Last updated: 2021-05-06

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Load packages and datasets

Overview of differentially expressed proteins related to trisomy19

A table of associations with 5% FDR

resList <- filter(resList, Gene == "trisomy19") %>%
  #mutate(adj.P.Val = adj.P.global) %>% #use IHW corrected P-value
  mutate(Chr = rowData(protCLL[id,])$chromosome_name)
resList %>% filter(adj.P.Val <= 0.05) %>% 
  select(name, Chr,logFC, P.Value, adj.P.Val) %>%
  mutate_if(is.numeric, formatC, digits=2) %>%
  DT::datatable()

Heatmap of differentially expressed proteins (5% FDR)

(Restricted to M-CLL with trisomy12)

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

proList <- filter(resList, !is.na(name), adj.P.Val < 0.05) %>% 
  arrange(desc(t)) %>%
  distinct(name, .keep_all = TRUE) %>% pull(id)
plotMat <- assays(protCLL)[["QRILC_combat"]][proList, protCLL$IGHV.status %in% "M" & protCLL$trisomy12 %in% 1]
rownames(plotMat) <- rowData(protCLL[proList,])$hgnc_symbol

colAnno <- filter(patMeta, Patient.ID %in% colnames(protCLL)) %>%
  select(Patient.ID, trisomy12, IGHV.status,trisomy19) %>%
  data.frame() %>% column_to_rownames("Patient.ID")
colAnno$trisomy12 <- ifelse(colAnno$trisomy12 %in% 1, "yes","no")
colAnno$trisomy19 <- ifelse(colAnno$trisomy19 %in% 1, "yes","no")

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() %>% remove_rownames() %>%
  column_to_rownames("hgnc_symbol")

plotMat <- jyluMisc::mscale(plotMat, censor = 5)

annoCol <- list(trisomy12 = c(yes = "black",no = "grey80"),
                trisomy19 = c(yes = "black",no = "grey80"),
                IGHV.status = c(M = colList[3], U = colList[4]),
                onChr19 = c(yes = colList[1],no = "white"))

tri19Heatmap <- pheatmap::pheatmap(plotMat, annotation_col = colAnno, scale = "none",
                   annotation_row = rowAnno,
                   cluster_rows = FALSE,
                   clustering_method = "ward.D2",
                   color = colorRampPalette(c(colList[2],"white",colList[1]))(100),
                   breaks = seq(-5,5, length.out = 101), annotation_colors = annoCol, 
                   show_rownames = TRUE, show_colnames = FALSE,
                   treeheight_row = 0, silent = TRUE)$gtable
plot_grid(tri19Heatmap)

Summary of chromosome distribution (5% FDR)

plotTab <- filter(resList, adj.P.Val <=0.05) %>% mutate(change = ifelse(logFC>0,"Up regulated","Down regulated"),
                              chromosome = ifelse(Chr %in% "19","chr19","other")) %>%
  group_by(change, chromosome) %>% summarise(n = length(id)) %>%
  bind_rows(tibble(change = "Down regulated", chromosome = "chr19",  n =0))

sigNumPlot <- ggplot(plotTab, aes(x=change, y=n, fill = chromosome)) + 
  geom_bar(stat = "identity", width = 0.8,
           position = position_dodge2(width = 6),
           col = "black") +
  geom_text(aes(label=n), 
            position = position_dodge(width = 0.9),
            size=4, hjust=-0.1)  +
  scale_fill_manual(name = "", labels = c("chr19","other"), values = colList) +
  coord_flip(ylim = c(0,35), expand = FALSE) + xlab("") + ylab("Number of significant associations (10% FDR)") + theme_half 
sigNumPlot

## Enrichment analysis

Barplot of enriched pathways

gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
            KEGG = "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt",
            GO = "../data/gmts/c5.bp.v6.2.symbols.gmt")
inputTab <- resList %>% filter(P.Value < 0.05) %>%
  mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
  distinct(name, .keep_all = TRUE) %>%
  select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes <- list()
enRes[["Proteins associated with trisomy19"]] <- runGSEA(inputTab, gmts$H, "page")

p <- plotEnrichmentBar(enRes[[1]], pCut =0.05, ifFDR= TRUE, setName = "HALLMARK gene set", 
                       title = names(enRes)[1], removePrefix = "HALLMARK_", insideLegend=TRUE)

