A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis.

ABSTRACT:

Background

Data errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation. Data errors need to be identified and corrected before integrative data analyses where different types of data are merged on the basis of the annotated labels. Data with labeling errors dampen true biological signals. More importantly, data analysis with sample errors could lead to wrong scientific conclusions. We developed a robust probabilistic multi-omics data matching procedure, proMODMatcher, to curate data and identify and correct data annotation and errors in large databases.

Results

Application to simulated datasets suggests that proMODMatcher achieved robust statistical power even when the number of cis-associations was small and/or

SUBMITTER: Lee E 

PROVIDER: S-EPMC6615984 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature