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EFFICIENT STATISTICAL SIGNIFICANCE APPROXIMATION FOR LOCAL
ASSOCIATION ANALYSIS OF HIGH-THROUGHPUT TIME SERIES DATA
by
Li Charlie Xia
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
MASTER OF SCIENCE
(STATISTICS)
August 2012
Copyright 2012 Li Charlie Xia
Object Description
| Title | Efficient statistical significance approximation for local association analysis of high-throughput time series data |
| Author | Xia, Li Charlie |
| Author email | lxia@usc.edu;lxia@usc.edu |
| Degree | Master of Science |
| Document type | Thesis |
| Degree program | Statistics |
| School | College of Letters, Arts And Sciences |
| Date defended/completed | 2012-08-03 |
| Date submitted | 2012-08-03 |
| Date approved | 2012-08-06 |
| Restricted until | 2012-08-06 |
| Date published | 2012-08-06 |
| Advisor (committee chair) | Sun, Fengzhu |
| Advisor (committee member) |
Schumitzky, Alan Zhang, Jianfeng |
| Abstract | Local association analysis, such as local similarity analysis and local shape analysis, of biological time series data helps elucidate the varying dynamics of biological systems. However, their applications to large scale high-throughput data are limited by slow permutation procedures for statistical significance evaluation. We developed a theoretical approach to approximate the statistical significance of local similarity and local shape analysis based on the approximate tail distribution of the maximum partial sum of independent identically distributed (i.i.d) and Markovian random variables. Simulations show that the derived formula approximates the tail distribution reasonably well (starting at time points > 10 with no delay and > 20 with delay) and provides p-values comparable to those from permutations. The new approach enables efficient calculation of statistical significance for pairwise local association analysis, making possible all-to-all association studies otherwise prohibitive. As a demonstration, local association analysis of human microbiome time series shows that core OTUs are highly synergetic and some of the associations are body-site specific across samples. The new approach is implemented in our eLSA package, which now provides pipelines for faster local similarity and shape analysis of time series data. The tool is freely available from eLSA's website: http://meta.usc.edu/softs/lsa. |
| Keyword | high-throughput data; local association; local shape analysis; local similarity analysis; microbial community; time-series data |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Rights | Xia, Li Charlie |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume4/etd-XiaLiCharl-1140.pdf |
Description
| Title | Page 1 |
| Full text | EFFICIENT STATISTICAL SIGNIFICANCE APPROXIMATION FOR LOCAL ASSOCIATION ANALYSIS OF HIGH-THROUGHPUT TIME SERIES DATA by Li Charlie Xia A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree MASTER OF SCIENCE (STATISTICS) August 2012 Copyright 2012 Li Charlie Xia |
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