Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS

Li, Shao; Xue, Rui; Lu, Xiaoyan; Liao, Jie; Shao, Xin; Fan, Xiaohui

doi:10.1016/j.csbj.2021.07.016

Cited by 9 publications

(8 citation statements)

References 70 publications

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“…Although several attempts can infer the cell-type proportions from traditional RNA-seq data, understanding bulk tissue at single-cell spatial resolution remains a pressing need in the field. Cell identity can be characterized by the clustering space of single-cell gene expression profiles 59 , and expression features can be stably retained across different conditions, technologies, and species 29 . Based on this, we used a deep learning model, termed β-VAE, to generate single-cell profiles with biological significance within the clustering space of each cell type and map them accurately to tissue coordinates, thus deconvolving the bulk transcriptomics into spatially resolved single-cell transcriptomics data.…”

Section: Discussionmentioning

confidence: 99%

De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution

et al. 2022

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Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms’ biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space (https://github.com/ZJUFanLab/bulk2space), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.

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Section: Discussionmentioning

confidence: 99%

De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution

et al. 2022

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“…However, there is no clear clinical meaning for pseudocells, so it is not reliable to explain underlying biological processes based upon WGCNA module analysis. Particularly, it is important to identify genes associated with dynamic changes during disease development or embryogenesis [49] , which is a difficult task for these methods. To address these challenges, we proposed scDisProcema, which, on the one hand, utilized the information obtained from upstream analysis of single-cell data.…”

Section: Discussionmentioning

confidence: 99%

Tracing the cell-type-specific modules of immune responses during COVID-19 progression using scDisProcema

Yang²,

Qian³

et al. 2022

Computational and Structural Biotechnology Journal

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“…Therefore, accurately characterizing the temporal dynamics of gene expression over time points is crucial for developmental biology [10,11], tumor biology [12][13][14], and biogerontology [15][16][17], which allows us to decipher the dynamic cellular heterogeneity during cell differentiation [18]; identifying cancer driver genes during the status transformation [14]; and investigating the mechanisms of cell senescence during aging [15]. Although the time-resolved and time-course scRNA-seq studies are initially designed for different purposes, they both require the same data analysis tools for detecting the temporal dynamics of gene expression [19]. As we know, the statistical modeling for both types of scRNA-seq data to identify temporal gene expression patterns meets significant challenges, i.e., modeling unwanted variables, accounting for temporal dependencies, and even characterizing nonstationary cell populations of scRNA-seq data.…”

Section: Introductionmentioning

confidence: 99%

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single cell transcriptomics data with TDEseq

Fan,

Li,

Sun

2023

Preprint

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The advanced single-cell RNA sequencing (scRNA-seq) technology allows us to measure the temporal dynamics of gene expression over multiple time points to gain an understanding of previously unknown biological diversity. However, there is currently a lack of efficient computational tools tailored for scRNA-seq data analysis, which can simultaneously analyze the entire sequence across different time points as well as accounting for temporal batch effects. In this paper, we present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type, including growth, recession, peak, and trough. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns, even with the case in large heterogeneous. TDEseq is also capable of handling unwanted confounding factors that may be hidden in biological processes, thereby enabling advancements in investigations that utilize time-resolved or time-course scRNA-seq data, particularly in the multi-sample multi-stage studies.

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Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS

Abstract: Graphical abstract

Cited by 9 publications

References 70 publications

De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution

De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution

Tracing the cell-type-specific modules of immune responses during COVID-19 progression using scDisProcema

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single cell transcriptomics data with TDEseq

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