dot-classwiseMahalanobisRain: Fuzzy clusters by bivariate normal distributions.

In twoddpcr: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules

Description

Assume that the class cl is bivariate normally distributed. This method adds fuzziness to the class. Other classes are not changed.

Usage

.classwiseMahalanobisRain(droplets, cl, maxDistance = 30, classCol = "class")

Arguments

droplets	A data frame of droplets with Ch1.Amplitude and Ch2.Amplitude columns, as well as a class column (see the parameter classCol).
cl	The class to focus on. This should be either "NN", "NP", "PN" or "PP".
maxDistance	An integer corresponding to the maximum Mahalanobis distance for which we will consider points to be members of the class, i.e. this is the level outside of which we consider droplets to be too far from the cluster.
classCol	The column (name or number) from droplets representing the class.

Value

A factor corresponding to the class column with Rain entries added for the class cl.

Author(s)

Anthony Chiu, [e-mail]

twoddpcr documentation built on Nov. 8, 2020, 5:49 p.m.