An important challenge in microarray data analysis is the detection of genes which are differentially expressed across different types of experimental conditions. We provide a nite mixture model aimed at clustering genes and experimental conditions, where the partition of experimental conditions may be known or unknown. In particular, the idea is to adopt a nite mixture approach with mean/covariance reparameterization, where an explicit distinction among upregulated genes, down-regulated genes, non-regulated genes (with respect to a reference probe) is made; moreover, within each of these groups genes that are differentially expressed between two or more types of experimental conditions may be identified.
moreover, within each of these groups genes that are differentially expressed between two or more types of experimental conditions may be identified.; An important challenge in microarray data analysis is the detection of genes which are differentially expressed across different types of experimental conditions. We provide a finite mixture model aimed at clustering 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 up-regulated genes, down-regulated genes, non-regulated genes (with respect to a reference probe) is made
Identifying partitions of genes and tissue samples in microarray data / Martella, Francesca; Alfo', Marco. - (2011), pp. 455-462. [10.1007/978-3-642-11363-5_51].
Identifying partitions of genes and tissue samples in microarray data
MARTELLA, Francesca;ALFO', Marco
2011
Abstract
An important challenge in microarray data analysis is the detection of genes which are differentially expressed across different types of experimental conditions. We provide a nite mixture model aimed at clustering genes and experimental conditions, where the partition of experimental conditions may be known or unknown. In particular, the idea is to adopt a nite mixture approach with mean/covariance reparameterization, where an explicit distinction among upregulated genes, down-regulated genes, non-regulated genes (with respect to a reference probe) 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.
