Vai ai contenuti. | Spostati sulla navigazione | Spostati sulla ricerca | Vai al menu | Contatti | Accessibilità

| Crea un account

Di Camillo, Barbara - Toffolo, Gianna - Nair, Sreekumaran K - Greenlund, Laura J - Cobelli, Claudio (2007) Significance analysis of microarray transcript levels in time series experiments. [Articolo di periodico (online)]

Full text disponibile come:

[img]
Anteprima
Documento PDF
638Kb

Per gentile concessione di: http://www.biomedcentral.com/1471-2105/8/S1/S10

Abstract (inglese)

Background

Microarray time series studies are essential to understand the dynamics of molecular events. In order to limit the analysis to those genes that change expression over time, a first necessary step is to select differentially expressed transcripts. A variety of methods have been proposed to this purpose; however, these methods are seldom applicable in practice since they require a large number of replicates, often available only for a limited number of samples. In this data-poor context, we evaluate the performance of three selection methods, using synthetic data, over a range of experimental conditions. Application to real data is also discussed.

Results

Three methods are considered, to assess differentially expressed genes in data-poor conditions. Method 1 uses a threshold on individual samples based on a model of the experimental error. Method 2 calculates the area of the region bounded by the time series expression profiles, and considers the gene differentially expressed if the area exceeds a threshold based on a model of the experimental error. These two methods are compared to Method 3, recently proposed in the literature, which exploits splines fit to compare time series profiles. Application of the three methods to synthetic data indicates that Method 2 outperforms the other two both in Precision and Recall when short time series are analyzed, while Method 3 outperforms the other two for long time series.

Conclusion

These results help to address the choice of the algorithm to be used in data-poor time series expression study, depending on the length of the time series.


Statistiche Download - Aggiungi a RefWorks
Tipo di EPrint:Articolo di periodico (online)
Anno di Pubblicazione:2007
Parole chiave (italiano / inglese):microarray, dynamics of molecular events
Settori scientifico-disciplinari MIUR:Area 06 - Scienze mediche > MED/03 Genetica medica
Struttura di riferimento:Dipartimenti > Dipartimento di Ingegneria dell'Informazione
Codice ID:1223
Depositato il:09 Dic 2008
Simple Metadata
Full Metadata
EndNote Format

Download statistics

Solo per lo Staff dell Archivio: Modifica questo record