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Author Correction: Benchmarking of cell type deconvolution pipelines for transcriptomics data
- Source :
- Nature Communications, Vol 11, Iss 1, Pp 1-1 (2020), Nature Communications
- Publication Year :
- 2020
- Publisher :
- Nature Portfolio, 2020.
-
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 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.
- Subjects :
- Computer science
Cells
Science
General Physics and Astronomy
computer.software_genre
General Biochemistry, Genetics and Molecular Biology
Mice
Species Specificity
Animals
Data Mining
Humans
Author Correction
Transcriptomics
Multidisciplinary
Sequence Analysis, RNA
Gene Expression Profiling
Published Erratum
Computational Biology
General Chemistry
Benchmarking
Computational biology and bioinformatics
Deconvolution
Data mining
Single-Cell Analysis
Transcriptome
computer
Subjects
Details
- Language :
- English
- ISSN :
- 20411723
- Volume :
- 11
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Nature Communications
- Accession number :
- edsair.doi.dedup.....1e881078ac1f0c1fdb7fe5dbfeb1bd78