Last updated: 2020-04-15

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Detect differentiall expressed proteins

Preprocessing proteomics data

protMat <- assays(protCLL)[["count"]]

Differential expression using proDA

Fit the probailistic dropout model

patAnno <- data.frame(row.names = colnames(protMat),
                      IGHV = patMeta[match(colnames(protMat),patMeta$Patient.ID),]$IGHV.status,
                      trisomy12 = patMeta[match(colnames(protMat),patMeta$Patient.ID),]$trisomy12) %>%
  mutate(IGHV = factor(IGHV, levels = c("U","M")))

fit <- proDA(protMat, design = ~ IGHV + trisomy12,
             col_data = patAnno)
diffRes.prot <- list()
diffRes.prot[["IGHV"]] <- test_diff(fit, "IGHVM")
diffRes.prot[["trisomy12"]] <- test_diff(fit, "trisomy121")

Differential expression using RNAseq

Preprocessing RNAseq data

dds$diag <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$diagnosis
dds <- dds[,dds$diag %in% "CLL"]
dds <- estimateSizeFactors(dds)

##filter out none protein coding genes and gene on sex chromosome
dds<-dds[rowData(dds)$biotype %in% "protein_coding",]
dds <- dds[! rowData(dds)$symbol %in% c("",NA),]

##filter out low count genes
minrs <- 100
rs  <- rowSums(counts(dds, normalized = TRUE))
dds<-dds[ rs >= minrs, ]

## Add IGHV and trisomy12 annotation
dds$IGHV <- factor(patMeta[match(dds$PatID, patMeta$Patient.ID),]$IGHV.status, levels = c("U","M"))
dds$trisomy12 <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$trisomy12
dds <- dds[,!(is.na(dds$IGHV) | is.na(dds$trisomy12))]

## A down-sampled data with only patients inluced in proteomic data
ddsSub <- dds[,dds$PatID %in% colnames(protMat)]
minrs <- 100
rs  <- rowSums(counts(ddsSub, normalized = TRUE))
ddsSub<-ddsSub[ rs >= minrs, ]

Differential expression using DESeq2

Full expression matrix

Test using DESeq2

dim(dds)
[1] 16141   208
design(dds) <- ~ IGHV + trisomy12
ddsDE <- DESeq(dds)
using pre-existing size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 692 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing

Get results

diffRes.rna <- list()
diffRes.rna[["IGHV"]] <- results(ddsDE, name = "IGHV_M_vs_U", tidy = TRUE)
diffRes.rna[["trisomy12"]] <- results(ddsDE, name = "trisomy12_1_vs_0", tidy = TRUE)

Subsetted expression matrix

Test using DESeq2

dim(ddsSub)
[1] 14902    46
design(ddsSub) <- ~ IGHV + trisomy12
ddsSubDE <- DESeq(ddsSub)
using pre-existing size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 473 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing

Get results

diffRes.rnaSub <- list()
diffRes.rnaSub[["IGHV"]] <- results(ddsSubDE, name = "IGHV_M_vs_U", tidy = TRUE)
diffRes.rnaSub[["trisomy12"]] <- results(ddsSubDE, name = "trisomy12_1_vs_0", tidy = TRUE)

Compare the results from proteomics and full RNAseq dataset

IGHV

Process differential expression output

protRes <- diffRes.prot$IGHV %>% mutate(symbol = rowData(protCLL_raw[name,])$hgnc_symbol) %>%
  select(symbol, pval, adj_pval, diff) %>%
  dplyr::rename(pval.prot = pval, padj.prot = adj_pval, diff.prot = diff) %>%
  arrange(pval.prot) %>% distinct(symbol, .keep_all = TRUE)

rnaRes <- diffRes.rna$IGHV %>% mutate(symbol = rowData(dds[row,])$symbol) %>%
  select(symbol, pvalue, padj, log2FoldChange) %>%
  dplyr::rename(pval.rna = pvalue, padj.rna = padj, diff.rna = log2FoldChange) %>% 
  arrange(pval.rna) %>% distinct(symbol, .keep_all = TRUE)

