Normalizes read counts (without UMIs) or transcripts per million (TPM) such as those generated by smart-seq2 to match a discrete quasi-UMI target distribution. The resulting QUMI counts can be analyzed as if they were UMI counts.
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for the generic, additional arguments to pass to object-specific methods.
Positive scalar, a fixed shape parameter for the target
distribution. The shape parameter represents sigma for the
Poisson-lognormal target distribution. See
Positive integer indicating the number of cores to use for
parallel processing. See
In case object is a SingleCellExperiment or SummarizedExperiment, the assay from which the quasi-UMIs should be calculated is specified here.
The default assay of "tpm" for transcripts per million does refer to such values after summarizing to gene level. The algorithm in its current form has not been evaluated for quasi-UMI generation from transcript counts, as full-length data with UMIs have not until very recently been available.
If NA values appear in the quminorm output, this is likely caused by numerical errors in computing the Poisson-lognormal cumulative distribution function. In our experience it is more common in cells with very high zero fractions. Consider either discarding these cells or using a smaller quminorm shape parameter to avoid having the NAs.
An object of same class as the input object but with the nonzero values normalized to match the target quasi-UMI distribution. In case of a SingleCellExperiment or SummarizedExperiment, the quasiUMIs are added to a new assays slot named qumi_poilog_[shape] where [shape] is the user-specified numeric shape parameter.
Townes FW and Irizarry RA (2020). Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers. Genome Biology https://doi.org/10.1186/s13059-020-02078-0
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