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scDenorm: a denormalization tool for integrating single-cell transcriptomics data
Yin Huang1,2, Anna Vathrakokili Pournara3, Ying Ao4
1Translational Research Institute of Brain and Brain-Like Intelligence and Department of Anesthesiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China.
Gigascience
|March 31, 2026
Summary
scDenorm reverses data normalization in single-cell omics, preventing biases and ensuring accurate cell type and disease mechanism analysis. This method preserves original measurements for reliable atlas-scale data integration.
Area of Science:
- Single-cell omics
- Computational biology
- Bioinformatics
Background:
- Integrating single-cell omics data at atlas scale advances cell type and disease mechanism understanding.
- Different normalization methods introduce biases (batch effects, gene distortion), hindering accurate downstream analysis.
- Existing methods struggle to reconcile data processed with varied normalization techniques.
Purpose of the Study:
- To introduce scDenorm, an algorithm designed to revert delta-method normalized single-cell omics data back to raw counts.
- To ensure the integrity of original measurements and enable consistent data processing for large-scale integration.
- To mitigate biases arising from diverse normalization strategies in single-cell omics datasets.
Main Methods:
- Developed scDenorm algorithm to reverse delta-method normalization.
- Applied scDenorm to revert normalized single-cell omics data to raw counts.
- Evaluated scDenorm performance on large-scale datasets and its impact on data integration.
Main Results:
- scDenorm successfully reverted delta-method normalized data to raw counts.
- Preservation of original measurement integrity was demonstrated.
- Consistent data processing was achieved, improving downstream analysis accuracy across multiple datasets.
Conclusions:
- scDenorm effectively addresses normalization biases in single-cell omics integration.
- The algorithm ensures data integrity and facilitates more reliable atlas-scale analyses.
- scDenorm is a valuable tool for researchers working with diverse single-cell omics datasets.
