A building block of the main functions. To derive the thresholds for detecting dubious metacells based on the output permutation results (TabMC)

Usage

mcRigor_threshold(
  TabMC,
  test_cutoff = 0.01,
  thre_smooth = T,
  thre_bw = 1/6,
  draw = T,
  palpha = 1,
  org_color = c("red", "orange", "yellow", "lightblue"),
  null_color = "#666666",
  pur_metric = NULL
)

Arguments

TabMC: A dataframe containing the permutation results. Saved in the previous steps
test_cutoff: The test size for dubious metacell detection testing
thre_smooth: A boolean indicating whether to smooth the threshold function
thre_bw: If thre_smooth is True, what is the bandwidth for smoothing
draw: A boolean indicating whether to visualize the mcRigor results
palpha: Point alpha value for transparency in drawing.
org_color: The colors indicating metacell purities or other interested factors
null_color: The color for the null.
pur_metric: Name of the covariate that we want to compute purity on. Can be NULL or a metadata variable name, ex. cell type.

Value

A list containing the following fields:

threshold: The thresholds for dubious metacell detection
TabMC: A dataframe containing the permutation results and the testing results given by mcRigor
test_plot: The scatter plots demonstrating the mcDiv values and the obtained thresholds for dubious metacell detection
purity_plot: A violin plot showing the purity distribution of the pur_metric covariate in dubious metacells and trustworthy metacells