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Data evaluation, normalisation and transformation was performed in Microsoft Excel® (http://www.microsoft.com).
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Data evaluation included background correction (substraction of minimum value) and normalisation to reference genes.
Data evaluation included background correction (subtraction of minimum value) and normalisation to reference genes.
B.B. performed the simulations and data evaluations.
However, proper and highly reliable reference genes are needed for normalisation of data, as normalisation by total pathogen RNA in mixed host-pathogen samples is usually not possible.
Cluster analysis of the data without normalisation confirmed that all samples were highly correlated with R>0.945.
Data normalisation was performed across all arrays using quantile normalisation.
Data normalisation and differential expression analysis was carried out using edgeR62.
Data normalisation was performed using the Gapdh mRNA level in each sample.
Thus data pretreatment, or normalisation, has been recognised as a major challenge in the analysis of microarray data.
Reliable RNA transcriptional differences between Dd2 and HB3 could be readily visualised using public algorithms for data normalisation and clustering.
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