Part E
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,noah,NovaArchSys,30.09.2022 12:12,file:///home/noah/.config/libreoffice/4;
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,noah,NovaArchSys,30.09.2022 12:27,file:///home/noah/.config/libreoffice/4;
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@ -130,3 +130,12 @@ p <- ggplot(mytopgene.data.df, aes(x=phenotype, y=mygene, fill=phenotype)) +
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stat_boxplot(geom ='errorbar') + geom_boxplot()
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stat_boxplot(geom ='errorbar') + geom_boxplot()
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p <- p + xlab("MDD versus HC") + ylab(mygene)
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p <- p + xlab("MDD versus HC") + ylab(mygene)
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p
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p
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#### Part E: Interpretation of top genes
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top_cutoff <- 200
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top_genes <- as.character(ttest_allgene.sorted[1:top_cutoff,1])
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write.table(top_genes, sep="\t", file="", quote=F, row.names=F, col.names=F)
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#### Optional 1: Identify Outliers
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