dot-classifyOnChannel: K-means classify a list of data frames individually, where...

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

Description

K-means classify a list of data frames individually, where each data frame comprises droplets that are negative in the same channels only.

Usage

.classifyOnChannel(
  cl,
  channel,
  centres = NULL,
  minSeparation = 2000,
  fullTable = TRUE
)

Arguments

cl	List of data frames, where each data frame corresponds to a well with droplets corresponding only to "NN" and "NP" or "NN" and "PN".
channel	The channel on which to classify (1 or 2).
centres	A data frame of centres. The data frame should have columns Ch1.Amplitude and Ch2.Amplitude and row names corresponding the cluster label, e.g. "NN", "NP", "PN" or "PP".
minSeparation	The minimum distance required between two cluster centres in order for us to assume that k-means found two distinct clusters. Defaults to 2000.
fullTable	Whether to return a full table or just a vector. Defaults to 'TRUE'

Value

A classification for cl.

Author(s)

Anthony Chiu, [e-mail]

CRUKMI-ComputationalBiology/twoddpcr documentation built on Feb. 14, 2021, 9:18 p.m.