東華大學圖書館 |

Statistical Methods for RNA-Seq and DNA Methylation Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Methods for RNA-Seq and DNA Methylation Data./
作者:	Ren, Xu.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	92 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Contained By:	Dissertations Abstracts International82-02B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27963966
ISBN:	9798662418448

Statistical Methods for RNA-Seq and DNA Methylation Data.
Ren, Xu.

Statistical Methods for RNA-Seq and DNA Methylation Data. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 92 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2020.

This item must not be sold to any third party vendors.

Genomics is an evolving area which focused on describing the structures and functions of an organism's genome. In recent years, rapid advancements in biotechnology have enabled the high-throughput sequencing (HTS) to become the current state-of-the-art method for characterizing gene functions. In particular, the RNA-Seq experiments and DNA methylation assays have been widely used to identify differentially expressed genes and differentially methylated regions, respectively. One important downstream analysis following differential expression analysis is the gene set testing which is used to relate significant genes or regions to known biological properties. Besides differential expression analysis and gene set testing, RNA-Seq data can also be used in the biological age calculation. This thesis focuses on nonlinear models for differential expression analysis and addresses gene length bias issue in gene set testing, as well as biological age calculation based on RNA-Seq data.In the first project, we reviewed the existing length bias correction methods for gene set testing in RNA-Seq and DNA methylation data. We then proposed a method named methylGSA for gene set testing in DNA methylation data which accounts for differences in gene length. The principle of our method can be divided into two complementary approaches (1) to apply p-value pooling to combine several p-values for each gene, and (2) to apply logistic regression model by incorporating number of probes as a covariate. Extensive simulations were performed to compare our method with existing methods, and the result showed our method yields better operating characteristics.In the second project, we introduced NBAMSeq for detecting nonlinear association in differential expression analysis. NBAMSeq is a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. Specifically, we modeled the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance was estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. Based on extensive simulations and case studies of RNA-Seq data, we showed that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. NBAMSeq is available from the Bioconductor project at http://bioconductor.org/packages/release/bioc/html/NBAMSeq.html.In the third project, we introduced RNAAgeCalc, a versatile across-tissue and tissue-specific transcriptional age calculator based on RNA-Seq data. By performing a meta-analysis of transcriptional age signature across multi-tissues using the GTEx database, we identified 1,616 common age-related genes, as well as tissue-specific age-related genes. We showed that our 1,616 common age-related genes outperformed other prior age related gene signatures in transcriptional age prediction. Our results also indicated that both racial and tissue differences were associated with transcriptional age. Furthermore, we demonstrated that the transcriptional age acceleration computed from our within-tissue predictor was significantly correlated with mutation burden, mortality risk and cancer stage in several types of cancer from the TCGA database, and offered complementary information to DNA methylation age. RNAAgeCalc is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#RNAAgeCalc, both as Bioconductor and Python packages, accompanied by a user-friendly interactive Shiny app.

ISBN: 9798662418448Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

RNA-Seq

Statistical Methods for RNA-Seq and DNA Methylation Data.
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