Fully Bayesian Mixture Model for Differential Gene Expression: Simulations and Model Checks

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Alex Lewin ◽  
Natalia Bochkina ◽  
Sylvia Richardson
Keyword(s):  
Mixture Model ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Component Mixture ◽  
Data Set ◽  
Knock Out ◽  
Bayesian Hierarchical ◽  
Mixture Prior ◽  
Parametric Families ◽  
Fully Bayesian

We present a Bayesian hierarchical model for detecting differentially expressed genes using a mixture prior on the parameters representing differential effects. We formulate an easily interpretable 3-component mixture to classify genes as over-expressed, under-expressed and non-differentially expressed, and model gene variances as exchangeable to allow for variability between genes. We show how the proportion of differentially expressed genes, and the mixture parameters, can be estimated in a fully Bayesian way, extending previous approaches where this proportion was fixed and empirically estimated. Good estimates of the false discovery rates are also obtained.Different parametric families for the mixture components can lead to quite different classifications of genes for a given data set. Using Affymetrix data from a knock out and wildtype mice experiment, we show how predictive model checks can be used to guide the choice between possible mixture priors. These checks show that extending the mixture model to allow extra variability around zero instead of the usual point mass null fits the data better.A software package for R is available.

scholarly journals Differential gene expression analysis of ankylosing spondylitis shows deregulation of the HLA-DRB, HLA-DQB, ITM2A, and CTLA4 genes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rowan AlEjielat ◽  
Anas Khaleel ◽  
Amneh H. Tarkhan
Keyword(s):  
Gene Expression ◽  
Ankylosing Spondylitis ◽  
Differentially Expressed Genes ◽  
Gene Network ◽  
Disease Diagnosis ◽  
Receptor Activation ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Inflammatory Disorder ◽  
Data Set

Abstract Background Ankylosing spondylitis (AS) is a rare inflammatory disorder affecting the spinal joints. Although we know some of the genetic factors that are associated with the disease, the molecular basis of this illness has not yet been fully elucidated, and the genes involved in AS pathogenesis have not been entirely identified. The current study aimed at constructing a gene network that may serve as an AS gene signature and biomarker, both of which will help in disease diagnosis and the identification of therapeutic targets. Previously published gene expression profiles of 16 AS patients and 16 gender- and age-matched controls that were profiled on the Illumina HumanHT-12 V3.0 Expression BeadChip platform were mined. Patients were Portuguese, 21 to 64 years old, were diagnosed based on the modified New York criteria, and had Bath Ankylosing Spondylitis Disease Activity Index scores > 4 and Bath Ankylosing Spondylitis Functional Index scores > 4. All patients were receiving only NSAIDs and/or sulphasalazine. Functional enrichment and pathway analysis were performed to create an interaction network of differentially expressed genes. Results ITM2A, ICOS, VSIG10L, CD59, TRAC, and CTLA-4 were among the significantly differentially expressed genes in AS, but the most significantly downregulated genes were the HLA-DRB6, HLA-DRB5, HLA-DRB4, HLA-DRB3, HLA-DRB1, HLA-DQB1, ITM2A, and CTLA-4 genes. The genes in this study were mostly associated with the regulation of the immune system processes, parts of cell membrane, and signaling related to T cell receptor and antigen receptor, in addition to some overlaps related to the IL2 STAT signaling, as well as the androgen response. The most significantly over-represented pathways in the data set were associated with the “RUNX1 and FOXP3 which control the development of regulatory T lymphocytes (Tregs)” and the “GABA receptor activation” pathways. Conclusions Comprehensive gene analysis of differentially expressed genes in AS reveals a significant gene network that is involved in a multitude of important immune and inflammatory pathways. These pathways and networks might serve as biomarkers for AS and can potentially help in diagnosing the disease and identifying future targets for treatment.

scholarly journals Bayesian Mixture Model Analysis for Detecting Differentially Expressed Genes

2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
Zhenyu Jia ◽  
Shizhong Xu
Keyword(s):  
Mixture Model ◽  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Control Treatment ◽  
Microarray Gene Expression ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Truncated Normal ◽  
Upregulated Genes

Control-treatment design is widely used in microarray gene expression experiments. The purpose of such a design is to detect genes that express differentially between the control and the treatment. Many statistical procedures have been developed to detect differentially expressed genes, but all have pros and cons and room is still open for improvement. In this study, we propose a Bayesian mixture model approach to classifying genes into one of three clusters, corresponding to clusters of downregulated, neutral, and upregulated genes, respectively. The Bayesian method is implemented via the Markov chain Monte Carlo (MCMC) algorithm. The cluster means of down- and upregulated genes are sampled from truncated normal distributions whereas the cluster mean of the neutral genes is set to zero. Using simulated data as well as data from a real microarray experiment, we demonstrate that the new method outperforms all methods commonly used in differential expression analysis.

