Abstract
With the advent of high-throughput sequencing, an efficient computing strategy is required to deal with large genomic data sets. The challenge of estimating a large precision matrix has garnered substantial research attention for its direct application to discriminant analyses and graphical models. Most existing methods either use a lasso-type penalty that may lead to biased estimators or are computationally intensive, which prevents their applications to very large graphs. We propose using an L0 penalty to estimate an ultra-large precision matrix (scalnetL0). We apply scalnetL0 to RNA-seq data from breast cancer patients represented in The Cancer Genome Atlas and find improved accuracy of classifications for survival times. The estimated precision matrix provides information about a large-scale co-expression network in breast cancer. Simulation studies demonstrate that scalnetL0 provides more accurate and efficient estimators, yielding shorter CPU time and less Frobenius loss on sparse learning for large-scale precision matrix estimation.
Original language | English (US) |
---|---|
Pages (from-to) | 18-30 |
Number of pages | 13 |
Journal | Statistical Analysis and Data Mining |
Volume | 14 |
Issue number | 1 |
DOIs | |
State | Published - Feb 2021 |
Keywords
- L penalty
- genomics
- network
- scalable
ASJC Scopus subject areas
- Analysis
- Information Systems
- Computer Science Applications
MD Anderson CCSG core facilities
- Biostatistics Resource Group