



Drugs x Cells 
Genes x Cells 
Genes x Drugs 
CIMminer generates colorcoded Clustered Image Maps (CIMs) ("heat maps") to represent
"highdimensional" data sets such as gene expression profiles. We introduced CIMs in the
mid1990's for data on drug activity, target expression, gene expression, and proteomic
profiles. Clustering of the axes brings like together with like to create patterns of
color. (Weinstein, et al., Science 1997; 275:343349). To
learn more details, look at the following articles.
 Weinstein, et al., Stem Cells 12: 1322 (1994)
 Weinstein, et al., Science 275: 343349 (1997)
 Myers, et al., Electophoresis 18: 647653 (1997)
 Eisen, et al., PNAS 95(25):148638 (1998)
 Scherf, et al., Nature Genetics 24: 236244 (2000)
One Matrix CIM 
The Clustered Image Map is a 2dimensional visualization of a realvalued matrix with N rows and M columns (NxM).
The CIM has the same number of rows and columns as the matrix and the color at the ith row and jth column is indicative
of the corresponding element of the matrix. 
Optionally, the rows and/or columns of the matrix can be clustered to identify interesting patterns. This clustering can
be done according to several different distance measures and clustering methods. 
Two Matrix CIM 
Two matrices are used as input in this case, one NxP and another PxM. From these, a third matrix (the product matrix) of
size NxM is created where element (i,j) is the correlation between the ith row of the first the jth column of the second matrix.
A CIM for the product matrix is created which is colored according to the elements in the various rows and columns. 
The rows and/or columns of the product matrix can be clustered to bring out patterns, but here the clustering is done
based on the rows of the first input matrix and the columns of the second input matrix. The rows are reordered by clustering
the rows of the first input matrix and this reordering is used for the product matrix. Similarly the clustering of the columns
of the second input matrix gives the reordering of the columns of the product matrix. 