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Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data

Aaron Lun
doi: https://doi.org/10.1101/404962
Aaron Lun
1Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom
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Abstract

Applying a log-transformation to normalized expression values is one of the most common procedures in exploratory analyses of single-cell RNA sequencing (scRNA-seq) data. Normalization removes systematic biases in sequencing coverage between cells, while the log-transformation ensures that downstream computational procedures operate on relative rather than absolute differences in expression. We show that the log-transformation can introduce systematic errors when cells vary in sequencing coverage, leading to spurious non-zero differences in expression and artificial population structure in simulations. We observe similar effects in real scRNA-seq data where the difference in transformed values between groups of cells is not an accurate proxy for the log-fold change. We provide some practical recommendations to overcome this effect and analytically derive an expression for a larger pseudo-count that controls the transformation-induced error to a specified threshold.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 31, 2018.
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Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data
Aaron Lun
bioRxiv 404962; doi: https://doi.org/10.1101/404962
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Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data
Aaron Lun
bioRxiv 404962; doi: https://doi.org/10.1101/404962

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