HarmonizR: blocking and singular feature data adjustment improve runtime efficiency and data preservation.

ABSTRACT:

Background

Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell) RNA, proteomics and other omics data. Despite its benefits, data integration introduces internal biases-so-called batch effects. Due to the inherent presence of missing values by such methods and their additional introduction by means of data integration, renowned algorithms such as ComBat and limma are unable to perform batch effect adjustment. Recently, the HarmonizR framework was presented for these cases, which is a tool for missing value tolerant data adjustment.

Results

In this contribution, we provide significant improvements to the HarmonizR approach. A novel blocking strategy is introduced to severe

SUBMITTER: Schlumbohm S 

PROVIDER: S-EPMC11817103 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature