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Article ; Online: OSCAA: A two-dimensional Gaussian mixture model for copy number variation association analysis.

Yu, Xuanxuan / Luo, Xizhi / Cai, Guoshuai / Xiao, Feifei

2024

Abstract: Copy number variants (CNVs) are prevalent in the human genome and are found to have a profound effect on genomic organization and human diseases. Discovering disease-associated CNVs is critical for understanding the pathogenesis of diseases and aiding ... ...

Abstract	Copy number variants (CNVs) are prevalent in the human genome and are found to have a profound effect on genomic organization and human diseases. Discovering disease-associated CNVs is critical for understanding the pathogenesis of diseases and aiding their diagnosis and treatment. However, traditional methods for assessing the association between CNVs and disease risks adopt a two-stage strategy conducting quantitative CNV measurements first and then testing for association, which may lead to biased association estimation and low statistical power, serving as a major barrier in routine genome-wide assessment of such variation. In this article, we developed One-Stage CNV-disease Association Analysis (OSCAA), a flexible algorithm to discover disease-associated CNVs for both quantitative and qualitative traits. OSCAA employs a two-dimensional Gaussian mixture model that is built upon the PCs from copy number intensities, accounting for technical biases in CNV detection while simultaneously testing for their effect on outcome traits. In OSCAA, CNVs are identified and their associations with disease risk are evaluated simultaneously in a single step, taking into account the uncertainty of CNV identification in the statistical model. Our simulations demonstrated that OSCAA outperformed the existing one-stage method and traditional two-stage methods by yielding a more accurate estimate of the CNV-disease association, especially for short CNVs or CNVs with weak signals. In conclusion, OSCAA is a powerful and flexible approach for CNV association testing with high sensitivity and specificity, which can be easily applied to different traits and clinical risk predictions.
Language	English
Publishing date	2024-03-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	605785-8
ISSN	1098-2272 ; 0741-0395
ISSN (online)	1098-2272
ISSN	0741-0395
DOI	10.1002/gepi.22558
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Article ; Online: A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data.

Qin, Fei / Cai, Guoshuai / Amos, Christopher I / Xiao, Feifei

Genome research

2024 Volume 34, Issue 1, Page(s) 85–93

Abstract: The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in ...

Abstract	The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.
MeSH term(s)	DNA Copy Number Variations ; Sequence Analysis, DNA/methods ; Base Sequence ; Cluster Analysis
Language	English
Publishing date	2024-02-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	1284872-4
ISSN	1549-5469 ; 1088-9051 ; 1054-9803
ISSN (online)	1549-5469
ISSN	1088-9051 ; 1054-9803
DOI	10.1101/gr.278098.123
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Article: A statistical learning method for simultaneous copy number estimation and subclone clustering with single cell sequencing data.

Qin, Fei / Cai, Guoshuai / Xiao, Feifei

bioRxiv : the preprint server for biology

2023

Abstract: The availability of single cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in ...

Abstract	The availability of single cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, since cells comprising a subpopulation are found to share genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified CNAs) in the procedure of CNA detection, hence diminishing the accuracy of subclone identification from a large complex cell population. In this study, we developed a CNA detection method based on a fused lasso model, referred to as FLCNA, which can simultaneously identify subclones in single cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA benchmarking to existing copy number estimation methods (SCOPE, HMMcopy) in combination with the existing and commonly used clustering methods. Interestingly, application of FLCNA to a real scDNA-seq dataset of breast cancer revealed remarkably different genomic variation patterns in neoadjuvant chemotherapy treated samples and pre-treated samples. We show that FLCNA is a practical and powerful method in subclone identification and CNA detection with scDNA-seq data.
Language	English
Publishing date	2023-04-20
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.04.18.537346
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Article: OSCAA: A Two-Dimensional Gaussian Mixture Model for Copy Number Variation Association Analysis.

Yu, Xuanxuan / Luo, Xizhi / Cai, Guoshuai / Xiao, Feifei

bioRxiv : the preprint server for biology

2023

Abstract: Copy number variants (CNVs) are prevalent in the human genome which provide profound effect on genomic organization and human diseases. Discovering disease associated CNVs is critical for understanding the pathogenesis of diseases and aiding their ... ...

Abstract	Copy number variants (CNVs) are prevalent in the human genome which provide profound effect on genomic organization and human diseases. Discovering disease associated CNVs is critical for understanding the pathogenesis of diseases and aiding their diagnosis and treatment. However, traditional methods for assessing the association between CNVs and disease risks adopt a two-stage strategy conducting quantitative CNV measurements first and then testing for association, which may lead to biased association estimation and low statistical power, serving as a major barrier in routine genome wide assessment of such variation. In this article, we developed OSCAA, a flexible algorithm to discover disease associated CNVs for both quantitative and qualitative traits. OSCAA employs a two-dimensional Gaussian mixture model that is built upon the principal components from copy number intensities, accounting for technical biases in CNV detection while simultaneously testing for their effect on outcome traits. In OSCAA, CNVs are identified and their associations with disease risk are evaluated simultaneously in a single step, taking into account the uncertainty of CNV identification in the statistical model. Our simulations demonstrated that OSCAA outperformed the existing one-stage method and traditional two-stage methods by yielding a more accurate estimate of the CNV-disease association, especially for short CNVs or CNVs with weak signal. In conclusion, OSCAA is a powerful and flexible approach for CNV association testing with high sensitivity and specificity, which can be easily applied to different traits and clinical risk predictions.
Language	English
Publishing date	2023-09-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.09.25.559392
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Article ; Online: Bulk and single-cell transcriptomics identify tobacco-use disparity in lung gene expression of ACE2, the receptor of 2019-nCov

Cai, Guoshuai

Abstract: In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In ... ...

