Trajectory-based differential expression analysis for single-cell sequencing data

AbstractTrajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression levels during biological processes such as the cell cycle, cell type differentiation, and cellular activation. Downstream of trajectory inference, it is vital to discover genes that are associated with the lineages in the trajectory to illuminate the underlying biological processes. Furthermore, genes that are differentially expressed between developmental/activational lineages might be highly relevant to further unravel the system under study. Current data analysis procedures, however, typically cluster cells and assess differential expression between the clusters, which fails to exploit the continuous resolution provided by trajectory inference to its full potential. The few available non-cluster-based methods only assess broad differences in gene expression between lineages, hence failing to pinpoint the exact types of divergence. We introduce a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of (i) within-lineage differential expression by detecting associations between gene expression and pseudotime over an entire lineage or by comparing gene expression between points/regions within the lineage and (ii) between-lineage differential expression by comparing gene expression between lineages over the entire lineages or at specific points/regions. By incorporating observation-level weights, the model additionally allows to account for zero inflation, commonly observed in single-cell RNA-seq data from full-length protocols. We evaluate the method on simulated and real datasets from droplet-based and full-length protocols, and show that the flexible inference framework is capable of yielding biological insights through a clear interpretation of the data.

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DEsingle for detecting three types of differential expression in single-cell RNA-seq data

10.1101/173997 ◽

2017 ◽

Cited By ~ 1

Author(s):

Zhun Miao ◽

Ke Deng ◽

Xiaowo Wang ◽

Xuegong Zhang

Keyword(s):

Single Cell ◽

Differential Expression ◽

Negative Binomial ◽

Single Cells ◽

R Package ◽

Supplementary Information ◽

Binomial Model ◽

Supplementary Data ◽

Rna Seq ◽

Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

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A novel single-cell based method for breast cancer prognosis

10.1101/2020.04.26.062794 ◽

2020 ◽

Author(s):

Xiaomei Li ◽

Lin Liu ◽

Greg Goodall ◽

Andreas Schreiber ◽

Taosheng Xu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Single Cell ◽

Tumor Heterogeneity ◽

Breast Cancer Prognosis ◽

Cancer Prognosis ◽

Biological Processes ◽

Expression Data ◽

Rna Seq ◽

Novel Method

AbstractBreast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.Author summaryVarious computational methods have been developed for breast cancer prognosis. However, those methods mainly use the gene expression data generated by the bulk RNA sequencing techniques, which average the expression level of a gene across different cell types. As breast cancer is a heterogenous disease, the bulk gene expression may not be the ideal resource for cancer prognosis. In this study, we propose a novel method to improve breast cancer prognosis using scRNA-seq data. The proposed method has been applied to the EMT scRNA-seq dataset for identifying breast cancer signatures for prognosis. In comparison with existing bulk expression data based methods in breast cancer prognosis, our method shows a better performance. Our single-cell-based signatures provide clues to the relation between EMT and clinical outcomes of breast cancer. In addition, the proposed method can also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.

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Biological Process Activity Transformation of Single Cell Gene Expression for Cross-Species Alignment

10.1101/555268 ◽

2019 ◽

Author(s):

Hongxu Ding ◽

Andrew Blair ◽

Ying Yang ◽

Joshua M. Stuart

Keyword(s):

Gene Expression ◽

Single Cell ◽

Biological Process ◽

Biological Processes ◽

Rna Seq ◽

Gene Set ◽

Cell Gene Expression ◽

Cell Gene

ABSTRACTThe maintenance and transition of cellular states are controlled by biological processes. Here we present a gene set-based transformation of single cell RNA-Seq data into biological process activities that provides a robust description of cellular states. Moreover, as these activities represent species-independent descriptors, they facilitate the alignment of single cell states across different organisms.

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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.

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zingeR: unlocking RNA-seq tools for zero-inflation and single cell applications

10.1101/157982 ◽

2017 ◽

Cited By ~ 7

Author(s):

Koen Van den Berge ◽

Charlotte Soneson ◽

Michael I. Love ◽

Mark D. Robinson ◽

Lieven Clement

Keyword(s):

Single Cell ◽

Differential Expression ◽

Expression Analysis ◽

Negative Binomial ◽

Differential Expression Analysis ◽

Negative Binomial Model ◽

Binomial Model ◽

Rna Seq ◽

Zero Inflation ◽

Zero Counts

AbstractDropout in single cell RNA-seq (scRNA-seq) applications causes many transcripts to go undetected. It induces excess zero counts, which leads to power issues in differential expression (DE) analysis and has triggered the development of bespoke scRNA-seq DE tools that cope with zero-inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce zingeR, a zero-inflated negative binomial model that identifies excess zero counts and generates observation weights to unlock bulk RNA-seq pipelines for zero-inflation, boosting performance in scRNA-seq differential expression analysis.

