LikelihoodMethod#

missionbio.demultiplex.protein.pace.likelihood.LikelihoodMethod

class LikelihoodMethod(protein: Assay, truth: Truth, min_distance_for_doublet: int = 3, **kwargs: Any)#

A PACE method to categorize the cells into the most likely category based on the given truth.

Parameters:

protein – The protein assay
truth – A Truth object
min_distance_for_doublet – The minimum number of antibodies that must be different between two clones for their doublet to be considered.
kwargs – The other parameters passed to PACEModel.

Functions#

`__init__`	param protein: The protein assay
`assignment_probabilities`	Compute the probability of each cell type for each row in the expression matrix
`classify_mixed_clusters`	Assigns the doublets as "Mixed" if they have an adjusted mixing rate lower than the max_adjusted_mixing or "Mixed like" for higher adjusted mixing rates.
`clipped_normalized_reads`	Clip the NSP counts to the range [0, 1]
`clipped_signature`	The mean NSP values rounded to 0 or 1
`cluster_truth`	The truth that defines the clusters in the protein assay
`get_normalized_reads`	returns: The normalized counts in the protein assay as a dataframe
`label_cells`	Adds the LABEL row attribute based on the maximum likelihood method of assignment
`label_sticky_cells`	Assigns the "Sticky" labels to the cells
`likelihoods`	Create the probability density function of the likelihood of each expression type.
`log_likelihood`	Calculate the log-likelihood of the data.
`palette`	Create a palette for the cell types by giving each sub-celltype a unique color