Quantile normalization of cell-level data to match typical UMI count data
A matrix of class dgCMatrix with genes as rows and columns as cells
A UMI-fied count matrix
sctransform::vst operates under the assumption that gene counts approximately follow a Negative Binomial dristribution. For UMI-based data that seems to be the case, however, non-UMI data does not behave in the same way. In some cases it might be better to to apply a transformation to such data to make it look like UMI data. This function applies such a transformation function.
Cells in the input matrix are processed independently. For each cell the non-zero data is transformed to quantile values. Based on the number of genes detected a smooth function is used to predict the UMI-like counts.
The functions have be trained on various public data sets and come as part of the package (see umify_data data set in this package).
silly_example <- umify(pbmc)
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