CompareResultJI function

Compare Biclustering Results using Jaccard Index

Creates a heatmap and returns a similarity matrix of the Jaccard Index (Row, Column or both dimensions) in order to compare 2 different biclustering results or compare the biclusters of a single result.

CompareResultJI(BCresult1, BCresult2 = NULL, type = "both", plot = TRUE)

Arguments

• BCresult1: A S4 Biclust object. If only this input Biclust object is given, the biclusters of this single result will be compared.
• BCresult2: A second S4 Biclust object to which BCresult1 should be compared. (default=NULL)
• type: Of which dimension should the Jaccard Index be computed? Can be "row", "col" or "both" (default).
• plot: Logical value if plot should be outputted (default=TRUE).

Returns

A list containing

• SimMat: The JI Similarity Matrix between the compared biclusters.
• MaxSim: A list containing the maximum values on each row (BCResult1) and each column (BCResult2).

Details

The Jaccard Index between two biclusters is calculated as following:

in which

• type="row" or type="col"

  • $m_1=$ Number of rows/columns of BC1
  • $m_2=$ Number of rows/columns of BC2
  • $m_{12}=$ Number of rows/columns of union of row/column membership of BC1 and BC2

• type="both"

  • $m_1=$ Size of BC1 (rows times columns)
  • $m_2=$ Size of BC2 (rows times columns)
  • $m_{12}= m_1+m_2 -$ size of overlapping BC of BC1 and BC2

Examples

## Not run:

data <- matrix(sample(c(0,1),100*100,replace=TRUE,prob=c(0.9,0.1)),nrow=100,ncol=100)
data[1:10,1:10] <- 1 # BC1
data[11:20,11:20] <- 1 # BC2
data[21:30,21:30] <- 1 # BC3
data <- data[sample(1:nrow(data),nrow(data)),sample(1:ncol(data),ncol(data))]

# Result 1
result1 <- bibit(data,minr=5,minc=5)
result1

# Result 2
result2 <- bibit(data,minr=2,minc=2)
result2

## Compare all BC's of Result 1 ##
Sim1 <- CompareResultJI(BCresult1=result1,type="both")
Sim1$SimMat

## Compare BC's of Result 1 and 2 ##
Sim12 <- CompareResultJI(BCresult1=result1,BCresult2=result2,type="both",plot=FALSE)
str(Sim12)
## End(Not run)

Author(s)

Ewoud De Troyer