Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation.

ABSTRACT: Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries. To demonstrate its utility and generalizability, we applied this pipeline to two use cases: a murine cardiac temporal proteomics data set and a human plasma temporal proteomics data set, both aimed at examining protein turnover rates. This DMI pipeline significantly enhanced the detection of

SUBMITTER: Yan Y 

PROVIDER: S-EPMC11385379 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature