plot_CCDF: Function to plot the CCDF according to the type of X et Z
In ccdf: Distribution-Free Single-Cell Differential Expression Analysis

Description Usage Arguments Value Examples

View source: R/plot_CCDF.R

Function to plot the CCDF according to the type of X et Z

plot_CCDF(
  Y,
  X,
  Z = NULL,
  method = "linear regression",
  fast = TRUE,
  space_y = FALSE,
  number_y = length(Y)
)

`Y`	a numeric vector of size `n` containing the preprocessed expressions from `n` samples (or cells).
`X`	a numeric or factor vector of size `n` containing the variable to be tested (the condition to be tested).
`Z`	a numeric or factor vector of size `n` containing the covariate. Multiple variables are not allowed.
`method`	a character string indicating which method to use to compute the CCDF, either `'linear regression'`, `'logistic regression'` and `'permutations'` or `'RF'` for Random Forests. Default is `'linear regression'` since it is the method used in the test.
`fast`	a logical flag indicating whether the fast implementation of logistic regression should be used. Only if `'dist_permutations'` is specified. Default is `TRUE`.
`space_y`	a logical flag indicating whether the y thresholds are spaced. When `space_y` is `TRUE`, a regular sequence between the minimum and the maximum of the observations is used. Default is `FALSE`.
`number_y`	an integer value indicating the number of y thresholds (and therefore the number of regressions) to perform the test. Default is `length(Y)`.

a ggplot object

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X <- as.factor(rbinom(n=100, size = 1, prob = 0.5))
Y <- ((X==1)*rnorm(n = 50,0,1)) + ((X==0)*rnorm(n = 50,0.5,1))
plot_CCDF(data.frame(Y=Y),data.frame(X=X),method="linear regression")