Fits the modified Michaelis-Menten equation (MM), a logistic regession (logistic), or a double exponential (ZIFA) function to the relationship between mean expression and dropout-rate (proportion of zero values).
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a vector of dropout rates for each gene.
a vector of mean expression values for each gene. Must be the same order & length as p.
Fits one of different models to the relationship between dropout rate and mean expression. The three models are:
bg__fit_MM : the Michaelis-Menten function
P = 1 - S/(K+S)
(see: ). Fit using
mle2 using normally distributed error.
bg__fit_logistic : a logistic regression between P and log base 10 of S (used by ). Fit using
glm (excludes genes where S == 0).
bg__fit_ZIFA : a double exponential
P = e^(-lambda*S^2)
(used by ). Fit using
lm after log-transformation (genes were P == 0 are assigned a value of one tenth of the smallest P which is not 0).
Named list including: K,fitted_err/B0,B1/lambda,fitted_err : the fitted parameters predictions : predicted values of p for each gene SSr/SAr : sum of squared/absolute residuals model : vector of string descriptors of the fit
 Keener, J.; Sneyd, J. (2008). Mathematical Physiology: I: Cellular Physiology (2 ed.). Springer. ISBN 978-0-387-75846-6  Kharchenko, PV; Silberstein, L; Scadden, DT. (2014) Bayesian approach to single-cell differential expression analysis. Nature Methods. 11:740-742  Pierson, E; Yau, C. (2015) ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biology. 16:241 doi:10.1186/s13059-015-0805-z
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# library(M3DExampleData) # gene_info = bg__calc_variables(Mmus_example_list$data) # MM_fit = bg__fit_MM(gene_info$p, gene_info$s) # logistic_fit = bg__fit_logistic(gene_info$p, gene_info$s) # ZIFA_fit = bg__fit_ZIFA(gene_info$p, gene_info$s)
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