Demystifying “drop-outs” in single-cell UMI data
dc.contributor.author | Kim, Tae Hyun | |
dc.contributor.author | Zhou, Xiang | |
dc.contributor.author | Chen, Mengjie | |
dc.date.accessioned | 2022-08-10T18:35:58Z | |
dc.date.available | 2022-08-10T18:35:58Z | |
dc.date.issued | 2020-08-06 | |
dc.identifier.citation | Genome Biology. 2020 Aug 06;21(1):196 | |
dc.identifier.uri | https://doi.org/10.1186/s13059-020-02096-y | |
dc.identifier.uri | https://hdl.handle.net/2027.42/173855 | en |
dc.description.abstract | Abstract Many existing pipelines for scRNA-seq data apply pre-processing steps such as normalization or imputation to account for excessive zeros or “drop-outs." Here, we extensively analyze diverse UMI data sets to show that clustering should be the foremost step of the workflow. We observe that most drop-outs disappear once cell-type heterogeneity is resolved, while imputing or normalizing heterogeneous data can introduce unwanted noise. We propose a novel framework HIPPO (Heterogeneity-Inspired Pre-Processing tOol) that leverages zero proportions to explain cellular heterogeneity and integrates feature selection with iterative clustering. HIPPO leads to downstream analysis with greater flexibility and interpretability compared to alternatives. | |
dc.title | Demystifying “drop-outs” in single-cell UMI data | |
dc.type | Journal Article | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/173855/1/13059_2020_Article_2096.pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/5586 | |
dc.language.rfc3066 | en | |
dc.rights.holder | The Author(s) | |
dc.date.updated | 2022-08-10T18:35:57Z | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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