Last updated: 2024-05-17

Checks: 5 1

Knit directory: SpinalCord_proteomics/analysis/

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In treated samples

Those proteins are the candidates to predict treatment response

At baseline (Visit 3)

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="1", Visit = "3"), perNA = 0.5)

[1] "Number of proteins: 377, number of samples: 74"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 377  74

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_baseline_treated"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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")

[1] "No sets passed the criteria"

plotList$plot

After treatment (visit 8)

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="1", Visit = "8"), perNA = 0.5)

[1] "Number of proteins: 377, number of samples: 66"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 377  66

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_visit8_treated"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  DT::datatable()

Correlation plot

exprMat <- assay(protSub)
pList <- lapply(seq(20), function(i) {
    rec <- resTab[i,]
    plotTab <- tibble(expr = exprMat[rec$name,],
                      nodeGroup = protSub$nodeGroup,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

At endpoint (visit 10)

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="1", Visit = "10"), perNA = 0.5)

[1] "Number of proteins: 375, number of samples: 62"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 375  62

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_visit10_treated"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

Change between baseline and after treatment (visit 8 - visit3)

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment = "1", Visit = c(3,8)), perNA = 0.5)

[1] "Number of proteins: 378, number of samples: 140"

protSub.before <- protSub[, protSub$Visit %in% 3 & !is.na(protSub$delta_UEMS)]
protSub.after <- protSub[,protSub$Visit %in% 8 & !is.na(protSub$delta_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
assays(protSub)[[1]] <- assays(protSub.after)[[1]] - assays(protSub.before)[[1]]
assays(protSub)[[2]] <- assays(protSub.after)[[2]] - assays(protSub.before)[[2]]

#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] 378  61

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_diff83_treated"]] <- resTab

hist(resTab$pval)

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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

In treated samples from node group B

At baseline

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="1", Visit = "3", nodeGroup = "B"), perNA = 0.5)

[1] "Number of proteins: 379, number of samples: 36"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 379  36

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_baseline_treatedB"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

After treatment (visit 8)

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="1", Visit = "8", nodeGroup ="B"), perNA = 0.5)

[1] "Number of proteins: 375, number of samples: 30"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 375  30

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_visit8_treatedB"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  DT::datatable()

Correlation plot

exprMat <- assay(protSub)
pList <- lapply(seq(20), function(i) {
    rec <- resTab[i,]
    plotTab <- tibble(expr = exprMat[rec$name,],
                      nodeGroup = protSub$nodeGroup,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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")

[1] "No sets passed the criteria"

plotList$plot

At endpoint (visit 10)

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="1", Visit = "10", nodeGroup = "B"), perNA = 0.5)

[1] "Number of proteins: 372, number of samples: 29"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 372  29

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_visit10_treatedB"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

Change between baseline and after treatment (visit 8 - visit3)

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment = "1", Visit = c(3,8), nodeGroup="B"), perNA = 0.5)

[1] "Number of proteins: 377, number of samples: 66"

protSub.before <- protSub[, protSub$Visit %in% 3 & !is.na(protSub$delta_UEMS)]
protSub.after <- protSub[,protSub$Visit %in% 8 & !is.na(protSub$delta_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
assays(protSub)[[1]] <- assays(protSub.after)[[1]] - assays(protSub.before)[[1]]
assays(protSub)[[2]] <- assays(protSub.after)[[2]] - assays(protSub.before)[[2]]

#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] 377  28

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_diff83_treatedB"]] <- resTab

hist(resTab$pval)

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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

In untreated samples

Those proteins may predict natural recovery without drug

At baseline

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="0", Visit = "3"), perNA = 0.5)

[1] "Number of proteins: 382, number of samples: 42"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 382  42

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_baseline_control"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

At visit 8

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="0", Visit = "8"), perNA = 0.5)

[1] "Number of proteins: 377, number of samples: 41"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 377  41

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_visit8_control"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  DT::datatable()

