Benchmarking of cell type deconvolution techniques for microarray data
Abstract
Cell type deconvolution algorithms have significantly advanced in recent years. However, a notable gap remains regarding the performance of these algorithms when applied to microarray data, which is widely available in numerous disease contexts. Here we compare the performance of existing cell type deconvolution algorithms across a spectrum of data preparation scenarios and diverse cell types within the context of microarray-based bulk transcriptome. Among the ten algorithms evaluated, MuSiC, qprogwc, and CIBERSORTx emerged as the top performers, demonstrating the highest correlation between the estimated and real cell type compositions (rmax = 0.74, 0.69, and 0.67, respectively). Our study provides a practical framework for microarray deconvolution, while also underscoring the necessity for greater precision in these techniques. Such methods would unlock the full potential of the vast repositories of microarray data in public domains, facilitating deeper insights into cellular compositions in various disease states, and promoting translational research.
Related
- DECONVersation: Single Cell Foundation Model-Derived Embeddings for Robust Cell Type Deconvolution of Bulk RNA-seq
- CellMAGE: cell-type deconvolution for multi-parent population analysis of gene expression
- Data-Driven Soft Labeling Scales DNA Read Classification to Whole-Body Cell-Type Deconvolution
- DECIPHER integrates disentangled representation learning and prototype-based cell-type deconvolution across molecular modalities
- ST-DISTAL: Dual-Branch Graph Convolution with Distributional Alignment for Cell-Type Deconvolution
BibTeX
@article{kamali2026benchmarking,
title = {{Benchmarking of cell type deconvolution techniques for microarray data}},
author = {Zoha Kamali and Harold Snieder and Ahmad Vaez},
journal = {iScience},
year = {2026},
month = oct,
doi = {10.1016/j.isci.2026.118079},
url = {https://www.cell.com/iscience/fulltext/S2589-0042(26)03462-0},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Benchmarking of cell type deconvolution techniques for microarray data
AU - Zoha Kamali
AU - Harold Snieder
AU - Ahmad Vaez
PY - 2026
DA - 2026/10/29
JO - iScience
DO - 10.1016/j.isci.2026.118079
UR - https://www.cell.com/iscience/fulltext/S2589-0042(26)03462-0
AB - Cell type deconvolution algorithms have significantly advanced in recent years. However, a notable gap remains regarding the performance of these algorithms when applied to microarray data, which is widely available in numerous disease contexts. Here we compare the performance of existing cell type deconvolution algorithms across a spectrum of data preparation scenarios and diverse cell types within the context of microarray-based bulk transcriptome. Among the ten algorithms evaluated, MuSiC, qprogwc, and CIBERSORTx emerged as the top performers, demonstrating the highest correlation between the estimated and real cell type compositions (rmax = 0.74, 0.69, and 0.67, respectively). Our study provides a practical framework for microarray deconvolution, while also underscoring the necessity for greater precision in these techniques. Such methods would unlock the full potential of the vast repositories of microarray data in public domains, facilitating deeper insights into cellular compositions in various disease states, and promoting translational research.
SN - 2589-0042
ER -