[1] "No sets passed the criteria"

tri19Enrich <- cowplot::plot_grid(p)
tri19Enrich

Volcano plot

plotTab <- resList %>% mutate(onChr19 = ifelse(Chr %in% "19","yes","no"))
#nameList <- filter(resList, adj.P.Val <=0.1)$name
nameList <- c("GPI","RANBP3","CALR","POU2F2")
tri19Volcano <- plotVolcano(plotTab, fdrCut =0.05, x_lab="log2FoldChange", posCol = colList[1], negCol = colList[2],
            plotTitle = "trisomy19", ifLabel = TRUE, labelList = nameList)
tri19Volcano

Boxplot plot of selected proteins

(Restricted to M-CLL with trisomy19)

protSub <- protCLL[, protCLL$IGHV.status %in% "M" & protCLL$trisomy12 %in% 1]

protTab <- sumToTidy(protSub, rowID = "uniprotID", colID = "patID")
resList.sig <- filter(resList, adj.P.Val < 0.1)
#nameList <- resList.sig$name[1:10]
plotTab <- protTab %>% filter(hgnc_symbol %in% nameList) %>%
  mutate(trisomy19 = patMeta[match(patID, patMeta$Patient.ID),]$trisomy19) %>%
  mutate(status = ifelse(trisomy19 %in% 1,"trisomy19","other"),
         name = hgnc_symbol) %>%
  mutate(status=factor(status, levels = c("other","trisomy19")))
pList <- plotBox(plotTab, pValTabel = resList, y_lab = "Protein expression")
tri19Box<-cowplot::plot_grid(plotlist= pList, ncol=2)
tri19Box

Buffering of gene dosage effect

Visualizing gene dosage effect on protein and RNA level

Preprocessing protein and RNA expression data

dds$trisomy19 <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$trisomy19
dds$IGHV <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$IGHV.status
dds$trisomy12 <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$trisomy12

ddsCLL <- dds[rownames(dds) %in% rowData(protCLL)$ensembl_gene_id, 
              !is.na(dds$trisomy19) & !is.na(dds$trisomy12) & !is.na(dds$IGHV)]

ddsSub <- dds[rownames(dds) %in% rowData(protCLL)$ensembl_gene_id, 
              dds$IGHV %in% "M" & dds$trisomy12 %in% 1 & !is.na(dds$trisomy19)]

ddsSub.vst <- varianceStabilizingTransformation(ddsSub)

Differential expression

resTab <- resListRNA %>% filter(Gene == "trisomy19")

protExprTab <- sumToTidy(protCLL) %>%
  filter(chromosome_name == "19", IGHV.status == "M", trisomy12 == 1) %>%
  mutate(id = ensembl_gene_id, patID = colID, expr = log2Norm_combat, type = "Protein") %>%
  select(id, patID, expr, type)

rnaExprTab <- counts(dds[rownames(dds) %in% protExprTab$id,
                            colnames(dds) %in% protExprTab$patID], normalized= TRUE) %>%
  as_tibble(rownames = "id") %>%
  pivot_longer(-id, names_to = "patID", values_to = "count") %>%
  mutate(expr = log2(count)) %>%
  select(id, patID, expr) %>% mutate(type = "RNA") 

comExprTab <- bind_rows(rnaExprTab, protExprTab) %>%
  mutate(trisomy19 = patMeta[match(patID, patMeta$Patient.ID),]$trisomy19) %>%
  filter(!is.na(trisomy19)) %>% mutate(cnv = ifelse(trisomy19 %in% 1, "trisomy19","other"))

