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. 2018 Nov 5;8:16329. doi: 10.1038/s41598-018-34688-x

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Handling dropouts and retaining “true zeros” - AutoImpute, scImpute, and MAGIC successfully recover the majority of true zeros in the data and don’t confuse them with dropouts (which need imputation). As the gene expression in bulk-RNA seq data enriches, the zeros in unimputed data (which essentially are dropouts) are filled in with counts in imputed data from AutoImpute, scImpute and drImpute, showing a drop in the fraction of zeros; while Magic mostly shows a high fraction of near-zero imputed values.