Fitting_Dropout_Models: Fit functions to the dropouts vs expression distribution.
In M3Drop: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

Description Usage Arguments Details Value References Examples

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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  bg__fit_MM(p, s)
  bg__fit_logistic(p, s)
  bg__fit_ZIFA(p, s)

`p`	a vector of dropout rates for each gene.
`s`	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: [1]). Fit using mle2 using normally distributed error. bg__fit_logistic : a logistic regression between P and log base 10 of S (used by [2]). Fit using glm (excludes genes where S == 0). bg__fit_ZIFA : a double exponential

P = e^(-lambda*S^2)

(used by [3]). 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

[1] Keener, J.; Sneyd, J. (2008). Mathematical Physiology: I: Cellular Physiology (2 ed.). Springer. ISBN 978-0-387-75846-6 [2] Kharchenko, PV; Silberstein, L; Scadden, DT. (2014) Bayesian approach to single-cell differential expression analysis. Nature Methods. 11:740-742 [3] 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

#  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)