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- W3134779741 abstract "Single-cell RNA-seq technologies have been successfully employed over the past decade to generate many high resolution cell atlases. These have proved invaluable in recent efforts aimed at understanding the cell type specificity of host genes involved in SARS-CoV-2 infections. While single-cell atlases are based on well-sampled highly-expressed genes, many of the genes of interest for understanding SARS-CoV-2 can be expressed at very low levels. Common assumptions underlying standard single-cell analyses don't hold when examining low-expressed genes, with the result that standard workflows can produce misleading results.Supplementary data and all of the code to reproduce Figure 1 are available here: https://github.com/pachterlab/BP_2020_2/." @default.
- W3134779741 created "2021-03-15" @default.
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- W3134779741 date "2021-03-02" @default.
- W3134779741 modified "2023-10-11" @default.
- W3134779741 title "Normalization of single-cell RNA-seq counts by log(<i>x</i> + 1) or log(1 + <i>x</i>)" @default.
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- W3134779741 doi "https://doi.org/10.1093/bioinformatics/btab085" @default.
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