scholarly journals Statistical tests for identifying differentially expressed genes in time-course microarray experiments

2003 ◽  
Vol 19 (6) ◽  
pp. 694-703 ◽  
Author(s):  
T. Park ◽  
S.-G. Yi ◽  
S. Lee ◽  
S. Y. Lee ◽  
D.-H. Yoo ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Time Course ◽  
Statistical Tests ◽  
Differentially Expressed ◽  
Microarray Experiments

scholarly journals RNA-Seq Transcriptome Analysis of Peripheral Blood From Cattle Infected With Mycobacterium bovis Across an Experimental Time Course

2021 ◽  
Vol 8 ◽  
Author(s):  
Kirsten E. McLoughlin ◽  
Carolina N. Correia ◽  
John A. Browne ◽  
David A. Magee ◽  
Nicolas C. Nalpas ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Mycobacterium Bovis ◽  
Peripheral Blood ◽  
Post Infection ◽  
Time Course ◽  
De Novo ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Infection Time ◽  
Time Point

Bovine tuberculosis, caused by infection with members of the Mycobacterium tuberculosis complex, particularly Mycobacterium bovis, is a major endemic disease affecting cattle populations worldwide, despite the implementation of stringent surveillance and control programs in many countries. The development of high-throughput functional genomics technologies, including RNA sequencing, has enabled detailed analysis of the host transcriptome to M. bovis infection, particularly at the macrophage and peripheral blood level. In the present study, we have analysed the transcriptome of bovine whole peripheral blood samples collected at −1 week pre-infection and +1, +2, +6, +10, and +12 weeks post-infection time points. Differentially expressed genes were catalogued and evaluated at each post-infection time point relative to the −1 week pre-infection time point and used for the identification of putative candidate host transcriptional biomarkers for M. bovis infection. Differentially expressed gene sets were also used for examination of cellular pathways associated with the host response to M. bovis infection, construction of de novo gene interaction networks enriched for host differentially expressed genes, and time-series analyses to identify functionally important groups of genes displaying similar patterns of expression across the infection time course. A notable outcome of these analyses was identification of a 19-gene transcriptional biosignature of infection consisting of genes increased in expression across the time course from +1 week to +12 weeks post-infection.

scholarly journals A Method Based on Differential Entropy-Like Function for Detecting Differentially Expressed Genes Across Multiple Conditions in RNA-Seq Studies

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 242
Author(s):  
Zhuo Wang ◽  
Shuilin Jin ◽  
Chiping Zhang
Keyword(s):  
Differentially Expressed Genes ◽  
Time Course ◽  
Real Data ◽  
Differentially Expressed ◽  
Differential Entropy ◽  
Biologically Relevant ◽  
Sample Data ◽  
Time Course Data ◽  
Differential Expressed Genes ◽  
Multiple Conditions

The advancement of high-throughput RNA sequencing has uncovered the profound truth in biology, ranging from the study of differential expressed genes to the identification of different genomic phenotype across multiple conditions. However, lack of biological replicates and low expressed data are still obstacles to measuring differentially expressed genes effectively. We present an algorithm based on differential entropy-like function (DEF) to test for the differential expression across time-course data or multi-sample data with few biological replicates. Compared with limma, edgeR, DESeq2, and baySeq, DEF maintains equivalent or better performance on the real data of two conditions. Moreover, DEF is well suited for predicting the genes that show the greatest differences across multiple conditions such as time-course data and identifies various biologically relevant genes.

scholarly journals A New Test Statistic Based on Shrunken Sample Variance for Identifying Differentially Expressed Genes in Small Microarray Experiments

2008 ◽  
Vol 2 ◽  
pp. BBI.S473 ◽  
Author(s):  
Akihiro Hirakawa ◽  
Yasunori Sato ◽  
Chikuma Hamada ◽  
Isao Yoshimura
Keyword(s):  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Simulation Studies ◽  
Test Statistic ◽  
Microarray Experiments ◽  
Cancer Data ◽  
False Discovery ◽  
The Mean ◽  
Sample Variances ◽  
Better Than

