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Harnessing the Power of MicroRNA Cargoes in Small Extracellular Vesicles Released from Fresh-Frozen Human Brain Sections
Published on: November 8, 2024
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Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs.
Yuto Takemoto1, Daisuke Ito2, Shota Komori2
1Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko-Minami, Higashi-Ku, Nagoya, 461-8673, Japan.
BMC Bioinformatics
|June 20, 2024
Summary
Choosing the right preprocessing pipeline is crucial for accurate extracellular vesicle-derived miRNA analysis using 3D-Gene microarrays. Effective batch effect correction and missing value imputation, like missForest, improve data reliability for biomarker discovery.
Area of Science:
- Biomarker Discovery
- Genomics
- Bioinformatics
Background:
- Extracellular vesicle-derived microRNAs (EV-miRNAs) show promise as disease biomarkers.
- MicroRNA (miRNA) microarrays are vital for quantifying circulating EV-miRNA levels.
- Preprocessing miRNA microarray data is critical for accuracy, but its impact on 3D-Gene chips is understudied.
Purpose of the Study:
- To evaluate batch effects, missing value imputation accuracy, and preprocessing influences on 3D-Gene microarray data.
- To compare 18 different preprocessing pipelines for EV-miRNA data.
- To assess preprocessing strategies for amyotrophic lateral sclerosis (ALS) cohorts.
Main Methods:
- Utilized 18 distinct preprocessing pipelines involving missing value imputation and normalization.
- Applied ComBat method for batch effect correction.
- Compared imputation methods including missForest and constant value imputation.
Main Results:
- ComBat effectively suppressed batch effects across all pipelines.
- Pipelines using missForest for imputation demonstrated high agreement with measured values.
- Imputation with constant values showed poor agreement with measured data.
Conclusions:
- The selection of appropriate preprocessing strategies is vital for EV-miRNA microarray data analyzed with 3D-Gene technology.
- Validating preprocessing approaches, especially for batch effect correction and imputation, is essential for reliable biomarker discovery and disease research.