Correlation plot

exprMat <- assay(protSub)
pList <- lapply(seq(20), function(i) {
    rec <- resTab[i,]
    plotTab <- tibble(expr = exprMat[rec$name,],
                      nodeGroup = protSub$nodeGroup,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

At endpoint (visit 10)

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="0", Visit = "10"), perNA = 0.5)

[1] "Number of proteins: 376, number of samples: 33"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 376  33

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_visit10_control"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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")

[1] "No sets passed the criteria"

plotList$plot

Change between baseline and visit 8 (visit 8 - visit3)

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment = "0", Visit = c(3,8)), perNA = 0.5)

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

protSub.before <- protSub[, protSub$Visit %in% 3 & !is.na(protSub$delta_UEMS)]
protSub.after <- protSub[,protSub$Visit %in% 8 & !is.na(protSub$delta_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
assays(protSub)[[1]] <- assays(protSub.after)[[1]] - assays(protSub.before)[[1]]
assays(protSub)[[2]] <- assays(protSub.after)[[2]] - assays(protSub.before)[[2]]

#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  35

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_diff83_control"]] <- resTab

hist(resTab$pval)

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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

In untreated samples from group B

At baseline

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="0", Visit = "3", nodeGroup="B"), perNA = 0.5)

[1] "Number of proteins: 369, number of samples: 23"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 369  23

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_baseline_controlB"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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")

[1] "No sets passed the criteria"

plotList$plot

At visit 8

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="0", Visit = "8", nodeGroup="B"), perNA = 0.5)

[1] "Number of proteins: 380, number of samples: 24"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 380  24

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_visit8_controlB"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  DT::datatable()

Correlation plot

exprMat <- assay(protSub)
pList <- lapply(seq(20), function(i) {
    rec <- resTab[i,]
    plotTab <- tibble(expr = exprMat[rec$name,],
                      nodeGroup = protSub$nodeGroup,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

At endpoint (visit 10)

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment ="0", Visit = "10", nodeGroup="B"), perNA = 0.5)

[1] "Number of proteins: 368, number of samples: 21"

#protSub <- seProt_corr[,seProt_corr$Treatment %in% 1 & seProt_corr$Visit %in% 3 & !is.na(seProt_corr$delta_UEMS)]

print("How many proteins and samples")

[1] "How many proteins and samples"

dim(protSub)

[1] 368  21

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_visit10_controlB"]] <- resTab
hist(resTab$pval)

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, n_obs) %>%
  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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

Change between baseline and visit 8 (visit 8 - visit3)

Subsetting

#subset for patients in the placebo group
protSub <- prepareProt(seProt_corr, filterCondi = list(Treatment = "0", Visit = c(3,8), nodeGroup="B"), perNA = 0.5)

[1] "Number of proteins: 375, number of samples: 47"

protSub.before <- protSub[, protSub$Visit %in% 3 & !is.na(protSub$delta_UEMS)]
protSub.after <- protSub[,protSub$Visit %in% 8 & !is.na(protSub$delta_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
assays(protSub)[[1]] <- assays(protSub.after)[[1]] - assays(protSub.before)[[1]]
assays(protSub)[[2]] <- assays(protSub.after)[[2]] - assays(protSub.before)[[2]]

#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] 375  21

Differential expression

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

resTab <- testDiff(protSub, design, coef = "delta_UEMS", assayName = "imputed")

allResList[["corrOutcome_diff83_controlB"]] <- resTab

hist(resTab$pval)

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,
                      delta_UEMS = protSub$delta_UEMS)
    ggplot(plotTab, aes(x=delta_UEMS, y=expr)) +
        geom_point(aes(col = nodeGroup)) +
        ggtitle(sprintf("%s (P=%s)",rec$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

Compare the results between treated and untreated

This may help identify treatment specific protein changes

Baseline

treatTab <- allResList$corrOutcome_baseline_treated %>%
    mutate(condi = "treated")
controlTab <- allResList$corrOutcome_baseline_control %>%
    mutate(condi = "control")

compareTab <- bind_rows(treatTab, controlTab) 
fcTab <- select(compareTab, name, symbol, diff, condi) %>%
    pivot_wider(names_from = condi, values_from = diff) %>%
    dplyr::rename(coef_treated = treated, coef_control = control)
pTab <-  select(compareTab, name, symbol, pval, condi) %>%
    pivot_wider(names_from = condi, values_from = pval)%>%
    dplyr::rename(p_treated = treated, p_control = control)

plotTab <- left_join(fcTab, pTab, by = c("name","symbol")) %>%
    mutate(sig = case_when(
        p_treated < 0.05 & p_control < 0.05 ~ "both",
        p_treated < 0.05 & p_control > 0.05 ~ "only_treated",
        p_treated > 0.05 & p_control < 0.05 ~ "only_control",
        TRUE ~ "none",

    ))

ggplot(plotTab, aes(x=coef_control, y = coef_treated)) +
    geom_point(aes(color = sig)) +
    geom_abline(slope = 1, intercept = 0, color = "red", linetype ="dashed") +
    xlim(-0.06,0.06) +
    ylim(-0.06,0.06) +
    scale_color_manual(values = list(none = "grey", both = "green", only_treated = "red", only_control = "blue")) +
    ggrepel::geom_text_repel(data = filter(plotTab, sig != "none"), aes(label = symbol, color = sig)) +
    theme_full

Visit 8

treatTab <- allResList$corrOutcome_visit8_treated %>%
    mutate(condi = "treated")
controlTab <- allResList$corrOutcome_visit8_control %>%
    mutate(condi = "control")

compareTab <- bind_rows(treatTab, controlTab) 
fcTab <- select(compareTab, name, symbol, diff, condi) %>%
    pivot_wider(names_from = condi, values_from = diff) %>%
    dplyr::rename(coef_treated = treated, coef_control = control)
pTab <-  select(compareTab, name, symbol, pval, condi) %>%
    pivot_wider(names_from = condi, values_from = pval)%>%
    dplyr::rename(p_treated = treated, p_control = control)

plotTab <- left_join(fcTab, pTab, by = c("name","symbol")) %>%
    mutate(sig = case_when(
        p_treated < 0.05 & p_control < 0.05 ~ "both",
        p_treated < 0.05 & p_control > 0.05 ~ "only_treated",
        p_treated > 0.05 & p_control < 0.05 ~ "only_control",
        TRUE ~ "none",

    ))

ggplot(plotTab, aes(x=coef_control, y = coef_treated)) +
    geom_point(aes(color = sig)) +
    geom_abline(slope = 1, intercept = 0, color = "red", linetype ="dashed") +
    xlim(-0.06,0.06) +
    ylim(-0.06,0.06) +
    scale_color_manual(values = list(none = "grey", both = "green", only_treated = "red", only_control = "blue")) +
    ggrepel::geom_text_repel(data = filter(plotTab, sig != "none"), aes(label = symbol, color = sig)) +
    theme_full

Difference between visit 8 and baseline (visit 3)

treatTab <- allResList$corrOutcome_diff83_treated %>%
    mutate(condi = "treated")
controlTab <- allResList$corrOutcome_diff83_control %>%
    mutate(condi = "control")

compareTab <- bind_rows(treatTab, controlTab) 
fcTab <- select(compareTab, name, symbol, diff, condi) %>%
    pivot_wider(names_from = condi, values_from = diff) %>%
    dplyr::rename(coef_treated = treated, coef_control = control)
pTab <-  select(compareTab, name, symbol, pval, condi) %>%
    pivot_wider(names_from = condi, values_from = pval)%>%
    dplyr::rename(p_treated = treated, p_control = control)

plotTab <- left_join(fcTab, pTab, by = c("name","symbol")) %>%
    mutate(sig = case_when(
        p_treated < 0.05 & p_control < 0.05 ~ "both",
        p_treated < 0.05 & p_control > 0.05 ~ "only_treated",
        p_treated > 0.05 & p_control < 0.05 ~ "only_control",
        TRUE ~ "none",

    ))

ggplot(plotTab, aes(x=coef_control, y = coef_treated)) +
    geom_point(aes(color = sig)) +
    geom_abline(slope = 1, intercept = 0, color = "red", linetype ="dashed") +
    xlim(-0.06,0.06) +
    ylim(-0.06,0.06) +
    scale_color_manual(values = list(none = "grey", both = "green", only_treated = "red", only_control = "blue")) +
    ggrepel::geom_text_repel(data = filter(plotTab, sig != "none"), aes(label = symbol, color = sig)) +
    theme_full

Comparison of number of significant associations

listSigProt <- list(
    Baseline_treated = filter(allResList$corrOutcome_baseline_treated, pval < 0.05)$symbol,
     Baseline_control = filter(allResList$corrOutcome_baseline_control, pval < 0.05)$symbol,
     Visit8_treated = filter(allResList$corrOutcome_visit8_treated, pval < 0.05)$symbol,
     Visit8_control = filter(allResList$corrOutcome_baseline_control, pval < 0.05)$symbol,
     Diff_treated = filter(allResList$corrOutcome_diff83_treated, pval < 0.05)$symbol,
     Diff_control = filter(allResList$corrOutcome_diff83_control, pval < 0.05)$symbol
)

UpSetR::upset(UpSetR::fromList(listSigProt), nsets = 6)

Compare the results between treated and untreated (only in node group B)

Baseline

treatTab <- allResList$corrOutcome_baseline_treatedB %>%
    mutate(condi = "treated")
controlTab <- allResList$corrOutcome_baseline_controlB %>%
    mutate(condi = "control")

compareTab <- bind_rows(treatTab, controlTab) 
fcTab <- select(compareTab, name, symbol, diff, condi) %>%
    pivot_wider(names_from = condi, values_from = diff) %>%
    dplyr::rename(coef_treated = treated, coef_control = control)
pTab <-  select(compareTab, name, symbol, pval, condi) %>%
    pivot_wider(names_from = condi, values_from = pval)%>%
    dplyr::rename(p_treated = treated, p_control = control)

plotTab <- left_join(fcTab, pTab, by = c("name","symbol")) %>%
    mutate(sig = case_when(
        p_treated < 0.05 & p_control < 0.05 ~ "both",
        p_treated < 0.05 & p_control > 0.05 ~ "only_treated",
        p_treated > 0.05 & p_control < 0.05 ~ "only_control",
        TRUE ~ "none",

    ))

ggplot(plotTab, aes(x=coef_control, y = coef_treated)) +
    geom_point(aes(color = sig)) +
    geom_abline(slope = 1, intercept = 0, color = "red", linetype ="dashed") +
    xlim(-0.06,0.06) +
    ylim(-0.06,0.06) +
    scale_color_manual(values = list(none = "grey", both = "green", only_treated = "red", only_control = "blue")) +
    ggrepel::geom_text_repel(data = filter(plotTab, sig != "none"), aes(label = symbol, color = sig)) +
    theme_full

Visit 8

treatTab <- allResList$corrOutcome_visit8_treatedB %>%
    mutate(condi = "treated")
controlTab <- allResList$corrOutcome_visit8_controlB %>%
    mutate(condi = "control")

compareTab <- bind_rows(treatTab, controlTab) 
fcTab <- select(compareTab, name, symbol, diff, condi) %>%
    pivot_wider(names_from = condi, values_from = diff) %>%
    dplyr::rename(coef_treated = treated, coef_control = control)
pTab <-  select(compareTab, name, symbol, pval, condi) %>%
    pivot_wider(names_from = condi, values_from = pval)%>%
    dplyr::rename(p_treated = treated, p_control = control)

plotTab <- left_join(fcTab, pTab, by = c("name","symbol")) %>%
    mutate(sig = case_when(
        p_treated < 0.05 & p_control < 0.05 ~ "both",
        p_treated < 0.05 & p_control > 0.05 ~ "only_treated",
        p_treated > 0.05 & p_control < 0.05 ~ "only_control",
        TRUE ~ "none",

    ))

ggplot(plotTab, aes(x=coef_control, y = coef_treated)) +
    geom_point(aes(color = sig)) +
    geom_abline(slope = 1, intercept = 0, color = "red", linetype ="dashed") +
    xlim(-0.06,0.06) +
    ylim(-0.06,0.06) +
    scale_color_manual(values = list(none = "grey", both = "green", only_treated = "red", only_control = "blue")) +
    ggrepel::geom_text_repel(data = filter(plotTab, sig != "none"), aes(label = symbol, color = sig)) +
    theme_full

Difference between visit 8 and baseline (visit 3)

treatTab <- allResList$corrOutcome_diff83_treatedB %>%
    mutate(condi = "treated")
controlTab <- allResList$corrOutcome_diff83_controlB %>%
    mutate(condi = "control")

compareTab <- bind_rows(treatTab, controlTab) 
fcTab <- select(compareTab, name, symbol, diff, condi) %>%
    pivot_wider(names_from = condi, values_from = diff) %>%
    dplyr::rename(coef_treated = treated, coef_control = control)
pTab <-  select(compareTab, name, symbol, pval, condi) %>%
    pivot_wider(names_from = condi, values_from = pval)%>%
    dplyr::rename(p_treated = treated, p_control = control)

plotTab <- left_join(fcTab, pTab, by = c("name","symbol")) %>%
    mutate(sig = case_when(
        p_treated < 0.05 & p_control < 0.05 ~ "both",
        p_treated < 0.05 & p_control > 0.05 ~ "only_treated",
        p_treated > 0.05 & p_control < 0.05 ~ "only_control",
        TRUE ~ "none",

    ))

ggplot(plotTab, aes(x=coef_control, y = coef_treated)) +
    geom_point(aes(color = sig)) +
    geom_abline(slope = 1, intercept = 0, color = "red", linetype ="dashed") +
    xlim(-0.06,0.06) +
    ylim(-0.06,0.06) +
    scale_color_manual(values = list(none = "grey", both = "green", only_treated = "red", only_control = "blue")) +
    ggrepel::geom_text_repel(data = filter(plotTab, sig != "none"), aes(label = symbol, color = sig)) +
    theme_full

Comparison of number of significant associations

listSigProt <- list(
    Baseline_treated = filter(allResList$corrOutcome_baseline_treatedB, pval < 0.05)$symbol,
     Baseline_control = filter(allResList$corrOutcome_baseline_controlB, pval < 0.05)$symbol,
     Visit8_treated = filter(allResList$corrOutcome_visit8_treatedB, pval < 0.05)$symbol,
     Visit8_control = filter(allResList$corrOutcome_baseline_controlB, pval < 0.05)$symbol,
     Diff_treated = filter(allResList$corrOutcome_diff83_treatedB, pval < 0.05)$symbol,
     Diff_control = filter(allResList$corrOutcome_diff83_controlB, pval < 0.05)$symbol
)

UpSetR::upset(UpSetR::fromList(listSigProt), nsets = 6)

#Save result list
save(allResList, file = "../output/allResList.RData")

sessionInfo()