Proteins/RNAs on Chr19 have higher expressions in trisomy19 samples compared to other samples

plotTab <- comExprTab %>%
  group_by(id,type) %>% mutate(zscore = (expr-mean(expr))/sd(expr)) %>%
  group_by(id, cnv, type) %>% summarise(meanExpr = mean(zscore, na.rm=TRUE)) %>%
  ungroup()

dosagePlot <- ggplot(plotTab, aes(x=meanExpr, fill = cnv, col=cnv)) + 
  geom_histogram(position = "identity", alpha=0.5, bins=30) + facet_wrap(~type, scale = "fixed") +
  scale_fill_manual(values = c(other = "grey80", trisomy19 = colList[2]), name = "") +
  scale_color_manual(values = c(other = "grey80", trisomy19 = colList[2]), name = "") +
  xlim(-1,1.5) +
  theme_full + xlab("Mean Z-score") +
  theme(strip.text = element_text(size =20), legend.position = c(0.1,0.9),
        legend.background = element_rect(fill = NA),
        legend.text = element_text(size=15))

dosagePlot

ggsave("tri19_dosage_effect.pdf", height = 3, width = 8)

Analyzing protein buffering effect

Detect buffered and non-buffered proteins

Preprocessing protein and RNA data

#subset samples and genes
overSampe <- intersect(colnames(ddsCLL), colnames(protCLL))
overGene <- intersect(rownames(ddsCLL), rowData(protCLL)$ensembl_gene_id)
ddsSub <- ddsCLL[overGene, overSampe]
protSub <- protCLL[match(overGene, rowData(protCLL)$ensembl_gene_id),overSampe]
rowData(ddsSub)$uniprotID <- rownames(protSub)[match(rownames(ddsSub),rowData(protSub)$ensembl_gene_id)]

#vst
ddsSub.vst <- varianceStabilizingTransformation(ddsSub)

Differential expression on RNA level

rnaRes <- resListRNA %>% filter(Gene == "trisomy19") %>%
  mutate(Chr = rowData(dds[id,])$chromosome) %>%
  #filter(Chr == "12") %>%
  #mutate(adj.P.Val = p.adjust(P.Value, method = "BH")) %>%
  dplyr::rename(geneID = id, log2FC.rna = log2FC, 
                pvalue.rna = P.Value, padj.rna = adj.P.Val, stat.rna= t) %>%
  select(geneID, log2FC.rna, pvalue.rna, padj.rna, stat.rna)

Protein abundance changes related to trisomy19

fdrCut <- 0.05
protRes <- resList %>% filter(Gene == "trisomy19") %>%
    dplyr::rename(uniprotID = id, 
                  pvalue = P.Value, padj = adj.P.global,
                  chrom = Chr) %>% 
    mutate(geneID = rowData(protCLL[uniprotID,])$ensembl_gene_id) %>%
    select(name, uniprotID, geneID, chrom, log2FC, pvalue, padj, t) %>%
    dplyr::rename(stat =t) %>%
    arrange(pvalue) %>% as_tibble()

Combine

allRes <- left_join(protRes, rnaRes, by = "geneID")

Only chr19 genes that are up-regulated are considered.

bufferTab <- allRes %>% filter(chrom %in% 19,stat.rna > 0, stat>0) %>%
  ungroup() %>%
  mutate(stat.prot.sqrt = sqrt(stat),
         stat.prot.center = stat.prot.sqrt - mean(stat.prot.sqrt, na.rm = TRUE)) %>%
  mutate(score = -stat.prot.center*stat.rna,
         diffFC = log2FC.rna - log2FC) %>%
  mutate(ifBuffer = case_when(
    padj < fdrCut & padj.rna < fdrCut & stat > 0 ~ "non-Buffered",
    padj > fdrCut & padj.rna < fdrCut ~ "Buffered",
    padj < fdrCut & padj.rna > fdrCut & stat > 0 ~ "Enhanced",
    TRUE ~ "Undetermined"
  )) %>%
  arrange(desc(score))

Table of buffering status

bufferTab %>% mutate_if(is.numeric, formatC, digits=2) %>%
  select(name, pvalue, pvalue.rna, padj, padj.rna, ifBuffer) %>%
  DT::datatable()

