threshLGF {Mercator} | R Documentation |
Threshing and Reaping the BinaryMatrix
Description
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.
Usage
threshLGF(object, cutoff = 0)
Arguments
object |
An object of class |
cutoff |
The value of |
Details
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.
Value
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."
Additional Slots
thresher
:Returns the functions of the
Thresher
object class of theThresher
R-package.reaper
:Returns the functions of the
Reaper
object class of theThresher
R-package.
Note
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.
Author(s)
Kevin R. Coombes <krc@silicovore.com>, Caitlin E. Coombes
References
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.
See Also
The threshLGF
function creates a new object of class
ThreshedBinaryMatrix
from an object of class BinaryMatrix
.
Examples
#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)