SignalSet-class: Class '"SignalSet"'

Description Objects from the Class Slots Methods Note Author(s) See Also Examples

Description

We use the term "(continuous) signal" to refer to a weighted sum (by default, the mean) of gene-features. By dichotomizing a continuous signals, we obtain a "binary signal". The SignalSet class represents the set of continuous and binary signals obtained after clustering the features in a data set.

Objects from the Class

Objects can be created by calls of the form new("SignalSet", ...). However, users are styrongly discouraged from contructing a SignalSet manually. They are only used in the code internal to the construction of a Reaper object.

Slots

members:

Object of class "list". Each member of the list is a character vector enumerating the features defining each signal.

continuous:

A matrix where the number of columns equals the length of the members list; each column contains the mean expression of the (assumed standardized) corresponding features.

binary:

A matrix where the number of columns equals the length of the members list; each column contains expression values dichotmoized to 0 or 1 by splitting the conmtinuous siognal at zero.

continuousClusters:

Object of class "hclust" obtained by clustering samples based on the continuous signals.

binaryClusters:

Object of class "hclust" obtained by clustering samples based on the binary signals.

Methods

No methods defined with class "SignalSet" in the signature.

Note

The length of members and thus the number of signals may be smaller than expected from the number of clusters found by Reaper. The implementation of the SignalSet tries to determine if two signals are pointing in opposite directions, which could happen if they are postively and negatively correlated sets. This behavior is likely to change in the future.

Author(s)

Kevin R. Coombes <[email protected]>

See Also

Reaper

Examples

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# Simulate  a data set with some structure
set.seed(250264)
sigma1 <- matrix(0, ncol=16, nrow=16)
sigma1[1:7, 1:7] <- 0.7
sigma1[8:14, 8:14] <- 0.3
diag(sigma1) <- 1
st <- SimThresher(sigma1, nSample=300)
# Threshing is completed; now we can reap
reap <- Reaper(st)
# now extract the signal set
ss <- reap@signalSet
dim(ss@continuous)
dim(ss@binary)
table(ss@binary[,1], ss@binary[,2])
plot(ss@continuousClusters)

Thresher documentation built on March 20, 2018, 3 a.m.