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  • statistical justification of partitioning dataset for multiple analysis (EdgeR)

    Dear all,

    I'm doing analysis on allready mapped reads from sequencing data for differential expression with EdgeR. My experimental setup is as follow. I have samples from 4 different subjects. Material of each subject wast treated with 2 different treatments (and a control) for 2 timepoints.

    I want to analyse the effect of the treatments (compared to control and compared to eachother)

    In EdgeR I used following design
    model.matrix(~ subject+ Treatment + Time +Treatment : Time)

    I considered 2 strategies to analye te data:
    Estimate parameters from above mentioned design with all data (all samples) and use different contrasts to get the differential expressed genes I want.

    OR

    Use only the samples of the two treatments (eg. control vs treatment1, treatment 1 vs treatment 2) I want to compare to fit the parameters. So using only 2/3 of all data. Repeat the previous 3 times till I have compared all 3 treatments with eachother. So exctually 3 different analysis using only a subset of the data.

    I noticed that I could find considerably more significant differential expressed genes between 2 treatments with the last approach. But I wondered how correct this approach is? Will I have for example problems with multiple testing? (I control each analysis on fdr 5% with bejamin Hochberg)

    thanks in advance
    Kind regards

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