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Activity Number:
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310
- SPEED:Statistical Methods for GWAs, Genetics, Genomics, and Other Omics Studies, Part 2
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Type:
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Contributed
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Date/Time:
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Tuesday, July 30, 2019 : 9:25 AM to 10:10 AM
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Sponsor:
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Biometrics Section
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Abstract #307698
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Title:
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A New Sparse Network Model for High-Throughput Count Data
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Author(s):
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Caesar (Zexuan) Li* and Gang Li and Eric Kawaguchi
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Companies:
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University of California, Los Angeles and UCLA and UCLA Department of Biostatistics
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Keywords:
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Graphical models;
Poisson graphical models;
next generation sequencing data;
regularization selection
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Abstract:
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When using high-throughput sequencing technologies to measure gene expression, researchers are often interested in constructing a sparse network model. One popular approach, Poisson Graphical LASSO (Allen 2013), is implemented by fitting L1 penalized regression model. However, it is well known that L0 penalized regression produces a more parsimonious and accurate model, compared to L1 penalized methods. In this research we developed a new L0 based Poisson graphical model, using cyclic coordinate-wise broken adaptive ridge regression. Performance of the model is evaluated and compared with some existing methods on both simulated and real data.
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Authors who are presenting talks have a * after their name.
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