Demystifying “drop-outs” in single-cell UMI data
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 th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-08-01
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| Series: | Genome Biology |
| Online Access: | http://link.springer.com/article/10.1186/s13059-020-02096-y |