Abstract
In this chapter, we discuss the Transcriptogram method for statistically analyzing differential gene expression in a genome-wide profile. This technique suggests a method to hierarchically interrogate the data and, subsequently, narrow down to gene level. We present the method, discuss its reproducibility and enhanced signal-to-noise ratio, and discuss its application in investigating time series data as in cell cycle, therapy gene target identification, lineage and tissue classification and as a powerful test to identify error and assess the quality of normalization procedures. We finally present the software ready for download and discuss the R-plugin for BioConductor.
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de Almeida, R.M.C., de Souza, L.L.S., Morais, D., Dalmolin, R.J.S. (2020). Transcriptograms: A Genome-Wide Gene Expression Analysis Method. In: da Silva, F.A.B., Carels, N., Trindade dos Santos, M., Lopes, F.J.P. (eds) Networks in Systems Biology. Computational Biology, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-030-51862-2_5
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DOI: https://doi.org/10.1007/978-3-030-51862-2_5
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