I am trying to analyze miRNA data from the platform NanoString ncounter to identify miRNAs differentially expressed (DE) in human serum samples. I think that the data that comes from this platform is very similar to RNA-seq counts data, because the results are the number of counts in aprox. 800 miRNAs for each sample. So I want to know if I could use RNA-Seq bioinformatics tools to identify the DE miRNAs in three conditions, because I think that some considerations should be taken into account:
Although the panel can detect 800 miRNAs, I have only detected an average of 30 endogenous miRNAs in each serum sample (could be due to the low amounts of miRNAs in the blood), of which most have <100 counts (with a substraction of the mean of the negative controls plus two standard desviations) . Because of that, I think that all the methods that are based on global expression would not be appropiated. Also, at the moment, there isn't a valid endogenous housekeeping miRNA when analyzing circulating miRNAs, so I think that the best would be to normalize data with 5 miRNA Spike-Ins (in different concentrations) that I added to the samples prior to extraction. Or maybe you could suggest me a better way to normalize this data.
So I would like to know if current RNA-Seq tools could be adapted to analyze this kind of data. I would also appreciate your commenting on useful tools that I could use for an exploratory analysis (eg volcano plot, heatmap, etc). I have few experience in Bioinformatics, although I have some notions of programming in R, so I hope that I could learn how to use some tools to study this kind of data. Any kind of tutorials or resources that you recommend me will be very helpful.
Thanks you in advance,