Journal
BIOINFORMATICS
Volume 37, Issue 6, Pages 815-821Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btaa904
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Funding
- NIGMS [GM073059, R35GM127063]
- NIH CEGS [2RM1HG00773506]
- NIH eGTEx grant [U01HG007611]
- , NIH GTEx grant [5U01HL13104203]
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The study focused on improving normalization methods in highly heterogeneous samples, developing a novel data-driven approach to correct systematic bias while maintaining biological heterogeneity. The results showed that this method achieved the greatest reduction in bias while preserving variation across samples.
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 and extended the one-dimensional robust fitting method described in the previous work to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm. In simulation studies and analysis of real data from the genotype-tissue expression project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples.
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