Benchmarking of cell type deconvolution pipelines for transcriptomics data
By F. Avila Cobos, José Alquicira-Hernández, Joseph E. Powell, Pieter Mestdagh, K. De Preter. Nature Communications, 2020
369 citations recorded by the source corpus.
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 performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance. Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance.
Subfields: Cancer Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/39670005154a18fd968a76ddfd276c0675228333
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
Citations in the corpus
- Deep learning in drug discovery: an integrative review and future challenges (2022, background): “Transfer of knowledge and deconvolution techniques for in silico cell set (Avila et al. 2020) may offer effective ways to reduce the requirement to generate a lot of single-cell data to predict combination therapy responders and impacts of toxicity, as well as the recommended dosage that optimizes b”
- IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures (2020, background): “TME-cell deconvolution is the major technological hurdle, and the deconvolution algorithms vary in their merits and pitfalls (10, 11).”