Summary plot

sumTab <- bufferTab %>% group_by(ifBuffer) %>%
  summarise(n = length(name))

bufferPlot <- ggplot(sumTab, aes(x=ifBuffer, y = n)) + 
  geom_bar(aes(fill = ifBuffer), stat="identity", width = 0.7) + 
  geom_text(aes(label = paste0(n)),vjust=-0.5,col="black",size=5) +
  scale_fill_manual(values =c(Buffered = colList[1],
                              Enhanced = colList[4],
                              `non-Buffered` = colList[2],
                              Undetermined = "grey50")) +
  theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5),
                     legend.position = "none") +
  ylab("Number of proteins") + ylim(0,130) +xlab("")
bufferPlot

ggsave("tri19_sum_buffer_number.pdf", width = 4, height = 4)

Enrichment of buffer and non-buffered proteins

Non-buffered prpteins

Using cancer hallmark genesets

protList <- filter(bufferTab, ifBuffer == "non-Buffered")$name
refList <- unique(protExprTab$symbol)
enRes <- runFisher(protList, refList, gmts$H, pCut =0.1, ifFDR = TRUE,removePrefix = "HALLMARK_",
                   plotTitle = "Non-buffered proteins", insideLegend = TRUE,
                   setName = "HALLMARK gene set")

[1] "No sets passed the criteria"

bufferEnrich <- enRes$enrichPlot + theme(plot.margin = margin(1,3,1,1, unit = "cm"))
bufferEnrich

NULL

Using GO Biological Process gene sets

protList <- filter(bufferTab, ifBuffer == "non-Buffered")$name
refList <- unique(protExprTab$symbol)
enRes <- runFisher(protList, refList, gmts$GO, pCut =0.1, ifFDR = TRUE,removePrefix = "GO_",
                   plotTitle = "Non-buffered proteins", insideLegend = TRUE,
                   setName = "GO BP gene set")

[1] "No sets passed the criteria"

bufferEnrich <- enRes$enrichPlot + theme(plot.margin = margin(1,3,1,1, unit = "cm"))
bufferEnrich

NULL

Buffered proteins

protList <- filter(bufferTab, ifBuffer == "Buffered")$name
enRes <- runFisher(protList, refList, gmts$H, pCut =0.1, ifFDR = TRUE)

[1] "No sets passed the criteria"

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS  10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] grid      parallel  stats4    stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] piano_2.4.0                 latex2exp_0.4.0            
 [3] forcats_0.5.1               stringr_1.4.0              
 [5] dplyr_1.0.5                 purrr_0.3.4                
 [7] readr_1.4.0                 tidyr_1.1.3                
 [9] tibble_3.1.0                tidyverse_1.3.0            
[11] ggbeeswarm_0.6.0            ComplexHeatmap_2.4.3       
[13] pheatmap_1.0.12             cowplot_1.1.1              
[15] ggraph_2.0.5                ggplot2_3.3.3              
[17] igraph_1.2.6                tidygraph_1.2.0            
[19] DESeq2_1.28.1               SummarizedExperiment_1.18.2
[21] DelayedArray_0.14.1         matrixStats_0.58.0         
[23] Biobase_2.48.0              GenomicRanges_1.40.0       
[25] GenomeInfoDb_1.24.2         IRanges_2.22.2             
[27] S4Vectors_0.26.1            BiocGenerics_0.34.0        
[29] limma_3.44.3               

