Microarray data mining using landmark gene-guided clustering

Abstract

Background: Clustering is a popular data exploration technique widely used in microarray data analysis. Most conventional clustering algorithms, however, generate only one set of clusters independent of the biological context of the analysis. This is often inadequate to explore data from different biological perspectives and gain new insights. We propose a new clustering model that can generate multiple versions of different clusters from a single dataset, each of which highlights a different aspect of the given dataset.

Results: By applying our SigCalc algorithm to three yeast Saccharomyces cerevisiae datasets we show two results. First, we show that different sets of clusters can be generated from the same dataset using different sets of landmark genes. Each set of clusters groups genes differently and reveals new biological associations between genes that were not apparent from clustering the original microarray expression data. Second, we show that many of these new found biological associations are common across datasets. These results also provide strong evidence of a link between the choice of landmark genes and the new biological associations found in gene clusters.

Conclusion: We have used the SigCalc algorithm to project the microarray data onto a completely new subspace whose co-ordinates are genes (called landmark genes), known to belong to a Biological Process. The projected space is not a true vector space in mathematical terms. However, we use the term subspace to refer to one of virtually infinite numbers of projected spaces that our proposed method can produce. By changing the biological process and thus the landmark genes, we can change this subspace. We have shown how clustering on this subspace reveals new, biologically meaningful clusters which were not evident in the clusters generated by conventional methods. The R scripts (source code) are freely available under the GPL license. The source code is available [see Additional File 1] as additional material, and the latest version can be obtained at http://www4.ncsu.edu/~pchopra/landmarks.html. The code is under active development to incorporate new clustering methods and analysis.

Figure 1

Comparison of microarray expression data with gene signatures for genes that clustered together using gene signatures. Gasch dataset: Genes associated with multi-organism process (GO:0051704) were clustered together.

Figure 2

Comparison of microarray expression data with gene signatures for genes that clustered together using gene signatures. Gasch dataset: Genes associated with reproduction (GO:0000003) were clustered together.

Figure 3

Number of GO terms for varying number of clusters. For each landmark, a number of unique GO terms are found irrespective of the number of clusters.

Figure 4

Comparison of unique GO terms found using gene signatures versus those found using semi-supervized clustering (SSC) for the Spellman and Gasch datasets. For the semi-supervized clustering (SSC), the landmark genes were considered as 'must-link' constraints. SSC1 denotes the number of unique GO terms found by using landmark genes as constraints in SSC. GSM1 denotes the number of unique GO terms found by using the gene signature model. SSC2 denotes the number of unique GO terms found for SSC if we remove the largest cluster (containing all the landmark genes) from analysis. GSM2 denotes the number of unique GO terms found using the gene signature model if we remove the largest cluster from analysis. The results for other landmarks are shown in Figure 3 in Additional File 2.

Figure 5

Comparison of gene expression patterns in the largest cluster of semi-supervized clustering (SSC) versus the gene signature model (GSM) for the Gasch dataset using landmark genes associated with 'proteolysis'.

Figure 6

Microarray expression data matrix. The selected landmark genes are highlighted.

Figure 7

Gene signatures derived from microarray data using SigCalc. Gene signature matrix, where each row represents a gene signature.

Figure 8

Significant GO terms in microarray data. The dots indicate Significant GO terms found by performing clustering on microarray data (i.e., original GO terms).

Figure 9

Significant GO terms in microarray data and in gene signatures. Shows a comparison of Significant GO terms found by clustering gene signatures (i.e., landmark GO terms) with the original GO terms.