An important challenge in microarray data analyses is the detection of genes which are differentially expressed across different types of experimental conditions. We provide an extension of a finite mixture model to the clustering of genes and experimental conditions, where the partition of experimental conditions may be known or unknown. In particular, the idea is to adopt a finite mixture approach with mean/covariance reparameterization, where an explicit distinction among upregulated genes, down-regulated genes, non-regulated genes (with respect to a reference) is made; moreover, within each of these groups; genes that are differentially expressed between two or more types of experimental conditions may be identified.
Identifying partitions of genes and tissue samples in microarray data / Martella, Francesca; Alfo', Marco. - STAMPA. - (2009), pp. 185-188.
Identifying partitions of genes and tissue samples in microarray data.
MARTELLA, Francesca;ALFO', Marco
2009
Abstract
An important challenge in microarray data analyses is the detection of genes which are differentially expressed across different types of experimental conditions. We provide an extension of a finite mixture model to the clustering of genes and experimental conditions, where the partition of experimental conditions may be known or unknown. In particular, the idea is to adopt a finite mixture approach with mean/covariance reparameterization, where an explicit distinction among upregulated genes, down-regulated genes, non-regulated genes (with respect to a reference) is made; moreover, within each of these groups; genes that are differentially expressed between two or more types of experimental conditions may be identified.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.