Mixture Model to Assess the Extent of Cross-Transmission of Multidrug-Resistant Pathogens in Hospitals

2009 ◽  
Vol 30 (8) ◽  
pp. 730-736 ◽  
Author(s):  
Rafael T. Mikolajczyk ◽  
Göran Kauermann ◽  
Ulrich Sagel ◽  
Mirjam Kretzschmar
Keyword(s):  
Mixture Model ◽  
Tertiary Care ◽  
Multidrug Resistant ◽  
Poisson Processes ◽  
Care Hospital ◽  
Component Mixture ◽  
Data Set ◽  
Admission Process ◽  
Cross Transmission ◽  
Multidrug Resistant Pathogens

Objective.Creation of a mixture model based on Poisson processes for assessment of the extent of cross-transmission of multidrug-resistant pathogens in the hospital.Methods.We propose a 2-component mixture of Poisson processes to describe the time series of detected cases of colonization. The first component describes the admission process of patients with colonization, and the second describes the cross-transmission. The data set used to illustrate the method consists of the routinely collected records for methicillin-resistant Staphylococcus aureus (MRSA), imipenem-resistant Pseudomonas aeruginosa, and multidrug-resistant Acinetobacter baumannii over a period of 3 years in a German tertiary care hospital.Results.For MRSA and multidrug-resistant A. baumannii, cross-transmission was estimated to be responsible for more than 80% of cases; for imipenem-resistant P. aeruginosa, cross-transmission was estimated to be responsible for 59% of cases. For new cases observed within a window of less than 28 days for MRSA and multidrug-resistant A. baumannii or 40 days for imipenem-resistant P. aeruginosa, there was a 50% or greater probability that the cause was cross-transmission.Conclusions.The proposed method offers a solution to assessing of the extent of cross-transmission, which can be of clinical use. The method can be applied using freely available software (the package FlexMix in R) and it requires relatively little data.

Construction of a chromosome 17 transcriptome in serous ovarian cancer identifies differentially expressed genes

2008 ◽  
Vol 18 (5) ◽  
pp. 963-975 ◽  
Author(s):  
P. M. Wojnarowicz ◽  
A. Breznan ◽  
S. L. Arcand ◽  
A. Filali-Mouhim ◽  
D. M. Provencher ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Differentially Expressed Genes ◽  
Molecular Genetic ◽  
Primary Cultures ◽  
Differentially Expressed ◽  
Chromosome 17 ◽  
Data Set ◽  
Malignant Ovarian Tumor ◽  
Ovarian Surface Epithelial ◽  
Subtype A

Cytogenetic, molecular genetic, and functional analyses have implicated chromosome 17 genes in epithelial ovarian cancer (EOC). To further characterize the contribution of chromosome 17 genes in EOC, the Affymetrix U133A GeneChip was used to perform transcriptome analyses of 15 primary cultures of normal ovarian surface epithelial (NOSE) cells and 17 malignant ovarian tumor (TOV) samples of the serous histopathologic subtype. A two-way comparative analysis of 776 known genes and expressed sequences identified 253 genes that exhibited at least a threefold difference in expression in at least one TOV sample compared to the mean of NOSE samples. Within this data set, 99 of the 253 (39.1%) genes exhibited similar patterns of expression across all tested samples, suggesting a high degree of concordance in the chromosome 17 transcriptome. This observation was supported by hierarchical clustering analysis that segregated the TOV and NOSE samples into two separate groups. There were 77 genes that were differentially expressed in at least 50% of the TOV samples. Five genes (AdoRA2B at 17p12, CCL2 at 17q12, ACLY at 17q21.2, WIPI1 at 17q24.2, and SLC16A3 at 17q25.3) were significantly (P< 5.13E−11) differentially expressed at least threefold in all serous TOV samples, and all five genes were underexpressed in these TOV samples as compared to the NOSE samples. Interestingly, several of these differentially expressed genes have been previously associated with response to hypoxia.

scholarly journals The inducible response of the nematode Caenorhabditis elegans to members of its natural microbiome across development and adult life

2019 ◽  
Author(s):  
W Yang ◽  
C Petersen ◽  
B Pees ◽  
J Zimmermann ◽  
S Waschina ◽  
...  
Keyword(s):  
Energy Metabolism ◽  
Metabolic Network ◽  
Differentially Expressed Genes ◽  
Adult Life ◽  
Differentially Expressed ◽  
Promoter Regions ◽  
Data Set ◽  
C Elegans ◽  
Folate Biosynthesis ◽  
Transcriptome Response

