Last updated: 2020-04-25

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Subset for M-CLL samples

protCLL <- protCLL[,protCLL$IGHV.status %in% "M"]
protCLL$trisomy12 <- patMeta[match(colnames(protCLL),patMeta$Patient.ID),]$trisomy12

Preprocessing

Process proteomics data

protMat <- assays(protCLL)[["count"]] #without imputation

Prepare genomic background

Get Mutations with at least 3 cases

geneMat <-  patMeta[match(colnames(protMat), patMeta$Patient.ID),] %>%
  select(Patient.ID, del11p:U1) %>%
  mutate_if(is.factor, as.character) %>% 
  mutate_at(vars(-Patient.ID), as.factor) %>% #assign a few unknown mutated cases to wildtype
  data.frame() %>% column_to_rownames("Patient.ID")


geneMat <- geneMat[,apply(geneMat,2, function(x) sum(x %in% 1, na.rm = TRUE))>=3]

Mutations that will be tested

colnames(geneMat)

[1] "del13q"    "trisomy12" "trisomy19" "HIST1H1E"  "SF3B1"

Test if there’s interaction between gene mutations (potential confounders)

chiRes <- lapply(seq(1,ncol(geneMat)-1), function(i) {
  lapply(seq(i+1, ncol(geneMat)), function(j) {
    geneA <- colnames(geneMat)[i]
    geneB <- colnames(geneMat)[j]
    #res <- chisq.test(geneMat[,i],geneMat[,j])
    res <- fisher.test(table(geneMat[,i], geneMat[,j]))
    tibble(geneA = geneA, geneB=geneB, p = res$p.value)
  }) %>% bind_rows()
}) %>% bind_rows() %>% arrange(p) %>%
  filter(p <=0.05)
chiRes

# A tibble: 1 x 3
  geneA     geneB           p
  <chr>     <chr>       <dbl>
1 trisomy12 trisomy19 0.00593

Heatmap of mutations

plotMat <- apply(geneMat,2,as.numeric)
rownames(plotMat) <- rownames(geneMat)
plotMat <- data.frame(plotMat)
#A recursive function to sort table
sortTab <- function(sumTab) {
  i <- ncol(sumTab)
  #print(i)
  if (i == 1) {
    #print(rownames(sumTab)[order(sumTab[,i])])
    return(rownames(sumTab)[order(sumTab[,i])])
  }
  orderRow <- c(sortTab(sumTab[sumTab[,i] %in% c(0,NA), seq(1,i-1), drop = FALSE]), sortTab(sumTab[sumTab[,i] %in% 1, seq(1,i-1), drop = FALSE]))
  return(orderRow)
}

#order columns based on number of samples
plotMat <- plotMat[,order(colSums(plotMat, na.rm = TRUE))]

#sort rows based on completeness
sampleOrder <- sortTab(plotMat)
plotMat <- plotMat[rev(sampleOrder),]

pheatmap(t(plotMat), cluster_rows = FALSE, cluster_cols = FALSE)

Test for all variantions

Fit the probailistic dropout model and test for differentially expressed proteins

resList <- lapply(colnames(geneMat), function(n) {
  designMat <- geneMat[,n, drop =FALSE]
  designMat <- designMat[!is.na(designMat[[n]]),,drop=FALSE]
  testMat <- protMat[,rownames(designMat)]
  
  fit <- proDA(testMat, design = ~ .,
             col_data = designMat)
  
  contra <- paste0(n,"1")
  resTab <- test_diff(fit, contra) %>%
    dplyr::rename(id = name, logFC = diff, t=t_statistic,
                  P.Value = pval, adj.P.Val = adj_pval) %>% 
    mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
    select(name, id, logFC, t, P.Value, adj.P.Val) %>%  
    arrange(P.Value) %>% mutate(Gene = n) %>%
    as_tibble()
  resTab
}) %>% bind_rows()

P-value histogram for each genomic feature

ggplot(resList, aes(x=P.Value)) + geom_histogram(fill = "green", alpha =0.5, bins=30, col = "grey50") + facet_wrap(~Gene, ncol=3, scales = "fixed") + xlim(0,1)

Warning: Removed 10 rows containing missing values (geom_bar).

