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Updated: Aug 23, 2026
Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms
Published on: May 9, 2017
Benchmarking cell-type deconvolution in cross-platform transcriptomic data
Aakanksha Singh1,2,3,4, Pinar Cakmak1,2,5, Jennifer H Lun1,2,6
1Goethe University, Faculty of Medicine, Institute of Neurology (Edinger Institute), Frankfurt, Germany.
Background:
Transcriptomic data from diverse measurement technologies are widely used to study tissue heterogeneity. Cell-type deconvolution, which resolves mixed transcriptomic signals into cellular components, is a key analytical approach. However, achieving accurate deconvolution across platforms remains challenging due to platform-specific experimental and technological biases.
Results:
We systematically benchmarked deconvolution performance using real-world cross-platform datasets and simulated data modeling distinct technological features. SpatialDecon and cell2location demonstrated the most reliable and consistent performance across both simulated and experimental settings across a broad range of technological biases.
Conclusions:
Our results highlight how the different deconvolution tools are affected by data properties that depend on technological differences between transcriptomic platforms. Moreover, we provide practical guidelines for selecting computational methods dependent on experimental design for robust deconvolution of cross-platform transcriptomic data.
