fdrci: FDR confidence interval selection and adjustment for large-scale hypothesis testing.
Journal
Bioinformatics advances
ISSN: 2635-0041
Titre abrégé: Bioinform Adv
Pays: England
ID NLM: 9918282081306676
Informations de publication
Date de publication:
2022
2022
Historique:
received:
09
12
2021
revised:
05
06
2022
accepted:
10
06
2022
entrez:
24
6
2022
pubmed:
25
6
2022
medline:
25
6
2022
Statut:
epublish
Résumé
Approaches that control error by applying a priori fixed discovery thresholds such as 0.05 limit the ability of investigators to identify and publish weak effects even when evidence suggests that such effects exist. However, current false discovery rate (FDR) estimation methods lack a principled approach for We describe a flexible approach that hinges on the precision of a permutation-based FDR estimator. A series of discovery thresholds are proposed, and an FDR confidence interval selection and adjustment technique is used to identify intervals that do not cover one, implying that some discoveries are expected to be true. We report an application to a transcriptome-wide association study of the MAVERICC clinical trial involving patients with metastatic colorectal cancer. Several genes are identified whose predicted expression is associated with progression-free or overall survival. Software is provided via the CRAN repository (https://cran.r-project.org/web/packages/fdrci/index.html). Supplementary data are available at
Identifiants
pubmed: 35747247
doi: 10.1093/bioadv/vbac047
pii: vbac047
pmc: PMC9210923
doi:
Types de publication
Journal Article
Langues
eng
Pagination
vbac047Subventions
Organisme : NIA NIH HHS
ID : P01 AG055367
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL118455
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA014089
Pays : United States
Organisme : NCI NIH HHS
ID : P01 CA196569
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK110793
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD098161
Pays : United States
Informations de copyright
© The Author(s) 2022. Published by Oxford University Press.
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