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Bioequivalence Data: Statistical Interpretation

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The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...

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Assessing the impact of batch effect associated missing values on downstream analysis in high-throughput biomedical

Harvard Wai Hann Hui¹, Wei Xin Chan^1,2, Wilson Wen Bin Goh^1,2,3,4,5

¹Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore.

Briefings in Bioinformatics

|April 15, 2025

Summary

Batch effect associated missing values (BEAMs) significantly impair data analysis by affecting missing value imputation and batch effect correction. Specific imputation methods like KNN, SVD, and RF are particularly vulnerable to BEAMs, leading to unreliable results.

Keywords:

batch effects biomedical informatics genomics missing values proteomics statistics

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Area of Science:

Biostatistics
Bioinformatics
Computational Biology

Background:

Batch effect associated missing values (BEAMs) arise from integrating multi-batch omics data with varying feature coverage.
BEAMs pose significant challenges for downstream data analysis, including missing value imputation (MVI) and batch effect correction (BE).

Purpose of the Study:

To investigate the impact of BEAMs on the performance of various MVI and BE correction algorithms (BECAs).
To evaluate the robustness of commonly used MVI methods when confronted with BEAMs in real-world and simulated datasets.

Main Methods:

Simulations and analysis of real-world datasets (e.g., CPTAC).
Evaluation of six MVI methods (KNN, Mean, MinProb, SVD, MICE, RF) alongside two BECAs (ComBat, limma).

Main Results:

BEAMs significantly degrade MVI performance, leading to inaccurate imputations and inflated P-values.
KNN, SVD, and RF methods propagated random signals, causing false statistical confidence.
Mean and MinProb imputations introduced artifacts, with detrimental effects increasing with BEAM severity.

Conclusions:

BEAMs necessitate comprehensive assessments and tailored strategies for reliable analysis of multi-batch omics data.
Current MVI and BE correction methods may not adequately handle BEAMs, compromising data integrity.
Future research should focus on advanced simulations and dedicated MVI methods to address BEAMs effectively.