BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:The use of a power law global error model for the identification o
 f differentially expressed genes in microarray data - Matthew Russell\, Bi
 ochemistry Department
DTSTART:20071130T140000Z
DTEND:20071130T150000Z
UID:TALK9139@talks.cam.ac.uk
CONTACT:Dr N Karp
DESCRIPTION:Matthew Russell will be presenting a paper on the use of a pow
 er law global error model for the identification of differentially express
 ed genes in microarray data.  \n\nPaper details: Pavelka\, N.\, M. Pelizzo
 la\, et al. (2004). BMC Bioinformatics 5(1): 203.\n\nAbstract\nBACKGROUND:
 High-density oligonucleotide microarray technology enables the discovery o
 f genes that are transcriptionally modulated in different biological sampl
 es due to physiology\, disease or intervention. Methods for the identifica
 tion of these so-called "differentially expressed genes" (DEG)would largel
 y benefit from a deeper knowledge of the intrinsic measurement variability
 . Though it is clear that variance of repeated measures is highly dependen
 t on the average expression level of a given gene\, there is still a lack 
 of consensus on how signal reproducibility is linked to signal intensity. 
 The aim of this study was to empirically model the variance versus mean de
 pendence in microarray data to improve the performance of existing methods
  for identifying DEG.RESULTS:In the present work we used data generated by
  our lab as well as publicly available data sets to show that dispersion o
 f repeated measures depends on location of the measures themselves followi
 ng a power law. This enables us to construct a power law global error mode
 l (PLGEM) that is applicable to various Affymetrix GeneChip data sets. A n
 ew DEG identification method is therefore proposed\, consisting of a stati
 stic designed to make explicit use of model-derived measurement spread est
 imates and a resampling-based hypothesis testing algorithm.CONCLUSIONS:The
  new method provides a control of the false positive rate\, a good sensiti
 vity vs. specificity trade-off and consistent results with varying number 
 of replicates and even using single samples.\n
LOCATION:Meeting room 1 cambridge system biology centre
END:VEVENT
END:VCALENDAR