Number of significantly associated proteins at 10% FDR

proNumTab <- resList %>% group_by(Gene) %>%
  summarise(number = sum(adj.P.Val < 0.1, na.rm=TRUE)) %>%
  arrange(desc(number)) %>% mutate(Gene = factor(Gene, levels = Gene))
proNumTab

# A tibble: 5 x 2
  Gene      number
  <fct>      <int>
1 trisomy12    789
2 trisomy19    157
3 del13q         0
4 HIST1H1E       0
5 SF3B1          0

Based on the P-value histograms and numbers of significant associations, trisomy12 has the most impact on protein expression, followed by IGHV and del11q. Other genomic variations do not seem to have major impact on protein expression.

Visualize results

Trisomy12

List of significant proteins (10% FDR)

corRes.sig <- resList %>% filter(Gene == "trisomy12", adj.P.Val < 0.1) %>%
  mutate(chromosome = rowData(protCLL[id,])$chromosome_name) %>%
  select(-Gene)
corRes.sig %>% mutate_if(is.numeric, formatC, digits=2, format="e") %>% DT::datatable()

Volcano plot (0.1% FDR)

plotVolcano <- function(pTab, fdrCut = 0.05, posCol = "red", negCol = "blue",
                        x_lab = "dm", plotTitle = "",ifLabel = FALSE,
                        colLabel = NULL) {
  plotTab <- pTab %>% mutate(ifSig = ifelse(adj.P.Val > fdrCut, "n.s.",
                                            ifelse(logFC > 0, "up","down"))) %>%
    mutate(ifSig = factor(ifSig, levels = c("up","down","n.s.")))
  pCut <- -log10((filter(plotTab, ifSig != "n.s.") %>% arrange(desc(P.Value)))$P.Value[1])
  g <- ggplot(plotTab, aes(x=logFC, y=-log10(P.Value), label = name)) +
    geom_point(shape = 21, aes(fill = ifSig),size=3) +
    geom_hline(yintercept = pCut, linetype = "dashed") +
    annotate("text", x = -Inf, y = pCut, label = paste0(fdrCut*100,"% FDR"),
             size = 5, vjust = -1.2, hjust=-0.1) +
    scale_fill_manual(values = c(n.s. = "grey70",
                                  up = posCol, down = negCol)) +
    theme( legend.position = "bottom",
          legend.text = element_text(size = 15)) +
    ylab(expression(-log[10]*'('*italic(P)~value*')')) +
    xlab(x_lab) + ggtitle(plotTitle)

  if (ifLabel & is.null(colLabel))
    g <- g + ggrepel::geom_text_repel(data = filter(plotTab, ifSig != "n.s."),
                                      size=5, force = 2)
  else if (ifLabel & !is.null(colLabel)) {
     g <- g+ggrepel::geom_text_repel(data = filter(plotTab, ifSig != "n.s."),
                                    aes_string(col = colLabel),
                                    size=5, force = 2) +
       scale_color_manual(values = c(yes = "red",no = "black"))
  }

  return(g)
}

plotTab <- filter(resList, Gene == "trisomy12") %>%
  mutate(chromosome = rowData(protCLL[id,])$chromosome_name) %>%
  mutate(onChr12 = ifelse(chromosome == "12","yes","no"))
plotVolcano(plotTab, fdrCut =0.01, x_lab="log2FoldChange", 
            plotTitle = "trisomy12", ifLabel = TRUE, colLabel = "onChr12")

Labels colored by red indicates the gene is on chromosome 12

Heatmap of differentially expressed proteins (1%FDR)

proList <- filter(corRes.sig, !is.na(name),adj.P.Val < 0.01) %>% distinct(name, .keep_all = TRUE) %>% pull(id)
plotMat <- assays(protCLL)[["QRILC"]][proList,]
rownames(plotMat) <- rowData(protCLL[proList,])$hgnc_symbol

colAnno <- colData(protCLL)[,c("gender","trisomy12","IGHV.status")] %>%
  data.frame()

rowAnno <- rowData(protCLL)[proList,c("chromosome_name","hgnc_symbol"),drop=FALSE] %>% 
  data.frame(stringsAsFactors = FALSE) %>%
  mutate(onChr12 = ifelse(chromosome_name == "12","yes","no")) %>%
  select(hgnc_symbol, onChr12) %>% data.frame() %>% column_to_rownames("hgnc_symbol")

plotMat <- jyluMisc::mscale(plotMat, censor = 6)

pheatmap(plotMat, scale = "none", annotation_col = colAnno, clustering_method = "ward.D2",
         annotation_row = rowAnno,
         color = colorRampPalette(c("navy","white","firebrick"))(100),
         breaks = seq(-6,6, length.out = 101))

Plot top 9 most differentially expressed proteins

protTab <- sumToTiday(protCLL,"patID") %>% mutate(name = hgnc_symbol)

plotTab <- filter(protTab, name %in% corRes.sig$name[1:9]) %>% dplyr::rename(expression = QRILC) %>%
  filter(!is.na(trisomy12))
ggplot(plotTab, aes(x=trisomy12, y =  expression)) + geom_boxplot(aes(fill = trisomy12)) + geom_point() +
  facet_wrap(~name, scale = "free")

Enrichment analysis

gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
            KEGG = "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt")
inputTab <- resList %>% filter(P.Value <0.05, Gene == "trisomy12") %>%
  mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
  distinct(name, .keep_all = TRUE) %>%
  select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes <- list()
enRes[["HALLMARK"]] <- runGSEA(inputTab, gmts$H, "page")
enRes[["KEGG"]] <- runGSEA(inputTab, gmts$KEGG, "page")
p <- plotEnrichmentBar(enRes, pCut =0.05, ifFDR= FALSE)

Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

#pdf("tri12Enrich.pdf", height = 15, width = 6)
plot(p)

#dev.off()

trisomy19

List of significant proteins (10% FDR)

corRes.sig <- resList %>% filter(Gene == "trisomy19", adj.P.Val < 0.1) %>%
  mutate(chromosome = rowData(protCLL[id,])$chromosome_name) %>%
  select(-Gene)
corRes.sig %>% mutate_if(is.numeric, formatC, digits=2, format="e") %>% DT::datatable()

Volcano plot

plotTab <- plotTab <- filter(resList, Gene == "trisomy19") %>%
  mutate(chromosome = rowData(protCLL[id,])$chromosome_name) %>%
  mutate(onChr19 = ifelse(chromosome == "19","yes","no"))
plotVolcano(plotTab, fdrCut =0.1, x_lab="log2FoldChange", 
            plotTitle = "trisomy19", ifLabel = TRUE, colLabel = "onChr19")

Labels colored by red indicates the gene is on chromosome 19

Heatmap of differentially expressed proteins

proList <- filter(corRes.sig, !is.na(name)) %>% distinct(name, .keep_all = TRUE) %>% pull(id)
plotMat <- assays(protCLL)[["QRILC"]][proList,]
rownames(plotMat) <- rowData(protCLL[proList,])$hgnc_symbol

colAnno <- geneMat[,c("trisomy19","trisomy12")] %>%
  data.frame()
colAnno$gender <- protCLL[,rownames(colAnno)]$gender

rowAnno <- rowData(protCLL)[proList, c("chromosome_name","hgnc_symbol"),drop=FALSE] %>% data.frame(stringsAsFactors = FALSE) %>%
  mutate(onChr19 = ifelse(chromosome_name == "19","yes","no")) %>%
  select(hgnc_symbol, onChr19) %>% data.frame() %>% column_to_rownames("hgnc_symbol")

plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno,  annotation_row = rowAnno,
         clustering_method = "ward.D2",
         color = colorRampPalette(c("navy","white","firebrick"))(100),
         breaks = seq(-6,6, length.out = 101))

It can be seen that some differentially expressed proteins are confounded by trisomy12