I am looking for a way that allows me to pinpoint genes that are significantly up- or down-regulated among such samples during the development (maturation) of a fruit. I thought of DESeq, but it allows, if I'm not wrong, only pairwise correlations between discrete samples, not series. Data were obtained with RNAseq.
Bayesian methods have been enormously helpful in analyzing temporally regulated genes from microarray datasets. See "An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation)" http://www.biomedcentral.com/1471-2105/10/409 for example (additional examples abound in the literature).
In principle, I think you could apply the R package developed in the above paper to your RNA-Seq datasets if you convert your data to a single metric per gene locus.
I agree it is critical that you avoid simply looking at pairwise comparisons if the important biology is the overall temporal trend.
Thanks a lot, I'll try to apply the approach you suggested. I also found that cuffdiff has a time-series flag; do you think it may be worth a try?
I haven't used cuffdiff, but there is some discussion about it at this thread:
https://www.researchgate.net/post/What_is_your_favorite_DEG_test_for_RNA-seq_data
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