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Related Experiment Video

Updated: Apr 1, 2026

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

scDenorm: a denormalization tool for integrating single-cell transcriptomics data.

Yin Huang^1,2, 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.

|March 31, 2026

Summary

This summary is machine-generated.

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.

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Last Updated: Apr 1, 2026

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

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Published on: July 18, 2019

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.