Events

Thursday, October 4, 2018 — 4:00 PM EDT

Methods for High Dimensional Compositional Data Analysis in Microbiome Studies


Human microbiome studies using high throughput DNA sequencing generate  compositional data with the absolute abundances of microbes not recoverable from sequence data alone. In compositional data analysis, each sample consists of proportions of various organisms with a unit sum constraint. This simple feature can lead traditional statistical methods when naively applied to produce errant results and spurious associations. In addition, microbiome sequence data sets are typically high dimensional, with the number of taxa much greater than the number of samples. These important features require further development of methods for  analysis of high dimensional compositional data.  This talk presents several latest developments in this area, including methods for estimating the compositions based on sparse count data,  two-sample test for compositional vectors and  regression analysis with compositional covariates.  Several microbiome studies at the University of Pennsylvania are used to illustrate these methods and several open questions will be discussed.

Thursday, October 11, 2018 — 4:00 PM EDT

Probabilistic approaches to mine association rules


Mining association rules is an important and widely applied data mining technique for discovering patterns in large datasets. However, the used support-confidence framework has some often overlooked weaknesses. This talk introduces a simple stochastic model and shows how it can be used in association rule mining. We apply the model to simulate data for analyzing the behavior and shortcomings of confidence and other measures of interestingness (e.g., lift). Based on these findings, we develop a new model-driven approach to mine rules based on the notion of NB-frequent itemsets, and we define a measure of interestingness which controls for spurious rules and has a strong foundation in statistical testing theory.

Wednesday, October 17, 2018 — 4:00 PM EDT

Check back soon for more information!

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