P-value correlation plot

compareTab <- left_join(protRes, rnaRes, by = "symbol") %>%
  filter(!is.na(pval.rna)) %>%
  mutate(significant = case_when(
    padj.rna < 0.01 & padj.prot < 0.01 ~ "both",
    padj.rna < 0.01 & padj.prot >= 0.01 ~ "rnaOnly",
    padj.rna >= 0.01 & padj.prot < 0.01 ~ "proteinOnly",
    padj.rna >= 0.01 & padj.prot >= 0.01 ~"none"
  ))
ggplot(compareTab, aes(x=-log10(padj.rna),y=-log10(padj.prot))) + geom_point(aes(col = significant)) +
  scale_color_manual(values = c(both = "green",rnaOnly = "red", proteinOnly = "blue",none="grey")) +
  scale_x_log10() + 
  ggrepel::geom_text_repel(data = filter(compareTab, significant == "proteinOnly"), aes(label = symbol)) +
  ylab("-log10(adjusted p value) in proteomic data") + 
  xlab("-log10(adjusted p value) in RNAseq data")

Version Author Date
b8e0823 Junyan Lu 2020-03-10

Note that the xaxis is shown is log10 scale, to concentrate more on the differentially expressed proteins, as there are way more differentially expressed genes by RNAseq than proteins.

Upset plot (1% FDR)

upsetList <- list(RNA_up = filter(rnaRes, padj.rna <= 0.01, diff.rna > 0)$symbol,
                  RNA_down = filter(rnaRes, padj.rna <= 0.01, diff.rna < 0)$symbol,
                  Protein_up = filter(protRes, padj.prot <= 0.01, diff.prot > 0)$symbol,
                  Protein_down = filter(protRes, padj.prot <= 0.01, diff.prot < 0)$symbol)
UpSetR::upset(fromList(upsetList))

Version Author Date
b8e0823 Junyan Lu 2020-03-10

Table of results (1% FDR)

compareTab %>% filter(significant != "none") %>%
  mutate_if(is.numeric, formatC, format ="e", digits=2) %>%
  DT::datatable(filter = "top")

trisomy12

Process differential expression output

protRes <- diffRes.prot$trisomy12 %>% mutate(symbol = rowData(protCLL_raw[name,])$hgnc_symbol) %>%
  select(symbol, pval, adj_pval, diff) %>%
  dplyr::rename(pval.prot = pval, padj.prot = adj_pval, diff.prot = diff) %>%
  arrange(pval.prot) %>% distinct(symbol, .keep_all = TRUE)

rnaRes <- diffRes.rna$trisomy12 %>% mutate(symbol = rowData(dds[row,])$symbol, 
                                           chr = rowData(dds[row,])$chromosome) %>%
  select(symbol, pvalue, padj, log2FoldChange, chr) %>%
  dplyr::rename(pval.rna = pvalue, padj.rna = padj, diff.rna = log2FoldChange) %>% 
  arrange(pval.rna) %>% distinct(symbol, .keep_all = TRUE)

P-value correlation plot

For all genes

compareTab <- left_join(protRes, rnaRes, by = "symbol") %>%
  filter(!is.na(pval.rna)) %>%
  mutate(significant = case_when(
    padj.rna < 0.01 & padj.prot < 0.01 ~ "both",
    padj.rna < 0.01 & padj.prot >= 0.01 ~ "rnaOnly",
    padj.rna >= 0.01 & padj.prot < 0.01 ~ "proteinOnly",
    padj.rna >= 0.01 & padj.prot >= 0.01 ~"none"
  ))
ggplot(compareTab, aes(x=-log10(padj.rna),y=-log10(padj.prot))) + geom_point(aes(col = significant)) +
  scale_color_manual(values = c(both = "green",rnaOnly = "red", proteinOnly = "blue",none="grey")) +
  scale_x_log10() + 
  ggrepel::geom_text_repel(data = filter(compareTab, significant == "proteinOnly"), aes(label = symbol)) +
  ylab("-log10(adjusted p value) in proteomic data") + 
  xlab("-log10(adjusted p value) in RNAseq data")

Version Author Date
b8e0823 Junyan Lu 2020-03-10

Note that the xaxis is shown is log10 scale, to concentrate more on the differentially expressed proteins, as there are way more differentially expressed genes by RNAseq than proteins.

