Calculates weights based on information contained in each expression value (i.e. difference from null expectation).
a numeric matrix of raw UMI counts, columns = samples, rows = genes.
output from PoisUMI_Fit_Full_Poisson.
The Poisson model considers each observation to be drawn from a Poisson distribution with parameter:
lambda_ij = m_i * m_j * T * alpha
are the proportion of all molecules detected that are of gene i or in cell j respectively, and T is the total molecules detected across all genes and cells.
approximates the extent of over-counting of molecules.
This function normalizes expression data as the number of standard deviations from the mean each observation is using the observation-specfic Poisson distribution. Resulting data will contain positive, higher than expected, and negative, lower than expected, values.
If "fit" is not provided it will be automatically calculated using PoisUMI_Fit_Full_Poisson.
A matrix of normalized expression values.
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