AbstractThe biology of all organisms is influenced by the associated community of microorganisms. In spite of its importance, it is usually not well understood how exactly this microbiome affects host functions and what are the underlying molecular processes. To rectify this knowledge gap, we took advantage of the nematode C. elegans as a tractable, experimental model system and assessed the inducible transcriptome response after colonization with members of its native microbiome. For this study, we focused on two isolates of the genus Ochrobactrum. These bacteria are known to be abundant in the nematode’s microbiome and are capable of colonizing and persisting in the nematode gut, even under stressful conditions. The transcriptome response was assessed across development and three time points of adult life, using general and C. elegans-specific enrichment analyses to identify affected functions. Our assessment revealed an influence of the microbiome members on the nematode’s dietary response, development, fertility, immunity, and energy metabolism. This response is mainly regulated by a GATA transcription factor, most likely ELT-2, as indicated by the enrichment of (i) the GATA motif in the promoter regions of inducible genes and (ii) of ELT-2 targets among the differentially expressed genes. We compared our transcriptome results with a corresponding previously characterized proteome data set, highlighting a significant overlap in the differentially expressed genes and the affected functions. Our analysis further identified a core set of 86 genes that consistently responded to the microbiome members across development and adult life, including several C-type lectin-like genes and genes known to be involved in energy metabolism or fertility. We additionally assessed the consequences of induced gene expression with the help of metabolic network model analysis, using a previously established metabolic network for C. elegans. This analysis complemented the enrichment analyses by revealing an influence of the Ochrobactrum isolates on C. elegans energy metabolism and furthermore metabolism of specific amino acids, fatty acids, and also folate biosynthesis. Our findings highlight the multifaceted impact of naturally colonizing microbiome isolates on C. elegans life history and thereby provide a framework for further analysis of microbiome-mediated host functions.

scholarly journals Differential lncRNA/mRNA Expression Profiling and Functional Network Analyses in Bmp2 Deletion of Mouse Dental Papilla Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Feng Wang ◽  
Ran Tao ◽  
Li Zhao ◽  
Xin-Hui Hao ◽  
Yi Zou ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Knock Out ◽  
Protein Protein Interaction ◽  
Functional Studies ◽  
Network Analyses ◽  
Dental Papilla ◽  
Non Coding Rna ◽  
Mrna Expression Profiling ◽  
Dental Papilla Cells

Bmp2 is essential for dentin development and formation. Bmp2 conditional knock-out (KO) mice display a similar tooth phenotype of dentinogenesis imperfecta (DGI). To elucidate a foundation for subsequent functional studies of cross talk between mRNAs and lncRNAs in Bmp2-mediated dentinogenesis, we investigated the profiling of lncRNAs and mRNAs using immortalized mouse dental Bmp2 flox/flox (iBmp2fx/fx) and Bmp2 knock-out (iBmp2ko/ko) papilla cells. RNA sequencing was implemented to study the expression of the lncRNAs and mRNAs. Quantitative real-time PCR (RT-qPCR) was used to validate expressions of lncRNAs and mRNAs. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to predict functions of differentially expressed genes (DEGs). Protein–protein interaction (PPI) and lncRNA–mRNA co-expression network were analyzed by using bioinformatics methods. As a result, a total of 22 differentially expressed lncRNAs (16 downregulated vs 6 upregulated) and 227 differentially expressed mRNAs (133 downregulated vs. 94 upregulated) were identified in the iBmp2ko/ko cells compared with those of the iBmp2fx/fx cells. RT-qPCR results showed significantly differential expressions of several lncRNAs and mRNAs which were consistent with the RNA-seq data. GO and KEGG analyses showed differentially expressed genes were closely related to cell differentiation, transcriptional regulation, and developmentally relevant signaling pathways. Moreover, network-based bioinformatics analysis depicted the co-expression network between lncRNAs and mRNAs regulated by Bmp2 in mouse dental papilla cells and symmetrically analyzed the effect of Bmp2 during dentinogenesis via coding and non-coding RNA signaling.

scholarly journals Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells

2017 ◽  
Author(s):  
John D. Blischak ◽  
Ludovic Tailleux ◽  
Marsha Myrthil ◽  
Cécile Charlois ◽  
Emmanuel Bergot ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dendritic Cells ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Healthy Individuals ◽  
Data Set ◽  
Expression Levels ◽  
Infected Cells ◽  
Latent Tb ◽  
Gene Expression Levels

ABSTRACTTuberculosis (TB) is a deadly infectious disease, which kills millions of people every year. The causative pathogen, Mycobac-terium tuberculosis (MTB), is estimated to have infected up to a third of the world’s population; however, only approximately 10% of infected healthy individuals progress to active TB. Despite evidence for heritability, it is not currently possible to predict who may develop TB. To explore approaches to classify susceptibility to TB, we infected with MTB dendritic cells (DCs) from putatively resistant individuals diagnosed with latent TB, and from susceptible individuals that had recovered from active TB. We measured gene expression levels in infected and non-infected cells and found hundreds of differentially expressed genes between susceptible and resistant individuals in the non-infected cells. We further found that genetic polymorphisms nearby the differentially expressed genes between susceptible and resistant individuals are more likely to be associated with TB susceptibility in published GWAS data. Lastly, we trained a classifier based on the gene expression levels in the non-infected cells, and demonstrated decent performance on our data and an independent data set. Overall, our promising results from this small study suggest that training a classifier on a larger cohort may enable us to accurately predict TB susceptibility.

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