Abstract
One of the most notable challenges in single cell RNA-Seq data analysis is the so called drop-out effect, where only a fraction of the transcriptome of each cell is captured. The random nature of drop-outs, however, makes it possible to consider imputation methods as means of correcting for drop-outs. In this paper we study some existing scRNA-Seq imputation methods and propose a novel iterative imputation approach based on efficiently computing highly similar cells. We then present the results of a comprehensive assessment of existing and proposed methods on real scRNA-Seq datasets with varying per cell sequencing depth.
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Notes
- 1.
Note that, unlike KNN, which uses similarity between genes, LSImpute uses similarity between cells. Also, the number of nearest cells used for imputation is not fixed but depends on the minimum similarity threshold.
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Moussa, M., Măndoiu, I.I. (2018). Locality Sensitive Imputation for Single-Cell RNA-Seq Data. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_33
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DOI: https://doi.org/10.1007/978-3-319-94968-0_33
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