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Scalable probabilistic matrix factorization for single-cell RNA-seq analysis

10.1101/496810 ◽

2018 ◽

Cited By ~ 3

Author(s):

Pedro F. Ferreira ◽

Alexandra M. Carvalho ◽

Susana Vinga

Keyword(s):

Gene Expression ◽

Dimensionality Reduction ◽

Single Cell ◽

Matrix Factorization ◽

Latent Variables ◽

Probabilistic Models ◽

Negative Binomial ◽

Hidden Variables ◽

Generative Models ◽

Rna Seq

Motivation: The gene expression profile of a cell dictates its function in molecular processes, and can be used to probe its health status. This represents a step forward in the deep characterization of diseases such as cancer and may lead to breakthroughs in their treatment. The technology used to measure the gene expression of isolated cells, single-cell RNA-seq (scRNA-seq), has emerged in the last decade as a key enabler of this progress. However, the use of existing methods for dimensionality reduction, clustering and differential expression is limited by the specificities of the data obtained from scRNA-seq experiments, where technical factors may confound analyses of the true biological signal and contribute to spurious results. To overcome this issue, a possible approach is designing probabilistic generative models of the data with hidden variables encoding different underlying processes. Results: We propose two novel probabilistic models for scRNA-seq data: modified probabilistic count matrix factorization (m-pCMF) and Bayesian zero-inflated negative binomial factorization (ZINBayes). These build upon previous models in the literature while leveraging scalable Bayesian inference via variational methods. We show that the proposed methods are competitive with the state-of-the-art models for robust dimensionality reduction in modern data sets, and improve upon the current best Bayesian model for small numbers of cells. The results show that building probabilistic models of latent variables which encode domain knowledge and using variational inference constitute a promising approach to analyse scRNA-seq data in a scalable way. Availability: m-pCMF and ZINBayes are publicly available as Python packages at https://github.com/pedrofale/, along with the code to reproduce all the results. Contact: [email protected]

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Isoform cell type specificity in the mouse primary motor cortex

10.1101/2020.03.05.977991 ◽

2020 ◽

Author(s):

A. Sina Booeshaghi ◽

Zizhen Yao ◽

Cindy van Velthoven ◽

Kimberly Smith ◽

Bosiljka Tasic ◽

...

Keyword(s):

Gene Expression ◽

Motor Cortex ◽

Single Cell ◽

Primary Motor Cortex ◽

Cell Types ◽

Full Length ◽

Rna Seq ◽

Cell Type Specificity ◽

Gene Level ◽

Isoform Specificity

Full-length SMART-Seq single-cell RNA-seq can be used to measure gene expression at isoform resolution, making possible the identification of isoform markers for cell types and for an isoform atlas. In a comprehensive analysis of 6,160 mouse primary motor cortex cells assayed with SMART-Seq, we find numerous examples of isoform specificity in cell types, including isoform shifts between cell types that are masked in gene-level analysis. These findings can be used to refine spatial gene expression information to isoform resolution. Our results highlight the utility of full-length single-cell RNA-seq when used in conjunction with other single-cell RNA-seq technologies.

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Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles

10.1101/027219 ◽

2015 ◽

Cited By ~ 18

Author(s):

Kieran Campbell ◽

Chris P Ponting ◽

Caleb Webber

Keyword(s):

Gene Expression ◽

Single Cell ◽

Biological Processes ◽

Rna Seq ◽

Principal Curves ◽

Cell Level ◽

Laplacian Eigenmaps ◽

Low Dimensional ◽

Cell Data ◽

Insight Into

Advances in RNA-seq technologies provide unprecedented insight into the variability and heterogeneity of gene expression at the single-cell level. However, such data offers only a snapshot of the transcriptome, whereas it is often the progression of cells through dynamic biological processes that is of interest. As a result, one outstanding challenge is to infer such progressions by ordering gene expression from single cell data alone, known as the cell ordering problem. Here, we introduce a new method that constructs a low-dimensional non-linear embedding of the data using laplacian eigenmaps before assigning each cell a pseudotime using principal curves. We characterise why on a theoretical level our method is more robust to the high levels of noise typical of single-cell RNA-seq data before demonstrating its utility on two existing datasets of differentiating cells.

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Biological process activity transformation of single cell gene expression for cross-species alignment

Nature Communications ◽

10.1038/s41467-019-12924-w ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 7

Author(s):

Hongxu Ding ◽

Andrew Blair ◽

Ying Yang ◽

Joshua M. Stuart

Keyword(s):

Gene Expression ◽

Single Cell ◽

Biological Process ◽

Biological Processes ◽

Rna Seq ◽

Gene Set ◽

Cell Gene Expression ◽

Cell Gene

Abstract The maintenance and transition of cellular states are controlled by biological processes. Here we present a gene set-based transformation of single cell RNA-Seq data into biological process activities that provides a robust description of cellular states. Moreover, as these activities represent species-independent descriptors, they facilitate the alignment of single cell states across different organisms.

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ROSeq: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data

10.1101/374025 ◽

2018 ◽

Cited By ~ 1

Author(s):

Krishan Gupta ◽

Manan Lalit ◽

Aditya Biswas ◽

Ujjwal Maulik ◽

Sanghamitra Bandyopadhyay ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Differential Expression ◽

Negative Binomial ◽

Single Cells ◽

Expression Patterns ◽

Critical Role ◽

Differential Expression Analysis ◽

Parametric Models ◽

Specific Gene

1AbstractSystematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single cell expression data. The zero-inflated version of Poisson/Negative Binomial and Log-Normal distributions have emerged as the most popular alternatives due to their ability to accommodate high dropout rates, as commonly observed in single cell data. While the majority of the parametric approaches directly model expression estimates, we explore the potential of modeling expression-ranks, as robust surrogates for transcript abundance. Here we examined the performance of the Discrete Generalized Beta Distribution (DGBD) on real data and devised a Wald-type test for comparing gene expression across two phenotypically divergent groups of single cells. We performed a comprehensive assessment of the proposed method, to understand its advantages as compared to some of the existing best practice approaches. Besides striking a reasonable balance between Type 1 and Type 2 errors, we concluded that ROSeq, the proposed differential expression test is exceptionally robust to expression noise and scales rapidly with increasing sample size. For wider dissemination and adoption of the method, we created an R package called ROSeq, and made it available on the Bioconductor platform.

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