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Transcriptomic analysis are characterized by being not directly quantitativeand only providing relative measurements of expression levels up to an unknownindividual scaling factor. This difficulty is enhanced for differentialexpression analysis. Several methods have been proposed to circumvent this lackof knowledge by estimating the unknown individual scaling factors however, eventhe most used one, are suffering from being built on hardly justifiablebiological hypotheses or from having weak statistical background. Only twomethods withstand this analysis: one based on largest connected graph componenthardly usable for large amount of expressions like in NGS, the second based on$\log$-linear fits which unfortunately require a first step which uses one ofthe methods described before.  We introduce a new procedure for differential analysis in the context oftranscriptomic data. It is the result of pooling together several differentialanalyses each based on randomly picked genes used as reference genes. Itprovides a differential analysis free from the estimation of the individualscaling factors or any other knowledge. Theoretical properties are investigatedboth in term of FWER and power. Moreover in the context of Poisson or negativebinomial modelization of the transcriptomic expressions, we derived a test withnon asymptotic control of its bounds. We complete our study by some empiricalsimulations and apply our procedure to a real data set of hepatic miRNAexpressions from a mouse model of non-alcoholic steatohepatitis (NASH), theCDAHFD model. This study on real data provides new hits with good biologicalexplanations.

Differential analysis in Transcriptomic: The strength of randomly picking 'reference' genes