Benchmarking of cell type deconvolution pipelines for transcriptomics data
Garvan Institute of Medical Research · Ghent University Hospital · +4 more institutions
Abstract
Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable…
Citation impact
- FWCI
- 21.42
- Percentile
- 100%
- References
- 78
Authors
5- FAFrancisco Avila CobosCorresponding
Garvan Institute of Medical Research, Ghent University Hospital, Ghent University, Cancer Research Institute Ghent, VIB-UGent Center for Medical Biotechnology
- JAJosé Alquicira-Hernández
Garvan Institute of Medical Research, The University of Queensland
- JEJoseph E. Powell
Garvan Institute of Medical Research, The University of Queensland
- PMPieter Mestdagh
Ghent University Hospital, Cancer Research Institute Ghent
- KDKatleen De Preter
Ghent University Hospital, Ghent University, Cancer Research Institute Ghent, VIB-UGent Center for Medical Biotechnology
Topics & keywords
- Deconvolution
- Normalization (sociology)
- Computer science
- Benchmarking
- Data mining
- Database normalization
- Data type
- Artificial intelligence