For genes not on chr12

plotTab <- filter(compareTab, chr != "12")
ggplot(plotTab, aes(x=-log10(padj.rna),y=-log10(padj.prot))) + geom_point(aes(col = significant)) +
  scale_color_manual(values = c(both = "green",rnaOnly = "red", proteinOnly = "blue",none="grey")) +
  scale_x_log10() + 
  ggrepel::geom_text_repel(data = filter(plotTab, significant == "proteinOnly"), aes(label = symbol)) +
  ylab("-log10(adjusted p value) in proteomic data") + 
  xlab("-log10(adjusted p value) in RNAseq data")

Version Author Date
b8e0823 Junyan Lu 2020-03-10

Note that the xaxis is shown is log10 scale, to concentrate more on the differentially expressed proteins, as there are way more differentially expressed genes by RNAseq than proteins.

For genes on chr12

plotTab <- filter(compareTab, chr == "12")
ggplot(plotTab, aes(x=-log10(padj.rna),y=-log10(padj.prot))) + geom_point(aes(col = significant)) +
  scale_color_manual(values = c(both = "green",rnaOnly = "red", proteinOnly = "blue",none="grey")) +
  scale_x_log10() + 
  ggrepel::geom_text_repel(data = filter(plotTab, significant == "proteinOnly"), aes(label = symbol)) +
  ylab("-log10(adjusted p value) in proteomic data") + 
  xlab("-log10(adjusted p value) in RNAseq data")

Version Author Date
b8e0823 Junyan Lu 2020-03-10

Upset plot (1% FDR)

upsetList <- list(RNA_up = filter(rnaRes, padj.rna <= 0.01, diff.rna > 0)$symbol,
                  RNA_down = filter(rnaRes, padj.rna <= 0.01, diff.rna < 0)$symbol,
                  Protein_up = filter(protRes, padj.prot <= 0.01, diff.prot > 0)$symbol,
                  Protein_down = filter(protRes, padj.prot <= 0.01, diff.prot < 0)$symbol)
UpSetR::upset(fromList(upsetList))

Version Author Date
b8e0823 Junyan Lu 2020-03-10

Table of results (1% FDR)

compareTab %>% filter(significant != "none") %>%
  mutate_if(is.numeric, formatC, format ="e", digits=2) %>%
  DT::datatable(filter="top")

Compare the results from proteomics and subsetted RNAseq dataset

Only the RNAseq samples with proteomic data are included, for a fair comparison.

IGHV

Process differential expression output

protRes <- diffRes.prot$IGHV %>% mutate(symbol = rowData(protCLL_raw[name,])$hgnc_symbol) %>%
  select(symbol, pval, adj_pval, diff) %>%
  dplyr::rename(pval.prot = pval, padj.prot = adj_pval, diff.prot = diff) %>%
  arrange(pval.prot) %>% distinct(symbol, .keep_all = TRUE)

rnaRes <- diffRes.rnaSub$IGHV %>% mutate(symbol = rowData(dds[row,])$symbol) %>%
  select(symbol, pvalue, padj, log2FoldChange) %>%
  dplyr::rename(pval.rna = pvalue, padj.rna = padj, diff.rna = log2FoldChange) %>% 
  arrange(pval.rna) %>% distinct(symbol, .keep_all = TRUE)

