Neither random nor censored: estimating intensity-dependent probabilities for missing values in label-free proteomics.

ABSTRACT:

Motivation

Mass spectrometry proteomics is a powerful tool in biomedical research but its usefulness is limited by the frequent occurrence of missing values in peptides that cannot be reliably quantified (detected) for particular samples. Many analysis strategies have been proposed for missing values where the discussion often focuses on distinguishing whether values are missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR).

Results

Statistical models and algorithms are proposed for estimating the detection probabilities and for evaluating how much statistical information can or cannot be recovered from the missing value pattern. The probability that an intensity is detected is shown to be accurately modeled as a logit-linear function o

SUBMITTER: Li M 

PROVIDER: S-EPMC10174703 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature