tsenglab.biostat.pitt.edu

George C. Tseng

Currently we have particular interests on statistical and computational methods to combine information from multiple high-throughput omics data sets. These include two major directions: (1) horizontal meta-analysis. That combines multiple same-type genomic data (usually microarray, GWAS or eQTL) to increase statistical power, accuracy and validated conclusion (2) vertical integrative analysis. Our current and past research interests:. Statistical meta-analysis for combining multiple transcriptomic studies.

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George C. Tseng | tsenglab.biostat.pitt.edu Reviews

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Currently we have particular interests on statistical and computational methods to combine information from multiple high-throughput omics data sets. These include two major directions: (1) horizontal meta-analysis. That combines multiple same-type genomic data (usually microarray, GWAS or eQTL) to increase statistical power, accuracy and validated conclusion (2) vertical integrative analysis. Our current and past research interests:. Statistical meta-analysis for combining multiple transcriptomic studies.

<META> KEYWORDS

1 publications

2 software

3 presentatioins

4 people

5 group meetings

6 perspective students

7 genomics curriculum

8 computing resources

9 research interests

10 genomic meta analysis

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publications,software,presentatioins,people,group meetings,perspective students,genomics curriculum,computing resources,research interests,genomic meta analysis,metaqc,metade,rim minp,metapath,metadimr,metapca,metamds,metafda,metaclust,metapredict

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George C. Tseng | tsenglab.biostat.pitt.edu Reviews

https://tsenglab.biostat.pitt.edu

Currently we have particular interests on statistical and computational methods to combine information from multiple high-throughput omics data sets. These include two major directions: (1) horizontal meta-analysis. That combines multiple same-type genomic data (usually microarray, GWAS or eQTL) to increase statistical power, accuracy and validated conclusion (2) vertical integrative analysis. Our current and past research interests:. Statistical meta-analysis for combining multiple transcriptomic studies.

INTERNAL PAGES

tsenglab.biostat.pitt.edu

1

George C. Tseng

http://www.tsenglab.biostat.pitt.edu/index.htm

Currently we have particular interests on statistical and computational methods to combine information from multiple high-throughput omics data sets. These include two major directions: (1) horizontal meta-analysis. That combines multiple same-type genomic data (usually microarray, GWAS or eQTL) to increase statistical power, accuracy and validated conclusion (2) vertical integrative analysis. Our current and past research interests:. Statistical meta-analysis for combining multiple transcriptomic studies.

2

George C. Tseng

http://www.tsenglab.biostat.pitt.edu/publication.htm

Publications and Professional Activities. To show and hide (Also available at My Bibliography. Co-first author; *corresponding/senior author; students underlined. Bull; Zhiguang Huo. And George C. Tseng*. 2017) Integrative sparse K-means for disease subtype discovery using multi-level omics data. Annals of Applied Statistics. tentatively accepted. (R package at lab software page. Bull; Tianzhou Ma. Faming Liang and George C. Tseng*. Bull; Lin Wang. Shin-sheng Yuan, Yen-Yi Ho, and George C. Tseng*. A prel...

3

George C. Tseng

http://www.tsenglab.biostat.pitt.edu/perspectiveStudents.htm

Below information is for incoming or existing biostatistics students interested in doing PhD thesis in genomic research in our group. Perspective students not yet admitted to the department. Existing students in Biostatistics. We occasionally recruit high-performing existing students for GSR when funding allows. For interested students, it is recommended to take the genomic data analysis course sequence we offer every spring and fall. Career for Biostatistics PhD students with genomic research training:.

4

George C. Tseng

http://www.tsenglab.biostat.pitt.edu/presentation.htm

Invited Presentations (slides are password protected for internal use). Do mouse models mimic genomic response in human transcriptomic system? An answer from Bayesian perspective. Department of Biostatistics, Boston University. A joint Bayesian modeling for integrating microarray and RNA-seq transcriptomic data. Dahshu Data Science Symposium: Computational Precision Health 2017, San Francisco. A joint Bayesian modeling for integrating microarray and RNA-seq transcroptomic data. 16/05/9, 5/26, 6/17. Integ...

5

George C. Tseng

http://www.tsenglab.biostat.pitt.edu/software.htm

A suite of packages for microarray and genomic meta-analysis. Quality control to determin inclusion/exclusion of studies in meta-analysis. Detect DE (candidate marker) genes in meta-analysis. Detect associated pathways in meta-analysis. Meta-analytic framework to identify novel disease subtypes ( R package. Dimension reductioin by PCA, sparse PCA and robust PCA in meta-analysis ( R package. A meta-analyltic framework of top scoring pair (TSP) algorithm for classification algorithm. ( R package. An R pack...

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tsenglab.biostat.pitt.edu

George C. Tseng

Currently we have particular interests on statistical and computational methods to combine information from multiple high-throughput omics data sets. These include two major directions: (1) horizontal meta-analysis. That combines multiple same-type genomic data (usually microarray, GWAS or eQTL) to increase statistical power, accuracy and validated conclusion (2) vertical integrative analysis. Our current and past research interests:. Statistical meta-analysis for combining multiple transcriptomic studies.

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