P-value correlation plot

compareTab <- left_join(protRes, rnaRes, by = "symbol") %>%
  filter(!is.na(pval.rna)) %>%
  mutate(significant = case_when(
    padj.rna < 0.01 & padj.prot < 0.01 ~ "both",
    padj.rna < 0.01 & padj.prot >= 0.01 ~ "rnaOnly",
    padj.rna >= 0.01 & padj.prot < 0.01 ~ "proteinOnly",
    padj.rna >= 0.01 & padj.prot >= 0.01 ~"none"
  ))
ggplot(compareTab, aes(x=-log10(padj.rna),y=-log10(padj.prot))) + geom_point(aes(col = significant)) +
  scale_color_manual(values = c(both = "green",rnaOnly = "red", proteinOnly = "blue",none="grey")) +
  scale_x_log10() + 
  ggrepel::geom_text_repel(data = filter(compareTab, significant == "proteinOnly"), aes(label = symbol)) +
  ylab("-log10(adjusted p value) in proteomic data") + 
  xlab("-log10(adjusted p value) in RNAseq data")

Version Author Date
b8e0823 Junyan Lu 2020-03-10

Upset plot (1% FDR)

upsetList <- list(RNA_up = filter(rnaRes, padj.rna <= 0.01, diff.rna > 0)$symbol,
                  RNA_down = filter(rnaRes, padj.rna <= 0.01, diff.rna < 0)$symbol,
                  Protein_up = filter(protRes, padj.prot <= 0.01, diff.prot > 0)$symbol,
                  Protein_down = filter(protRes, padj.prot <= 0.01, diff.prot < 0)$symbol)
UpSetR::upset(fromList(upsetList))

Version Author Date
b8e0823 Junyan Lu 2020-03-10

RNAseq still detects more differentially expressed genes

Table of results (1% FDR)

compareTab %>% filter(significant != "none") %>%
  mutate_if(is.numeric, formatC, format ="e", digits=2) %>%
  DT::datatable(filter = "top")

trisomy12

Process differential expression output

protRes <- diffRes.prot$trisomy12 %>% mutate(symbol = rowData(protCLL_raw[name,])$hgnc_symbol) %>%
  select(symbol, pval, adj_pval, diff) %>%
  dplyr::rename(pval.prot = pval, padj.prot = adj_pval, diff.prot = diff) %>%
  arrange(pval.prot) %>% distinct(symbol, .keep_all = TRUE)

rnaRes <- diffRes.rnaSub$trisomy12 %>% mutate(symbol = rowData(dds[row,])$symbol, 
                                           chr = rowData(dds[row,])$chromosome) %>%
  select(symbol, pvalue, padj, log2FoldChange, chr) %>%
  dplyr::rename(pval.rna = pvalue, padj.rna = padj, diff.rna = log2FoldChange) %>% 
  arrange(pval.rna) %>% distinct(symbol, .keep_all = TRUE)

P-value correlation plot

For all genes

compareTab <- left_join(protRes, rnaRes, by = "symbol") %>%
  filter(!is.na(pval.rna)) %>%
  mutate(significant = case_when(
    padj.rna < 0.01 & padj.prot < 0.01 ~ "both",
    padj.rna < 0.01 & padj.prot >= 0.01 ~ "rnaOnly",
    padj.rna >= 0.01 & padj.prot < 0.01 ~ "proteinOnly",
    padj.rna >= 0.01 & padj.prot >= 0.01 ~"none"
  ))
ggplot(compareTab, aes(x=-log10(padj.rna),y=-log10(padj.prot))) + geom_point(aes(col = significant)) +
  scale_color_manual(values = c(both = "green",rnaOnly = "red", proteinOnly = "blue",none="grey")) +
  scale_x_log10() + 
  ggrepel::geom_text_repel(data = filter(compareTab, significant == "proteinOnly"), aes(label = symbol)) +
  ylab("-log10(adjusted p value) in proteomic data") + 
  xlab("-log10(adjusted p value) in RNAseq data")

Version Author Date
b8e0823 Junyan Lu 2020-03-10

Note that the xaxis is shown is log10 scale, to concentrate more on the differentially expressed proteins, as there are way more differentially expressed genes by RNAseq than proteins.

