I have obtained the RNA-seq results, which is in an excel file with all the fpkm values. May I know how do I check the gene expression profiles? Is there any software for this or I gotta manually plot graph using excel?
Since it's already in a spreadsheet you might want to consider saving it as a csv (comma separated values) and loading it into R. There's a ton of different analysis modules you can use there depending on what you're looking for. edgeR is the first that comes to mind, but google is your friend for all those options and the details of each.
Hello!
As far as I know, FPKM are supposed to be proportional to the gene expression. After proper normalization you should be able to analyze these values with excel (although it can be a very boring work). If you are looking for a single gene or a small group of genes, then excel may be enough. If you are looking for gene expression signatures or something like that, you can have a look at limma package for R. If you are not familiar with R you could try and download MeV (Multiple Experiment Viewer) or to use its webapp at http://mev.tm4.org/.
It all depends on what kind of analysis you need, but these tools I mentioned should cover the most common tasks.
Best of luck!
Hello,
It would be much better if you will go for manual analysis (excel sheet format). As in that case you can select total significant data based on P- and FDR values much suited to our data.
Good luck
Please read some review papers like the one given below which will guide you on how to proceed
http://chagall.med.cornell.edu/RNASEQcourse/Intro2RNAseq.pdf
Since it's already in a spreadsheet you might want to consider saving it as a csv (comma separated values) and loading it into R. There's a ton of different analysis modules you can use there depending on what you're looking for. edgeR is the first that comes to mind, but google is your friend for all those options and the details of each.
Among the tools, limma has been shown to perform well under many circumstances and it is also the fastest to run. DESeq and edgeR perform similarly in ranking genes but are often relatively conservative or too liberal, respectively, in controlling FDR . SAMseq performs well in terms of FDR but presents an acceptable sensitivity when the number of replicates is relatively high, at least 10 . NOISeq and NOISeqBIO (the adaptation of NOISeq for biological replication) are more efficient in avoiding false positive calls at the cost of some sensitivity but perform well with different numbers of replicates . BitSeq compares favorably to other transcript-level packages such as Cuffdiff2 .Besides the actual performance, other issues affecting the choice of the tool include ease of installation and use, computational requirements, and quality of documentation and instructions. Finally, an important consideration when choosing an analysis method is the experimental design.
I concur with Seth Munholland, loading it into R would be the best starting point. In addition to differential analysis which many of the members have suggested, I would also perform enrichment analysis (GSEA/GSVA) and look for pathways involved (Ref: https://bioconductor.org/packages/release/bioc/html/GSVA.html).
So far I do not have duplicate or triplicate results, this means I can't perform significant test right?
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