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.
a numeric matrix of estimated lambdas of equal dimension to expr_mat, see Details.
scaling parameter, see Details.
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 calculates weights for each observation as the probability of observing a greater or equal deviation from the mean of the Poisson distribution.
lambdas and alpha should be calculated using the PoisUMI_Fit_Full_Poisson function.
A matrix of calculated weights for each expression value (same dimensions as input matrix).
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