For genes not on chr12

plotTab <- filter(compareTab, chr != "12")
ggplot(plotTab, aes(x=-log10(padj.rna),y=-log10(padj.prot))) + geom_point(aes(col = significant)) +
  scale_color_manual(values = c(both = "green",rnaOnly = "red", proteinOnly = "blue",none="grey")) +
  scale_x_log10() + 
  ggrepel::geom_text_repel(data = filter(plotTab, significant == "proteinOnly"), aes(label = symbol)) +
  ylab("-log10(adjusted p value) in proteomic data") + 
  xlab("-log10(adjusted p value) in RNAseq data")

Version Author Date
b8e0823 Junyan Lu 2020-03-10

Note that the xaxis is shown is log10 scale, to concentrate more on the differentially expressed proteins, as there are way more differentially expressed genes by RNAseq than proteins.

For genes on chr12

plotTab <- filter(compareTab, chr == "12")
ggplot(plotTab, aes(x=-log10(padj.rna),y=-log10(padj.prot))) + geom_point(aes(col = significant)) +
  scale_color_manual(values = c(both = "green",rnaOnly = "red", proteinOnly = "blue",none="grey")) +
  scale_x_log10() + 
  ggrepel::geom_text_repel(data = filter(plotTab, significant == "proteinOnly"), aes(label = symbol)) +
  ylab("-log10(adjusted p value) in proteomic data") + 
  xlab("-log10(adjusted p value) in RNAseq data")

Version Author Date
b8e0823 Junyan Lu 2020-03-10

Upset plot (1% FDR)

upsetList <- list(RNA_up = filter(rnaRes, padj.rna <= 0.01, diff.rna > 0)$symbol,
                  RNA_down = filter(rnaRes, padj.rna <= 0.01, diff.rna < 0)$symbol,
                  Protein_up = filter(protRes, padj.prot <= 0.01, diff.prot > 0)$symbol,
                  Protein_down = filter(protRes, padj.prot <= 0.01, diff.prot < 0)$symbol)
UpSetR::upset(fromList(upsetList))

Version Author Date
b8e0823 Junyan Lu 2020-03-10

Table of results (1% FDR)

compareTab %>% filter(significant != "none") %>%
  mutate_if(is.numeric, formatC, format ="e", digits=2) %>%
  DT::datatable(filter="top")

sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.15.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] forcats_0.4.0               stringr_1.4.0              
 [3] dplyr_0.8.5                 purrr_0.3.3                
 [5] readr_1.3.1                 tidyr_1.0.0                
 [7] tibble_3.0.0                tidyverse_1.3.0            
 [9] jyluMisc_0.1.5              ggrepel_0.8.1              
[11] proDA_1.1.2                 UpSetR_1.4.0               
[13] vsn_3.52.0                  DESeq2_1.24.0              
[15] SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
[17] BiocParallel_1.18.0         matrixStats_0.54.0         
[19] Biobase_2.44.0              GenomicRanges_1.36.0       
[21] GenomeInfoDb_1.20.0         IRanges_2.18.1             
[23] S4Vectors_0.22.0            BiocGenerics_0.30.0        
[25] cowplot_0.9.4               ggplot2_3.3.0              
[27] limma_3.40.2               

