[Statlist] CANCELLED - Research Webinar in Statistics *FRIDAY 23 APRIL 2021* GSEM, University of Geneva

[gsem-support-instituts]

Dear All,

Due to unforeseen circumstances, this research webinar in statistics is cancelled. Dr. Erica PONZI's presentation is postponed to May 14.

Thank you for your understanding.

Best regards,


Organizers : E. Cantoni - S. Engelke - D. La Vecchia - E. Ronchetti S. Sperlich - F. Trojani - M.-P. Victoria-Feser


-----Message d'origine-----
De : gsem-support-instituts 
Envoyé : lundi, 19 avril 2021 12:17
Cc : Sebastian Engelke <Sebastian.Engelke using unige.ch>
Objet : Research Webinar in Statistics *FRIDAY 23 APRIL 2021* GSEM, University of Geneva
Importance : Haute

Dear All,

We are pleased to invite you to our next Research Webinar.

Looking forward to seeing you


Organizers : E. Cantoni - S. Engelke - D. La Vecchia - E. Ronchetti S. Sperlich - F. Trojani - M.-P. Victoria-Feser


FRIDAY 23 APRIL 2021 at 11:15am
ONLINE
Please join the Zoom research webinar: https://unige.zoom.us/j/92924332087?pwd=U1U1NFk4dTFCRHBMeWYrSDBQcXBiQT09
Meeting ID: 929 2433 2087
Passcode: 399192


Estimation and Use of Variance Contributions in Multiple High-Dimensional Data Sources Erica PONZI (https://www.med.uio.no/imb/english/?vrtx=person-view&uid=ericapo) - University of Oslo, Norway

ABSTRACT:
The simultaneous analysis of multiple sources of high-dimensional data is nowadays a major challenge in several research areas. In cancer genomics, data collected on several omic platforms provide information both in form of individual patterns within each data source and of joint patterns that are shared among the different sources. Capturing these two components of variation can help provide a broader understanding of cancer genetics. Several methods have been proposed to separate common and distinct components of variation in multiple data sources, based on different algorithms and frameworks. For instance, Joint and Individual Variation Explained (JIVE) [1] is based on an iterative algorithm to factorize the data matrix into low rank approximations that capture variation across and within data types. It has been widely used in integrative genomics, and several generalizations and improvements have been developed, such as the angle based JIVE (aJIVE) [2]. On the other hand, integrated PCA (iPCA) [3] is a model based generalization of principal components analysis that can be used in similar applications. We will describe several methods for data integration, especially focusing on the estimation of joint and individual variance components. We will present an application of such methods to a lung cancer case control study nested in the Norwegian Woman and Cancer (NOWAC) cohort study [4]. JIVE, aJIVE and iPCA are used to separate the joint and individual contributions of DNA methylation, miRNA and mRNA expression and to improve prediction models for the occurrence of lung cancer.

References

[1] Lock, E. F., Hoadley, K. A., Marron, J. S. and Nobel, A.B. (2013). Joint and Individual Variation Explained (JIVE) for integrated analysis of multiple data types. Annals of Applied Statistics, 7, 523 - 542.

[2] Feng, Q., Jiang, M., Hannig and J., Marron, J. S.(2018). Angle based joint and individual variation explained. Journal of Multivariate Analysis, 166, 241 - 265.

[3] Tang, T. M. and Allen, G. I. (2018). Integrated principal components analysis. arXiv, 1810.00832.

[4] Lund, E., Dumeaux, V., Braaten, T., Hjartåker, A.,Engeset, D., Skeie, G. and Kumle, M. (2008) Cohort profile: the Norwegian Women and Cancer Study-NOWAC-Kvinner og kreft. International Journal of Epidemiology,37, 36 - 41.


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