Abstract	In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed five large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn't find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker's lung compared to non-smoker's lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.
Keywords	covid19
Publisher	MedRxiv
Document type	Article ; Online
DOI	10.1101/2020.02.05.20020107
Database	COVID19

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Article ; Online: Bulk and single-cell transcriptomics identify tobacco-use disparity in lung gene expression of ACE2, the receptor of 2019-nCov

Cai, Guoshuai

medRxiv

Abstract	In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed five large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn9t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker9s lung compared to non-smoker9s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.
Keywords	covid19
Language	English
Publishing date	2020-02-28
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.02.05.20020107
Database	COVID19

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Article ; Online: Correction to: A pilot study that provides evidence of epigenetic changes among mother-child pairs living proximal to mining in the US.

Cai, Guoshuai / Yu, Xuanxuan / Hutchins, David / McDermott, Suzanne

Environmental geochemistry and health

2022 Volume 44, Issue 12, Page(s) 4747

Language	English
Publishing date	2022-03-17
Publishing country	Netherlands
Document type	Published Erratum
ZDB-ID	52039-1
ISSN	1573-2983 ; 0142-7245 ; 0269-4042
ISSN (online)	1573-2983
ISSN	0142-7245 ; 0269-4042
DOI	10.1007/s10653-022-01248-2
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Article: The Effect of Different Diluents and Curing Agents on the Performance of Epoxy Resin-Based Intumescent Flame-Retardant Coatings.

Yang, Xukun / Wan, Yange / Yang, Nan / Hou, Yilin / Chen, Dantong / Liu, Jiachen / Cai, Guoshuai / Wang, Mingchao

Materials (Basel, Switzerland)

2024 Volume 17, Issue 2

Abstract: The epoxy resin-based (ESB) intumescent flame-retardant coatings were modified with 1,4-butanediol diglycidyl ether (14BDDE) and butyl glycidyl ether (BGE) as diluents and T403 and 4,4'-diaminodiphenylmethane (DDM) as curing agents, respectively. The ... ...

Abstract	The epoxy resin-based (ESB) intumescent flame-retardant coatings were modified with 1,4-butanediol diglycidyl ether (14BDDE) and butyl glycidyl ether (BGE) as diluents and T403 and 4,4'-diaminodiphenylmethane (DDM) as curing agents, respectively. The effects of different diluents and curing agents on the flame-retardant and mechanical properties, as well as the composition evolution of the coatings, were investigated by using large-plate combustion, the limiting oxygen index (LOI), vertical combustion, a cone calorimeter, X-ray diffraction, FTIR analysis, a N
Language	English
Publishing date	2024-01-10
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2487261-1
ISSN	1996-1944
ISSN	1996-1944
DOI	10.3390/ma17020348
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Article ; Online: BISC: accurate inference of transcriptional bursting kinetics from single-cell transcriptomic data.

Luo, Xizhi / Qin, Fei / Xiao, Feifei / Cai, Guoshuai

Briefings in bioinformatics

2022 Volume 23, Issue 6

Abstract: Gene expression in mammalian cells is inherently stochastic and mRNAs are synthesized in discrete bursts. Single-cell transcriptomics provides an unprecedented opportunity to explore the transcriptome-wide kinetics of transcriptional bursting. However, ... ...

Abstract	Gene expression in mammalian cells is inherently stochastic and mRNAs are synthesized in discrete bursts. Single-cell transcriptomics provides an unprecedented opportunity to explore the transcriptome-wide kinetics of transcriptional bursting. However, current analysis methods provide limited accuracy in bursting inference due to substantial noise inherent to single-cell transcriptomic data. In this study, we developed BISC, a Bayesian method for inferring bursting parameters from single cell transcriptomic data. Based on a beta-gamma-Poisson model, BISC modeled the mean-variance dependency to achieve accurate estimation of bursting parameters from noisy data. Evaluation based on both simulation and real intron sequential RNA fluorescence in situ hybridization data showed improved accuracy and reliability of BISC over existing methods, especially for genes with low expression values. Further application of BISC found bursting frequency but not bursting size was strongly associated with gene expression regulation. Moreover, our analysis provided new mechanistic insights into the functional role of enhancer and superenhancer by modulating both bursting frequency and size. BISC also formulated a downstream framework to identify differential bursting (in frequency and size separately) genes in samples under different conditions. Applying to multiple datasets (a mouse embryonic cell and fibroblast dataset, a human immune cell dataset and a human pancreatic cell dataset), BISC identified known cell-type signature genes that were missed by differential expression analysis, providing additional insights in understanding the cell-specific stochastic gene transcription. Applying to datasets of human lung and colon cancers, BISC successfully detected tumor signature genes based on alterations in bursting kinetics, which illustrates its value in understanding disease development regarding transcriptional bursting. Collectively, BISC provides a new tool for accurately inferring bursting kinetics and detecting differential bursting genes. This study also produced new insights in the role of transcriptional bursting in regulating gene expression, cell identity and tumor progression.
MeSH term(s)	Animals ; Humans ; Mice ; Transcriptome ; In Situ Hybridization, Fluorescence ; Reproducibility of Results ; Bayes Theorem ; Kinetics ; Neoplasms ; Transcription, Genetic ; Mammals/genetics
Language	English
Publishing date	2022-11-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbac464
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Article: Editorial: High-Throughput Sequencing-Based Investigation of Chronic Disease Markers and Mechanisms.

Orlov, Yuriy L / Chen, Wen-Lian / Sekacheva, Marina I / Cai, Guoshuai / Li, Hua

Frontiers in genetics

2022 Volume 13, Page(s) 922206

Language	English
Publishing date	2022-06-21
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2022.922206
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