loaded via a namespace (and not attached):
  [1] shinydashboard_0.7.1   tidyselect_1.0.0       RSQLite_2.1.1         
  [4] AnnotationDbi_1.46.0   htmlwidgets_1.3        grid_3.6.0            
  [7] maxstat_0.7-25         munsell_0.5.0          codetools_0.2-16      
 [10] preprocessCore_1.46.0  DT_0.7                 withr_2.1.2           
 [13] colorspace_1.4-1       knitr_1.23             rstudioapi_0.10       
 [16] ggsignif_0.5.0         labeling_0.3           git2r_0.26.1          
 [19] slam_0.1-45            GenomeInfoDbData_1.2.1 KMsurv_0.1-5          
 [22] farver_2.0.3           bit64_0.9-7            rprojroot_1.3-2       
 [25] vctrs_0.2.4            generics_0.0.2         TH.data_1.0-10        
 [28] xfun_0.8               sets_1.0-18            R6_2.4.0              
 [31] locfit_1.5-9.1         bitops_1.0-6           fgsea_1.10.0          
 [34] assertthat_0.2.1       promises_1.0.1         scales_1.1.0          
 [37] multcomp_1.4-10        nnet_7.3-12            gtable_0.3.0          
 [40] extraDistr_1.8.11      affy_1.62.0            sandwich_2.5-1        
 [43] workflowr_1.6.0        rlang_0.4.5            genefilter_1.66.0     
 [46] cmprsk_2.2-8           splines_3.6.0          acepack_1.4.1         
 [49] broom_0.5.2            checkmate_2.0.0        BiocManager_1.30.4    
 [52] yaml_2.2.0             abind_1.4-5            modelr_0.1.5          
 [55] crosstalk_1.0.0        backports_1.1.4        httpuv_1.5.1          
 [58] Hmisc_4.2-0            tools_3.6.0            relations_0.6-8       
 [61] affyio_1.54.0          ellipsis_0.2.0         gplots_3.0.1.1        
 [64] RColorBrewer_1.1-2     Rcpp_1.0.1             plyr_1.8.4            
 [67] base64enc_0.1-3        visNetwork_2.0.7       zlibbioc_1.30.0       
 [70] RCurl_1.95-4.12        ggpubr_0.2.1           rpart_4.1-15          
 [73] zoo_1.8-6              haven_2.2.0            cluster_2.1.0         
 [76] exactRankTests_0.8-30  fs_1.4.0               magrittr_1.5          
 [79] data.table_1.12.2      openxlsx_4.1.0.1       reprex_0.3.0          
 [82] survminer_0.4.4        mvtnorm_1.0-11         whisker_0.3-2         
 [85] hms_0.5.2              shinyjs_1.0            mime_0.7              
 [88] evaluate_0.14          xtable_1.8-4           XML_3.98-1.20         
 [91] rio_0.5.16             readxl_1.3.1           gridExtra_2.3         
 [94] compiler_3.6.0         KernSmooth_2.23-15     crayon_1.3.4          
 [97] htmltools_0.4.0        later_0.8.0            Formula_1.2-3         
[100] geneplotter_1.62.0     lubridate_1.7.4        DBI_1.0.0             
[103] dbplyr_1.4.2           MASS_7.3-51.4          Matrix_1.2-17         
[106] car_3.0-3              cli_1.1.0              marray_1.62.0         
[109] gdata_2.18.0           igraph_1.2.4.1         pkgconfig_2.0.2       
[112] km.ci_0.5-2            foreign_0.8-71         piano_2.0.2           
[115] xml2_1.2.2             annotate_1.62.0        XVector_0.24.0        
[118] drc_3.0-1              rvest_0.3.5            digest_0.6.19         
[121] rmarkdown_1.13         cellranger_1.1.0       fastmatch_1.1-0       
[124] survMisc_0.5.5         htmlTable_1.13.1       curl_3.3              
[127] shiny_1.3.2            gtools_3.8.1           lifecycle_0.2.0       
[130] nlme_3.1-140           jsonlite_1.6           carData_3.0-2         
[133] pillar_1.4.3           lattice_0.20-38        httr_1.4.1            
[136] plotrix_3.7-6          survival_2.44-1.1      glue_1.3.2            
[139] zip_2.0.2              bit_1.1-14             stringi_1.4.3         
[142] blob_1.1.1             latticeExtra_0.6-28    caTools_1.17.1.2      
[145] memoise_1.1.0