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Activity Number: 86 - New Topics and Methodological Developments for Single-Cell Data Science
Type: Invited
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #320509
Title: A Distribution-Free Independence Test for High-Dimensional Data
Author(s): Jing Lei* and Zhanrui Cai and Kathryn Roeder
Companies: Carnegie Mellon University and Carnegie Mellon University and Carnegie Mellon University
Keywords: independence test; sample splitting; single cell sequencing; permutation
Abstract:

Test of independence is of fundamental importance in modern data analysis, with broad applications in variable selection, graphical models, and causal inference. When the data is high dimensional and the potential dependence signal is sparse, independence testing becomes very challenging without distributional or structural assumptions. We propose a general framework for independence testing by first fitting a classifier that distinguishes the joint and product distributions, and then testing the significance of the fitted classifier. This framework allows us to borrow the strength of the most advanced classification algorithms developed from the modern machine learning community, making it applicable to high dimensional, complex data. We apply the new test to a single cell data set to test the independence between two types of single cell sequencing measurements, whose high dimensionality and sparsity make existing methods hard to apply.


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