10.5281/zenodo.1466019
https://zenodo.org/records/1466019
oai:zenodo.org:1466019
Cai, Weixin
Weixin
Cai
0000-0003-2680-3066
UC Berkeley
Hejazi, Nima
Nima
Hejazi
0000-0002-7127-2789
UC Berkeley
Hubbard, Alan
Alan
Hubbard
0000-0002-3769-0127
UC Berkeley
adaptest: JOSS Publication
Zenodo
2018
differential expression
multiple testing,
machine learning
targeted learning
variable importance
genomics
computational biology
2018-10-18
10.5281/zenodo.1466018
v1.1.0
GNU General Public License v2.0 only
Data-adaptive test statistics represent a general methodology for performing multiple hypothesis testing on effects sizes while maintaining honest statistical inference when operating in high-dimensional settings. The utilities provided here extend the use of this general methodology to many common data analytic challenges that arise in modern computational and genomic biology.