RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data.

ABSTRACT:

Motivation

Data normalization is an important step in processing proteomics data generated in mass spectrometry experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity.

Results

To robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers an

SUBMITTER: Wang M 

PROVIDER: S-EPMC8098025 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature