assessGrouping: assessGrouping

In nearBynding: Discern RNA structure proximal to protein binding

Description

Assess grouping of samples assigned to the same category relative to random.

Usage

assessGrouping(
    distances,
    annotations,
    measurement = "mean",
    output = "KS.pvalue",
    ctrl_iterations = 10000
)

Arguments

distances	Data frame object with at least three columns where the first three columns are sample 1 name, sample 2 name, and the distance between them.
annotations	Data frame object with at least two columns where the first two columns are sample name and the category of the sample for grouping. Sample names must match sample 1 and sample 2 names in distances data frame.
measurement	The measurement for comparison between cases and controls and statistical analysis ("mean", "max", or "min). Default "mean"
output	A string denoting what information will be returned: either a list of test and control measurement distances ("measurements"), the p-value of the Kolmogorov-Smirnov test comparing test and control distributions ("KS.pvalue"), or a ggplot object plotting the test and control distributions ("plot"). Default "KS.pvalue"
ctrl_iterations	The number of iterations to test for the control distribution; an integer. Default 10000.

Value

output = "KS.pvalue"	the p-value of the Kolmogorov-Smirnov test comparing test and control distributions
output = "plot"	a ggplot object plotting the test and control distributions
output = "measurements"	a list or test and control measurement distances

Examples

## create random distance data frame
dist<-expand.grid(letters, letters)
dist$distance<-rnorm(nrow(dist))
annot<-data.frame(sample<-letters, category<- rep(1:13, 2))
## get KS p-value
assessGrouping(dist, annot)
## get plot of test vs control distributions
assessGrouping(dist, annot,
                output = "plot")

nearBynding documentation built on Nov. 8, 2020, 8:15 p.m.