Description Usage Arguments Details Value Author(s) Examples
Apply a new noise level on a Biclust object result or BiBitWorkflow result. See Details on how both objects are affected.
1 2 | UpdateBiclust_RowNoise(result, matrix, noise = 0.1, noise_select = 0,
removeBC = FALSE)
|
result |
A Biclust or BiBitWorkflow Object. |
matrix |
Accompanying binary data matrix which was used to obtain |
noise |
The new noise level which should be used in the rows of the biclusters. (default=
|
noise_select |
Should the allowed noise level be automatically selected for each pattern? (Using ad hoc method to find the elbow/kink in the Noise Scree plots)
|
removeBC |
(Only applicable when result is a Biclust object) Logical value if after applying a new noise level, duplicate and non-maximal BC's should be deleted. |
Using the column patterns of the Biclust result, new grows are grown using the inputted noise
level.
The removeBC
parameter decides if duplicate and non-maximal BC's should be deleted. Afterwards a new Biclust
S4 object is returned with the new biclusters.
The merged column patterns (after cutting the hierarchical tree) are extracted from the BiBitWorkflow object, namely the $info$MergedColPatterns
slot.
Afterwards, using the new noise
level, new rows are grown and the returned object is an updated BiBitWorkflow
object. (e.g. The final Biclust slot, MergedNoiseThresholds, coverage,etc. are updated)
A Biclust
or BiBitWorkflow
Object (See Details)
Ewoud De Troyer
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ## Not run:
## Prepare some data ##
set.seed(254)
mat <- matrix(sample(c(0,1),5000*50,replace=TRUE,prob=c(1-0.15,0.15)),
nrow=5000,ncol=50)
mat[1:200,1:10] <- matrix(sample(c(0,1),200*10,replace=TRUE,prob=c(1-0.9,0.9)),
nrow=200,ncol=10)
mat[300:399,6:15] <- matrix(sample(c(0,1),100*10,replace=TRUE,prob=c(1-0.9,0.9)),
nrow=100,ncol=10)
mat[400:599,21:30] <- matrix(sample(c(0,1),200*10,replace=TRUE,prob=c(1-0.9,0.9)),
nrow=200,ncol=10)
mat[700:799,29:38] <- matrix(sample(c(0,1),100*10,replace=TRUE,prob=c(1-0.9,0.9)),
nrow=100,ncol=10)
mat <- mat[sample(1:5000,5000,replace=FALSE),sample(1:50,50,replace=FALSE)]
## Apply BiBitWorkflow ##
out <- BiBitWorkflow(matrix=mat,minr=50,minc=5,noise=0.1,cut_type="number",cut_pm=4)
summary(out$Biclust)
## Update Rows with new noise level on Biclust Obect -> returns Biclust Object ##
out_new <- UpdateBiclust_RowNoise(result=out$Biclust,matrix=mat,noise=0.3)
summary(out_new)
out_new@info$Noise.Threshold # New Noise Levels
## Update Rows with new noise level on BiBitWorkflow Obect -> returns BiBitWorkflow Object ##
out_new2 <- UpdateBiclust_RowNoise(result=out,matrix=mat,noise=0.2)
summary(out_new2$Biclust)
out_new2$info$MergedNoiseThresholds # New Noise Levels
## End(Not run)
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