Abstract
Expression profiles of all genes can aid in getting more insight into the biological foundation of observed phenotypes or in identifying marker genes for use in clinical practice. With the invention of high-throughput DNA Microarrays profiling the expression state of cells on a whole-genome scale became feasible.
Here, we propose a method based on model-based clustering to detect marker gene clusters that are most important in classifying different cell types. We show at the example of Acute Lymphoblastic Leukemia that these modules capture the expression state of different sample classes and that they give more biological insight into the different cell types than using just marker genes. Additionally, our method suggests groups of genes that can serve as clinical relevant markers.
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Perner, J., Zotenko, E. (2011). Characterizing Cell Types through Differentially Expressed Gene Clusters Using a Model-Based Approach. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2011. Lecture Notes in Computer Science(), vol 6870. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23184-1_9
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DOI: https://doi.org/10.1007/978-3-642-23184-1_9
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