R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] forcats_0.5.1               stringr_1.4.1              
 [3] dplyr_1.1.4.9000            purrr_0.3.4                
 [5] readr_2.1.2                 tidyr_1.2.0                
 [7] tibble_3.2.1                ggplot2_3.4.1              
 [9] tidyverse_1.3.2             limma_3.52.2               
[11] proDA_1.10.0                SummarizedExperiment_1.26.1
[13] Biobase_2.56.0              GenomicRanges_1.48.0       
[15] GenomeInfoDb_1.32.2         IRanges_2.30.0             
[17] S4Vectors_0.34.0            BiocGenerics_0.42.0        
[19] MatrixGenerics_1.8.1        matrixStats_0.62.0         

loaded via a namespace (and not attached):
 [1] googledrive_2.0.0      fgsea_1.22.0           colorspace_2.0-3      
 [4] ellipsis_0.3.2         rprojroot_2.0.3        XVector_0.36.0        
 [7] fs_1.5.2               rstudioapi_0.13        farver_2.1.1          
[10] ggrepel_0.9.1          DT_0.23                fansi_1.0.6           
[13] lubridate_1.8.0        xml2_1.3.3             codetools_0.2-18      
[16] splines_4.2.0          cachem_1.0.6           knitr_1.39            
[19] jsonlite_1.8.3         workflowr_1.7.0        broom_1.0.0           
[22] dbplyr_2.2.1           BiocManager_1.30.18    compiler_4.2.0        
[25] httr_1.4.3             backports_1.4.1        assertthat_0.2.1      
[28] Matrix_1.5-4           fastmap_1.1.0          gargle_1.2.0          
[31] cli_3.6.2              later_1.3.0            htmltools_0.5.4       
[34] tools_4.2.0            gtable_0.3.0           glue_1.7.0            
[37] GenomeInfoDbData_1.2.8 fastmatch_1.1-3        Rcpp_1.0.9            
[40] cellranger_1.1.0       jquerylib_0.1.4        vctrs_0.6.5           
[43] nlme_3.1-158           crosstalk_1.2.0        xfun_0.31             
[46] rvest_1.0.2            lifecycle_1.0.4        googlesheets4_1.0.0   
[49] zlibbioc_1.42.0        scales_1.2.0           BiocStyle_2.24.0      
[52] hms_1.1.1              promises_1.2.0.1       parallel_4.2.0        
[55] yaml_2.3.5             gridExtra_2.3          UpSetR_1.4.0          
[58] sass_0.4.2             stringi_1.7.8          highr_0.9             
[61] BiocParallel_1.30.3    rlang_1.1.3            pkgconfig_2.0.3       
[64] bitops_1.0-7           evaluate_0.15          lattice_0.20-45       
[67] htmlwidgets_1.5.4      labeling_0.4.2         cowplot_1.1.1         
[70] tidyselect_1.2.1       plyr_1.8.7             magrittr_2.0.3        
[73] R6_2.5.1               generics_0.1.3         DelayedArray_0.22.0   
[76] DBI_1.1.3              pillar_1.9.0           haven_2.5.0           
[79] withr_3.0.0            mgcv_1.8-40            RCurl_1.98-1.7        
[82] modelr_0.1.8           crayon_1.5.2           utf8_1.2.4            
[85] tzdb_0.3.0             rmarkdown_2.14         grid_4.2.0            
[88] readxl_1.4.0           data.table_1.14.8      git2r_0.30.1          
[91] reprex_2.0.1           digest_0.6.30          httpuv_1.6.6          
[94] munsell_0.5.0          bslib_0.4.1

Identify proteins associated with outcome, delta_UEMS

Junyan Lu

17 May 2024

In treated samples

At baseline (Visit 3)

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

After treatment (visit 8)

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

At endpoint (visit 10)

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

Change between baseline and after treatment (visit 8 - visit3)

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

In treated samples from node group B

At baseline

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

After treatment (visit 8)

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

At endpoint (visit 10)

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

Change between baseline and after treatment (visit 8 - visit3)

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

In untreated samples

At baseline

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

At visit 8

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

At endpoint (visit 10)

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

Change between baseline and visit 8 (visit 8 - visit3)

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

In untreated samples from group B

At baseline

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

At visit 8

Differential expression

Table of proteins passed raw P-value < 0.05

Correlation plot

Enrichment analysis

At endpoint (visit 10)

Differential expression

Table of proteins passed raw P-value < 0.05