

Xiaohui Wu ◽

Andreas Beyer

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Regulatory Network ◽

Missing Values ◽

Transcriptional Regulatory Network ◽

Imputation Method ◽

Sequencing Data ◽

Expression Levels ◽

Single Cell Rna Sequencing ◽

Baseline Approach

AbstractSingle-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information.Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Additionally, we tested a baseline approach, where we imputed missing values using the sample-wide average expression of a gene. Unexpectedly, up to 48% of the genes were better predicted using this baseline approach, suggesting negligible cell-to-cell variation of expression levels for many genes. Our work shows that there is no single best imputation method; rather, the best method depends on gene-specific features, such as expression level and expression variation across cells. We thus implemented an R-package called ADImpute (available from https://github.com/anacarolinaleote/ADImpute) that automatically determines the best imputation method for each gene in a dataset.

Download Full-text

Comparison of computational methods for imputing single-cell RNA-sequencing data

10.1101/241190 ◽

2017 ◽

Cited By ~ 10

Author(s):

Lihua Zhang ◽

Shihua Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Real Data ◽

Cell Types ◽

Biological Functions ◽

Sequencing Data ◽

Imputation Methods ◽

Future Studies ◽

Single Cell Rna Sequencing

AbstractSingle-cell RNA-sequencing (scRNA-seq) is a recent breakthrough technology, which paves the way for measuring RNA levels at single cell resolution to study precise biological functions. One of the main challenges when analyzing scRNA-seq data is the presence of zeros or dropout events, which may mislead downstream analyses. To compensate the dropout effect, several methods have been developed to impute gene expression since the first Bayesian-based method being proposed in 2016. However, these methods have shown very diverse characteristics in terms of model hypothesis and imputation performance. Thus, large-scale comparison and evaluation of these methods is urgently needed now. To this end, we compared eight imputation methods, evaluated their power in recovering original real data, and performed broad analyses to explore their effects on clustering cell types, detecting differentially expressed genes, and reconstructing lineage trajectories in the context of both simulated and real data. Simulated datasets and case studies highlight that there are no one method performs the best in all the situations. Some defects of these methods such as scalability, robustness and unavailability in some situations need to be addressed in future studies.

Download Full-text

Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

10.1101/2020.02.09.940221 ◽

2020 ◽

Cited By ~ 2

Author(s):

Mingxuan Gao ◽

Mingyi Ling ◽

Xinwei Tang ◽

Shun Wang ◽

Xu Xiao ◽

...

Keyword(s):

Data Processing ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Large Scale ◽

Evaluation Framework ◽

Integrated Analysis ◽

Sequencing Data ◽

Single Experiment ◽

Single Cell Rna Sequencing

AbstractWith the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. How-ever, it remains unclear whether such integrated analysis would be biased if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performances in terms of running time, computational resource consumption, and data processing consistency using nine public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performances on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

Download Full-text