loaded via a namespace (and not attached):
  [1] shinydashboard_0.7.1   utf8_1.1.4             tidyselect_1.1.0      
  [4] RSQLite_2.2.3          AnnotationDbi_1.50.3   htmlwidgets_1.5.3     
  [7] BiocParallel_1.22.0    maxstat_0.7-25         munsell_0.5.0         
 [10] codetools_0.2-18       DT_0.17                withr_2.4.1           
 [13] colorspace_2.0-0       highr_0.8              knitr_1.31            
 [16] rstudioapi_0.13        ggsignif_0.6.1         labeling_0.4.2        
 [19] git2r_0.28.0           slam_0.1-48            GenomeInfoDbData_1.2.3
 [22] KMsurv_0.1-5           polyclip_1.10-0        bit64_4.0.5           
 [25] farver_2.1.0           rprojroot_2.0.2        vctrs_0.3.6           
 [28] generics_0.1.0         TH.data_1.0-10         xfun_0.21             
 [31] sets_1.0-18            R6_2.5.0               clue_0.3-58           
 [34] graphlayouts_0.7.1     locfit_1.5-9.4         fgsea_1.14.0          
 [37] bitops_1.0-6           cachem_1.0.4           assertthat_0.2.1      
 [40] promises_1.2.0.1       scales_1.1.1           multcomp_1.4-16       
 [43] beeswarm_0.3.1         gtable_0.3.0           sandwich_3.0-0        
 [46] workflowr_1.6.2        rlang_0.4.10           genefilter_1.70.0     
 [49] GlobalOptions_0.1.2    splines_4.0.2          rstatix_0.7.0         
 [52] broom_0.7.5            yaml_2.2.1             abind_1.4-5           
 [55] modelr_0.1.8           crosstalk_1.1.1        backports_1.2.1       
 [58] httpuv_1.5.5           tools_4.0.2            relations_0.6-9       
 [61] ellipsis_0.3.1         gplots_3.1.1           jquerylib_0.1.3       
 [64] RColorBrewer_1.1-2     Rcpp_1.0.6             visNetwork_2.0.9      
 [67] zlibbioc_1.34.0        RCurl_1.98-1.2         ggpubr_0.4.0          
 [70] GetoptLong_1.0.5       viridis_0.5.1          zoo_1.8-9             
 [73] haven_2.3.1            ggrepel_0.9.1          cluster_2.1.1         
 [76] exactRankTests_0.8-31  fs_1.5.0               magrittr_2.0.1        
 [79] data.table_1.14.0      openxlsx_4.2.3         circlize_0.4.12       
 [82] survminer_0.4.9        reprex_1.0.0           mvtnorm_1.1-1         
 [85] shinyjs_2.0.0          hms_1.0.0              mime_0.10             
 [88] evaluate_0.14          xtable_1.8-4           XML_3.99-0.5          
 [91] rio_0.5.26             readxl_1.3.1           gridExtra_2.3         
 [94] shape_1.4.5            compiler_4.0.2         KernSmooth_2.23-18    
 [97] crayon_1.4.1           htmltools_0.5.1.1      later_1.1.0.1         
[100] geneplotter_1.66.0     lubridate_1.7.10       DBI_1.1.1             
[103] tweenr_1.0.1           dbplyr_2.1.0           MASS_7.3-53.1         
[106] jyluMisc_0.1.5         Matrix_1.3-2           car_3.0-10            
[109] cli_2.3.1              marray_1.66.0          km.ci_0.5-2           
[112] pkgconfig_2.0.3        foreign_0.8-81         xml2_1.3.2            
[115] annotate_1.66.0        vipor_0.4.5            bslib_0.2.4           
[118] XVector_0.28.0         drc_3.0-1              rvest_1.0.0           
[121] digest_0.6.27          fastmatch_1.1-0        rmarkdown_2.7         
[124] cellranger_1.1.0       survMisc_0.5.5         curl_4.3              
[127] shiny_1.6.0            gtools_3.8.2           rjson_0.2.20          
[130] lifecycle_1.0.0        jsonlite_1.7.2         carData_3.0-4         
[133] viridisLite_0.3.0      fansi_0.4.2            pillar_1.5.1          
[136] lattice_0.20-41        fastmap_1.1.0          httr_1.4.2            
[139] plotrix_3.8-1          survival_3.2-7         glue_1.4.2            
[142] zip_2.1.1              png_0.1-7              bit_4.0.4             
[145] ggforce_0.3.3          stringi_1.5.3          sass_0.3.1            
[148] blob_1.2.1             caTools_1.18.1         memoise_2.0.0

Section 5: Proteomic signatures of trisomy19

Junyan Lu

2021-02-16