Last updated: 2024-05-17

Checks: 5 1

Knit directory: SpinalCord_proteomics/analysis/

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In untreated samples

All random node groups

Subsetting

protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment = "0", Visit = c(3,8)), perNA = 0.5)

[1] "Number of proteins: 379, number of samples: 83"

#subset for patients in the placebo group
protSub.before <- protSub[,protSub$Treatment %in% 0 & protSub$Visit %in% 3 & !is.na(protSub$UEMS)]
protSub.after <- protSub[,protSub$Treatment %in% 0 & protSub$Visit %in% 8 & !is.na(protSub$UEMS)]

overPat <- intersect(protSub.after$PSN, protSub.before$PSN)
protSub.before <- protSub.before[,match(overPat, protSub.before$PSN)]
protSub.after <- protSub.after[,match(overPat, protSub.after$PSN)]
colnames(protSub.after) <- overPat
colnames(protSub.before) <- overPat

protSub <- protSub.before
assay(protSub) <- assay(protSub.after) - assay(protSub.before)
protSub$UEMS_change <- protSub.after$UEMS - protSub.before$UEMS

#remove feature with too many missings
#protSub <- protSub[rowSums(is.na(assay(protSub)))/ncol(protSub) < 0.5,]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 379  37

Differential expression

design <- model.matrix(~ UEMS_change, colData(protSub))


resTab <- testDiff(protSub, design, coef = "UEMS_change", assayName = "imputed")
hist(resTab$pval)

allResList[["corr_UEMS_control"]] <- resTab

Table of proteins passed raw P-value < 0.05

filter(resTab, pval <= 0.05) %>% mutate(across(where(is.numeric), formatC, digits=2)) %>%
  select(name, symbol, pval, adj_pval, diff) %>%
  DT::datatable()

Correlation plot

exprMat <- assays(protSub)[[2]]
pList <- lapply(seq(20), function(i) {
    rec <- resTab[i,]
    plotTab <- tibble(expr = exprMat[rec$name,],
                      nodeGroup = protSub$nodeGroup,
                      UEMS_change = protSub$UEMS_change)
    ggplot(plotTab, aes(x=UEMS_change, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$symbol,formatC(rec$pval, digits = 2))) +
        #scale_color_gradient(low="green",high="red") +
        geom_smooth(method = "lm") +
        theme_bw() +
        theme(plot.title = element_text(hjust = 0.5, face = "bold")) 
})

cowplot::plot_grid(plotlist = pList, ncol=4)

Enrichment analysis

gmts = list(GO_BiologicalProcess = "../data/gmts/c5.go.bp.v2023.2.Hs.symbols.gmt",
            GO_MolecularFunction = "../data/gmts/c5.go.mf.v2023.2.Hs.symbols.gmt")
plotList <- runGeneSetEnrichment(resTab, gmts, genePCut  = 1, pCutSet = 0.05, setFdr = FALSE, method = "gsea")
plotList$plot