Choosing an appropriate statistic and precisely evaluating the false discovery rate (FDR) are both essential for devising an effective method for identifying differentially expressed genes in microarray data. The t-type score proposed by Pan et al. (2003) succeeded in suppressing false positives by controlling the underestimation of variance but left the overestimation uncontrolled. For controlling the overestimation, we devised a new test statistic (variance stabilized t-type score) by placing shrunken sample variances of the James-Stein type in the denominator of the t-type score. Since the relative superiority of the mean and median FDRs was unclear in the widely adopted Significance Analysis of Microarrays (SAM), we conducted simulation studies to examine the performance of the variance stabilized t-type score and the characteristics of the two FDRs. The variance stabilized t-type score was generally better than or at least as good as the t-type score, irrespective of the sample size and proportion of differentially expressed genes. In terms of accuracy, the median FDR was superior to the mean FDR when the proportion of differentially expressed genes was large. The variance stabilized t-type score with the median FDR was applied to actual colorectal cancer data and yielded a reasonable result.

scholarly journals Bayesian analysis of fMRI data and RNA-Seq time course experiment data

2015 ◽  
Author(s):  
◽  
Yuan Cheng
Keyword(s):  
Model Selection ◽  
Bayesian Analysis ◽  
Differentially Expressed Genes ◽  
Time Course ◽  
Selection Procedure ◽  
Principal Component ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Hemodynamic Response Function ◽  
Model Selection Procedure

The present dissertation contains two parts. In the first part, we develop a new Bayesian analysis of functional MRI data. We propose a novel triple gamma Hemodynamic Response Function (HRF) including the component to describe the initial dip. We use HRF to inform voxel-wise neuronal activities. Then we devise a new model selection procedure with a nonlocal pMOM prior for joint detection of neuronal activation and estimation of HRF, in order to time the activation time difference between visual and motor areas in the brain. In the second part, we develop a new Bayesian analysis of RNA-Seq Time Course experiments data. We propose to use Bayesian Principal Component regression model and based on that, devise a model selection procedure by using nonlocal piMOM prior in order to identify differentially expressed genes. Most current existing methods for RNA-Seq Time Course experiments data are from static view of point and cannot predict temporal patterns. Our method estimate the posterior differentially expressed probability for each gene by borrowing information across all subjects. Use of nonlocal prior in the model selection procedure reduces false discovered differentially expressed genes.

Bayesian analysis for detecting differentially expressed genes from RNA-seq data

2014 ◽  
Author(s):  
◽  
Shiqi Cui
Keyword(s):  
Differentially Expressed Genes ◽  
Time Course ◽  
Markov Models ◽  
Single Gene ◽  
Principal Component ◽  
Integrated Approach ◽  
Differentially Expressed ◽  
Monte Carlo Algorithm ◽  
Rna Seq ◽  
Paired Data

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This dissertation introduces hmmSeq, a model-based hierarchical Bayesian technique for detecting differentially expressed genes from RNA-seq data. Our novel hmmSeq methodology uses hidden Markov models to account for potential co-expression of neighboring genes. In addition, hmmSeq employs an integrated approach to studies with technical or biological replicates, automatically adjusting for any extra-Poisson variability. Moreover, for cases when paired data are available, hmmSeq includes a paired structure between treatments that incorporates subject-specific effects. To perform parameter estimation for the hmmSeq model, we develop an efficient Markov chain Monte Carlo algorithm. Further, we develop a procedure for detection of differentially expressed genes that automatically controls false discovery rate. A simulation study shows that the hmmSeq methodology performs better than competitors in terms of receiver operating characteristic curves. Finally, the analyses of three publicly available RNA-Seq datasets demonstrate the power and flexibility of the hmmSeq methodology. This dissertation also introduces an empirical Bayesian approach to detect differentially expressed genes in time course RNA-seq experiments. The proposed Bayesian method identifies major variation in gene expression profile by Bayesian principal component regression. The expression data are normalized for each gene, and the high dimentionality of time course data is first reduced by principal component analysis. The proposed model assumes a mixture distribution of expression parameters for differentially and nondifferentially expressed genes, borrows strength by sharing same variance across multiple subjects for each single gene, as well as shares information across genes by assuming gene-wise probabilities of being differentially expressed from the common beta prior distribution.

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