[BioC] short time-course design. Any suggestion?
ramasamy at cancer.org.uk
Tue Jul 20 20:00:37 CEST 2004
a) Are you interested in the difference in cell lines over times OR
b) are you treating the different cell lines as biological replicates
Assuming the latter, you have a oneway anova with time as a main factor
and 3 replicates at each time point.
I would suggest you try RMA and GC-RMA on the whole dataset first and
truncating your list later. The truncation at step 2 ignores more than
90% of the genes and your number of true positives will be quite low.
You can use GO tools (I think BioConductor have some packages to handle
these) on the final gene list to see if your favourite pathway is
On Tue, 2004-07-20 at 18:17, Naomi Altman wrote:
> You appear to have no replicates. Without replication you cannot do any
> statistical analysis such as ANOVA or limma.
> At 06:10 PM 7/19/2004 +0000, Stefano Calza wrote:
> >Hi everybody.
> >I'm looking at a small experiment with 12 chips (Affy), from 3 different
> >cell lines measured at 4 different time points (0,2 hours, 8 h, 24 h).
> >1) mas5 expression values
> >2) selected about 1500 genes (out of ~22000) using GO annotations for
> >those BP of possible interest
> >3) selected genes with at least 25% Presence/Calls (I know this is quite
> >4) ANOVA using gls with Compound Symmetry correlation structure
> >5) p value corrected either using p.adjust(...,"fdr") or computing Q values.
> >I actually get few "significant" genes and mostly with low fold-change
> >(relative to time 0) and overall low expression intensities.
> >Any objection about all this and/or any suggestion for improvement?
> >Thanks in advance,
> >Bioconductor mailing list
> >Bioconductor at stat.math.ethz.ch
> Naomi S. Altman 814-865-3791 (voice)
> Associate Professor
> Bioinformatics Consulting Center
> Dept. of Statistics 814-863-7114 (fax)
> Penn State University 814-865-1348 (Statistics)
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