GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis.

ABSTRACT:

Motivation

Missingness in label-free mass spectrometry is inherent to the technology. A computational approach to recover missing values in metabolomics and proteomics datasets is important. Most existing methods are designed under a particular assumption, either missing at random or under the detection limit. If the missing pattern deviates from the assumption, it may lead to biased results. Hence, we investigate the missing patterns in free mass spectrometry data and develop an omnibus approach GMSimpute, to allow effective imputation accommodating different missing patterns.

Results

Three proteomics datasets and one metabolomics dataset indicate missing values could be a mixture of abundance-dependent and abundance-independent missingness. We assess the performance of GMS

SUBMITTER: Li Q 

PROVIDER: S-EPMC6956786 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature