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

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 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.

`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.

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

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.

Kevin R. Coombes <[email protected]>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# 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.

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