A Simulation Based Evaluation of the Bootstrap Bias Corrected Percentile Interval Estimators of the Local False Discovery Rates
This work was partially supported by the Canada Foundation for Innovation, by the Ministry of Research and Innovation of Ontario, and by the Faculty of Medicine of the University of Ottawa. I would like to thank Prof David Bickel, at the Department of Biochemistry, Microbiology and Immunology, Facul...
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ftstatunivnysoar:oai:soar.suny.edu:20.500.12648/2603 2023-05-15T17:22:55+02:00 A Simulation Based Evaluation of the Bootstrap Bias Corrected Percentile Interval Estimators of the Local False Discovery Rates Zaihra, Tasneem The College at Brockport 2017-07-01 https://hdl.handle.net/20.500.12648/2603 unknown http://hdl.handle.net/20.500.12648/2603 Technical Report published Bootstrap Bias Corrected Percentile Interval Estimator False Discovery Rates Local False Discovery Rates Type I Error Microarray Data article 2017 ftstatunivnysoar https://doi.org/20.500.12648/2603 2022-09-14T05:54:58Z This work was partially supported by the Canada Foundation for Innovation, by the Ministry of Research and Innovation of Ontario, and by the Faculty of Medicine of the University of Ottawa. I would like to thank Prof David Bickel, at the Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa for his guidance throughout the pilot study and for the permission to use the R functions developed by him. I would also like to thank Corey Yanofsky, for the useful discussions related to the simulation study, while I was getting trained at Dr.Bickel’s Lab. Furthermore, the computational resources were provided by ACENET, the regional advanced research computing consortium for post-secondary institutions in Atlantic Canada. ACENET is funded by the Canada Foundation for Innovation (CFI), the Atlantic Canada Opportunities Agency (ACOA), and the provinces of Newfoundland and Labrador, Nova Scotia, and New Brunswick. Large scale data, such as the one collected in microarray, proteomics, MRI imaging, and massive social science surveys etc., often requires simultaneous consideration of hundreds or thousands of hypothesis tests, which leads to inflated type I error rate. A popular way to account for it is to use local false discovery rates (LFDR), which is the probability that a gene is truly not differentially expressed given the observed test statistic. The purpose of this report is to evaluate the Bootstrap Bias Corrected Percentile (BBCP) method proposed by Shao and Tu (1995) for estimating the lower bound for the LFDR. The method didn’t perform as expected. The overall coverage probability for null genes as well as non null genes was far from nominal coverage level of 50%. SUNY Brockport Mathematics Faculty Publications Article in Journal/Newspaper Newfoundland SUNY Open Access Repository (SOAR - State University of New York) Canada Corey ENVELOPE(-145.133,-145.133,-76.667,-76.667) Newfoundland |
institution |
Open Polar |
collection |
SUNY Open Access Repository (SOAR - State University of New York) |
op_collection_id |
ftstatunivnysoar |
language |
unknown |
topic |
Bootstrap Bias Corrected Percentile Interval Estimator False Discovery Rates Local False Discovery Rates Type I Error Microarray Data |
spellingShingle |
Bootstrap Bias Corrected Percentile Interval Estimator False Discovery Rates Local False Discovery Rates Type I Error Microarray Data Zaihra, Tasneem A Simulation Based Evaluation of the Bootstrap Bias Corrected Percentile Interval Estimators of the Local False Discovery Rates |
topic_facet |
Bootstrap Bias Corrected Percentile Interval Estimator False Discovery Rates Local False Discovery Rates Type I Error Microarray Data |
description |
This work was partially supported by the Canada Foundation for Innovation, by the Ministry of Research and Innovation of Ontario, and by the Faculty of Medicine of the University of Ottawa. I would like to thank Prof David Bickel, at the Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa for his guidance throughout the pilot study and for the permission to use the R functions developed by him. I would also like to thank Corey Yanofsky, for the useful discussions related to the simulation study, while I was getting trained at Dr.Bickel’s Lab. Furthermore, the computational resources were provided by ACENET, the regional advanced research computing consortium for post-secondary institutions in Atlantic Canada. ACENET is funded by the Canada Foundation for Innovation (CFI), the Atlantic Canada Opportunities Agency (ACOA), and the provinces of Newfoundland and Labrador, Nova Scotia, and New Brunswick. Large scale data, such as the one collected in microarray, proteomics, MRI imaging, and massive social science surveys etc., often requires simultaneous consideration of hundreds or thousands of hypothesis tests, which leads to inflated type I error rate. A popular way to account for it is to use local false discovery rates (LFDR), which is the probability that a gene is truly not differentially expressed given the observed test statistic. The purpose of this report is to evaluate the Bootstrap Bias Corrected Percentile (BBCP) method proposed by Shao and Tu (1995) for estimating the lower bound for the LFDR. The method didn’t perform as expected. The overall coverage probability for null genes as well as non null genes was far from nominal coverage level of 50%. SUNY Brockport Mathematics Faculty Publications |
author2 |
The College at Brockport |
format |
Article in Journal/Newspaper |
author |
Zaihra, Tasneem |
author_facet |
Zaihra, Tasneem |
author_sort |
Zaihra, Tasneem |
title |
A Simulation Based Evaluation of the Bootstrap Bias Corrected Percentile Interval Estimators of the Local False Discovery Rates |
title_short |
A Simulation Based Evaluation of the Bootstrap Bias Corrected Percentile Interval Estimators of the Local False Discovery Rates |
title_full |
A Simulation Based Evaluation of the Bootstrap Bias Corrected Percentile Interval Estimators of the Local False Discovery Rates |
title_fullStr |
A Simulation Based Evaluation of the Bootstrap Bias Corrected Percentile Interval Estimators of the Local False Discovery Rates |
title_full_unstemmed |
A Simulation Based Evaluation of the Bootstrap Bias Corrected Percentile Interval Estimators of the Local False Discovery Rates |
title_sort |
simulation based evaluation of the bootstrap bias corrected percentile interval estimators of the local false discovery rates |
publishDate |
2017 |
url |
https://hdl.handle.net/20.500.12648/2603 |
long_lat |
ENVELOPE(-145.133,-145.133,-76.667,-76.667) |
geographic |
Canada Corey Newfoundland |
geographic_facet |
Canada Corey Newfoundland |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_source |
Technical Report published |
op_relation |
http://hdl.handle.net/20.500.12648/2603 |
op_doi |
https://doi.org/20.500.12648/2603 |
_version_ |
1766109837722124288 |