dot-classifyDfOnChannel: K-means classify a data frame where the droplets are negative...

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

Description

K-means classify a data frame where the droplets are negative in the same channels only.

Usage

.classifyDfOnChannel(
  df,
  channel,
  centres = NULL,
  minSeparation = 2000,
  fullTable = TRUE
)

Arguments

df	A data frame corresponding 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 df.

Author(s)

Anthony Chiu, [e-mail]

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