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ALL <- read.csv('figs/ALL.csv', row.names = 1, na.strings = c("NA", ".")) |
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ALL$score[ALL$score==0] <- -1 |
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library(ggplot2) |
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good.genes <- c("P15056", "P04637", "Q09428", "Q14654", "O00555", "Q99250", "Q14524", 'P07949') |
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bad.genes <- c('P21802') |
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ALL$plot.label <- ALL$uniprotID |
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ALL$plot.label[!ALL$uniprotID %in% c(good.genes, bad.genes)] <- NA |
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ALL$plot.label[ALL$uniprotID %in% c(bad.genes)] <- 'Bad Genes' |
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|
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ALL$plot.label[ALL$uniprotID %in% c(good.genes)] <- 'Good Genes' |
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ALL$score.label <- ALL$score |
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ALL$score.label[ALL$score == -1] <- 'LoF' |
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ALL$score.label[ALL$score == 1] <- 'GoF' |
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wil.stat <- wilcox.test(ALL$pLDDT[!is.na(ALL$plot.label) & ALL$plot.label=='Good Genes' & ALL$score==-1], |
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ALL$pLDDT[!is.na(ALL$plot.label) & ALL$plot.label=='Good Genes' & ALL$score==1]) |
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p1 <- ggplot(ALL[!is.na(ALL$plot.label) & ALL$plot.label=='Good Genes',], |
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aes(x=pLDDT, col=score.label)) + geom_density() + |
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ggpp::geom_text_npc(data=data.frame(x="left", y="middle", |
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label=paste0("Mann-Whitney test p=", signif(wil.stat$p.value, digits = 2))), |
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aes(npcx=x, npcy=y, label=label), |
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col='black') + |
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theme_bw() + xlim(0, 100) + |
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ggtitle('Structure increase prediction: site pLDDT') + ggeasy::easy_center_title() |
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wil.stat <- wilcox.test(ALL$pLDDT[!is.na(ALL$plot.label) & ALL$plot.label=='Bad Genes' & ALL$score==-1], |
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ALL$pLDDT[!is.na(ALL$plot.label) & ALL$plot.label=='Bad Genes' & ALL$score==1]) |
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p5 <- ggplot(ALL[!is.na(ALL$plot.label) & ALL$plot.label=='Bad Genes',], |
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aes(x=pLDDT, col=score.label)) + geom_density() + |
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ggpp::geom_text_npc(data=data.frame(x="left", y="middle", |
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label=paste0("Mann-Whitney test p=", signif(wil.stat$p.value, digits = 2))), |
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aes(npcx=x, npcy=y, label=label), |
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col='black') + |
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theme_bw() + xlim(0, 100) + |
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ggtitle('Structure worsen prediction: site pLDDT') + ggeasy::easy_center_title() |
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|
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library(patchwork) |
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p <- (p1 + p5) + plot_layout(ncol = 2) |
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ggsave(filename = 'figs/fig.sup.8c.pdf', p, width = 12, height = 2) |
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