Benchmarking of cell type deconvolution pipelines for transcriptomics data
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.
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-19015-1 |
| Summary: | 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. |
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| ISSN: | 2041-1723 |