Threshing and Reaping the BinaryMatrix
The threshLGF function produces an object of class
ThreshedBinaryMatrix from threshing on an object of class
BinaryMatrix.
The function threshLGF and the ThreshedBinaryMatrix
object can be used to access the functionality of the Thresher
R-package within Mercator.
threshLGF(object, cutoff = 0)
object |
An object of class BinaryMatrix |
cutoff |
The value of |
The Thresher R-package provides a variety of functionalities
for data filtering and the identification of and reduction to "informative" features.
It performs clustering using a combination of outlier detection, principal
component analysis, and von Mises Fisher mixture models. By identifying
significant features, Thresher performs feature reduction through the
identification and removal of noninformative features and the nonbiased
calculation of the number of groups (K) for down-stream use.
threshLGF returns an object of class ThreshedBinaryMatrix.
The ThreshedBinaryMatrix object retains all the functionality,
slots, and methods of the BinaryMatrix object class with added
features. After threshing, the ThreshedBinaryMatrix records the
history, "Threshed."
The Thresher R-package applies the Auer-Gervini statistic
for principal component analysis, outlier detection, and identification
of uninformative features on a matrix of class integer or
numeric.
An initial delta of 0.3 is recommended.
Kevin R. Coombes <krc@silicovore.com>, Caitlin E. Coombes
Wang, M., Abrams, Z. B., Kornblau, S. M., & Coombes, K. R. (2018). Thresher: determining the number of clusters while removing outliers. BMC bioinformatics, 19(1), 9.
The threshLGF function creates a new object of class
ThreshedBinaryMatrix from an object of class BinaryMatrix.
#Create a BinaryMatrix
set.seed(52134)
my.matrix <- matrix(rbinom(50*100, 1, 0.15), ncol=50)
my.rows <- as.data.frame(paste("R", 1:100, sep=""))
my.cols <- as.data.frame(paste("C", 1:50, sep=""))
my.binmat <- BinaryMatrix(my.matrix, my.cols, my.rows)
summary(my.binmat)
#Identify delta cutoff and thresh
my.binmat <- threshLGF(my.binmat)
Delta <- my.binmat@thresher@delta
sort(Delta)
hist(Delta, breaks=15, main="", xlab="Weight")
abline(v=0.3, col='red')
my.binmat <- threshLGF(my.binmat, cutoff = 0.3)
summary(my.binmat)
#Principal Component Analysis
my.binmat@reaper@pcdim
my.binmat@reaper@nGroups
plot(my.binmat@reaper@ag)
abline(h=1, col="red")
screeplot(my.binmat@reaper)
abline(v=6, col="forestgreen", lwd=2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.