TY - JOUR
T1 - scDenorm
T2 - a denormalization tool for integrating single-cell transcriptomics data
AU - Huang, Yin
AU - Pournara, Anna Vathrakokoili
AU - Ao, Ying
AU - Huang, Ziliang
AU - Zhang, Hui
AU - Zhang, Yongjian
AU - Liu, Sheng
AU - Brazma, Alvis
AU - Papatheodorou, Irene
AU - Yang, Xinlu
AU - Shi, Ming
AU - Miao, Zhichao
N1 - Publisher Copyright:
© The Author(s) 2026. Published by Oxford University Press on behalf of GigaScience. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
PY - 2022
Y1 - 2022
N2 - Integrating single-cell omics data at an atlas scale enhances our understanding of cell types and disease mechanisms. However, the integration of data processed by different normalization methods can lead to biases, such as unexpected batch effects and gene expression distortion, leading to misinterpretations in downstream analysis. To address these challenges, we present scDenorm, an algorithm that reverts delta-method normalized single-cell omics data to raw counts, preserving the integrity of the original measurements and ensuring consistent data processing during integration. We evaluated scDenorm’s performance on large-scale datasets and benchmarked its impact on data integration and downstream analysis across 3 datasets.
AB - Integrating single-cell omics data at an atlas scale enhances our understanding of cell types and disease mechanisms. However, the integration of data processed by different normalization methods can lead to biases, such as unexpected batch effects and gene expression distortion, leading to misinterpretations in downstream analysis. To address these challenges, we present scDenorm, an algorithm that reverts delta-method normalized single-cell omics data to raw counts, preserving the integrity of the original measurements and ensuring consistent data processing during integration. We evaluated scDenorm’s performance on large-scale datasets and benchmarked its impact on data integration and downstream analysis across 3 datasets.
UR - https://www.scopus.com/pages/publications/105037847176
U2 - 10.1093/gigascience/giag032
DO - 10.1093/gigascience/giag032
M3 - 文章
C2 - 41915012
AN - SCOPUS:105037847176
SN - 2047-217X
VL - 15
JO - GigaScience
JF - GigaScience
ER -