Description Usage Arguments Details Value Additional Slots Note Author(s) References See Also Examples

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.

1 | ```
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."

`thresher`

:Returns the functions of the

`Thresher`

object class of the`Thresher`

R-package.`reaper`

:Returns the functions of the

`Reaper`

object class of the`Thresher`

R-package.

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`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
#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)
``` |

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