I have 3 genotypes, sampled at three different time points. (de novo transcriptomes assembled for each using Trinity).
The first time point, consisiting of just controls, the second and third time points feature two sets of treatments.
Three biological replicates were taken at each sampling point, so I now have 45 samples which may or may not cluster according to genotype, time and/or treatment.
With so many aspects to consider, can anyone advise how best to visualize (MDS 'v' clustering tools for example) the DGE data I have (processed through EdgeR), and with what software?
Do I understand it correctly: Timepoint1 --treatment1-> Timepoint2 --treatment2--> Timepoint3 ? Which means you have paried data (like a time-series).
Or, do you simply just have 3 groups in 3 conditions (different samples in each group and condition)?
Dear Lori,
Are you familiar with SPSS?
If yes, maybe you could try using a linear mixed model.
You could determine if the gene expression depends on the genotype and on the time points.
Best.
Gilles
Dear Lori,
We have developed the transcriptogramer method, for global views of transcritograms.
The liink is http://lief.if.ufrgs.br/pub/biosoftwares/transcriptogramer/
and I would be happy to assist you if you are interested. I have also attached a paper we just submitted, but on the site you will also find some publications.
Thank you for your replies. Sindre I have three time points, T0C, T1C, T1D, T2C, T2D, with only one treatment applied to the first, two for the others (water deficit). Gilles, I think you may be right but didn't think SPSS would be up to the job when it came to visualising the data, I had been looking at web based applications, including VENNT, EXPANDER AND J-Express.
Thank you Rita for your application link, I will have a read through and get back to you.
Dear Lori,
Effectively, if SPSS provides a lot of information on the stats of your sample, I would use another software to vizualize the data. I don't know those web based applications.
Best
Gilles
Dear Lori,
For differential gene expression visulazation use BioConductor package of R-programming language
Regards
I suggest clustering your data using K-means using GeneCluster tool, then you can easily visualize the results using Java Tree View. I have tried that and that perfectly works. Both tools are freely available.
Find differentially expressed genes in RNA-Seq data
http://www.genomespace.org/support/guides/recipes/sections/find-differentially-expressed-genes-in-rna-seq-data
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