Last updated: 2020-03-10

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Test association with RNA expression

Dimension of the inputed data

dim(protCLL)

[1] 3329   49

Process both datasets

colnames(dds) <- dds$PatID
dds <- estimateSizeFactors(dds)
sampleOverlap <- intersect(colnames(protCLL), colnames(dds))
geneOverlap <- intersect(rowData(protCLL)$ensembl_gene_id, rownames(dds))
ddsSub <- dds[geneOverlap, sampleOverlap]
protSub <- protCLL[match(geneOverlap, rowData(protCLL)$ensembl_gene_id), sampleOverlap]

#how many gene don't have RNA expression at all?
noExp <- rowSums(counts(ddsSub)) == 0
sum(noExp)

[1] 11

#remove those genes in both datasets
ddsSub <- ddsSub[!noExp,]
protSub <- protSub[!noExp,]

#remove proteins with duplicated identifiers
protSub <- protSub[!duplicated(rowData(protSub)$name)]

geneOverlap <- intersect(rowData(protSub)$ensembl_gene_id, rownames(ddsSub))

ddsSub.vst <- varianceStabilizingTransformation(ddsSub)

Calculate correlations between protein abundance and RNA expression

rnaMat <- assay(ddsSub.vst)
proMat.raw <- assays(protSub)[["count"]]
proMat.qrilc <- assays(protSub)[["QRILC"]]
rownames(proMat.qrilc) <- rowData(protSub)$ensembl_gene_id
rownames(proMat.raw) <- rowData(protSub)$ensembl_gene_id

corTab <- lapply(geneOverlap, function(n) {
  rna <- rnaMat[n,]
  pro.q <- proMat.qrilc[n,]
  pro.raw <- proMat.raw[n,]
  res.q <- cor.test(rna, pro.q)
  res.raw <- cor.test(rna, pro.raw, use = "pairwise.complete.obs")
  tibble(id = n, impute=c("No Imputation","QRILC"),
         p = c(res.raw$p.value, res.q$p.value),
         coef = c(res.raw$estimate, res.q$estimate))
}) %>% bind_rows() %>%
  arrange(desc(coef)) %>% mutate(p.adj = p.adjust(p, method = "BH"),
                                 symbol = rowData(dds[id,])$symbol)

Plot the distribution of correlation coefficient

ggplot(corTab, aes(x=coef, fill = impute)) + geom_histogram(position = "identity", col = "grey50", alpha =0.3, bins =100) +
  geom_vline(xintercept = 0, col = "red", linetype = "dashed")

Warning: Removed 2 rows containing non-finite values (stat_bin).

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Most of the correlations are positive, which is reasonable.

Number of significant positive and negative correlations (10% FDR)

sigTab <- corTab %>% filter(p.adj < 0.1) %>% mutate(direction = ifelse(coef > 0, "positive", "negative")) %>%
  group_by(impute, direction) %>% summarise(number = length(id)) %>% ungroup() %>%
  mutate(ratio = format(number/length(geneOverlap), digits = 2)) %>% arrange(number)
DT::datatable(sigTab)

Number of significant correlations VS FDR cut-off

plotTab <- lapply(seq(0,0.1, length.out = 100), function(fdr) {
  filTab <- dplyr::filter(corTab, p.adj < fdr, coef > 0) %>%
    group_by(impute) %>% summarise(n = length(id)) %>% mutate(fdr = fdr)
}) %>% bind_rows()

ggplot(plotTab, aes(x=fdr, y = n, col = impute))+ geom_line() +
  ylab("Number of significant correlations") +
  xlab("FDR cut-off")

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

List of proteins that significantly correlated with RNA expression (10 %FDR, no imputation)

sigTab <- filter(corTab, p.adj < 0.1, impute == "No Imputation") %>% mutate_if(is.numeric, format, digits=2) 
DT::datatable(sigTab)

List of proteins that significantly correlated with RNA expression (10 %FDR, QRILC imputed)

sigTab <- filter(corTab, p.adj < 0.1, impute == "QRILC") %>% mutate_if(is.numeric, format, digits=2) 
DT::datatable(sigTab)

Correlation plot of top 9 most correlated protein-rna pairs

plotList <- lapply(sigTab$id[1:9], function(n) {
  plotTab <- tibble(pro = proMat.qrilc[n,], gene = rnaMat[n,])
  symbol <- filter(sigTab, id == n)$symbol
  ggplot(plotTab, aes(x=pro, y=gene)) + geom_point() + geom_smooth(method = "lm") +
    ggtitle(symbol) + ylab("RNA expression") + xlab("Protein abundance")
})

cowplot::plot_grid(plotlist = plotList, ncol =3)

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Assocation with technical factors

Are thechnical variables confounded with major genomic variabes?

# A tibble: 3 x 4
  genomic     technical       pval p.adj
  <chr>       <chr>          <dbl> <dbl>
1 IGHV.status processDate   0.0219 0.480
2 SF3B1       freeThawCycle 0.0229 0.480
3 del13q      operator      0.0494 0.692

No Significant assocations

Association between technical variables and priciple components of protein expression

plotMat <- assays(protCLL)[["QRILC"]]
pcRes <- prcomp(t(plotMat), center =TRUE, scale. = FALSE)$x

testRes <- lapply(colnames(pcRes), function(pc) {
  lapply(colnames(techTab), function(tech) {
    pcVar <- pcRes[,pc]
    techVar <- techTab[[tech]]
    res <- summary(aov(pcVar ~ techVar))
    p <- res[[1]][1,4]
    tibble(component = pc, technical = tech, pval = p)
  }) %>% bind_rows()
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(pval, method = "BH")) %>%
  arrange(pval)
filter(testRes, p.adj < 0.1)

# A tibble: 10 x 4
   component technical          pval  p.adj
   <chr>     <chr>             <dbl>  <dbl>
 1 PC31      proteinConc   0.0000391 0.0115
 2 PC47      proteinConc   0.000124  0.0158
 3 PC41      proteinConc   0.000162  0.0158
 4 PC45      proteinConc   0.000806  0.0593
 5 PC27      proteinConc   0.00143   0.0843
 6 PC25      proteinConc   0.00216   0.0991
 7 PC12      proteinConc   0.00237   0.0991
 8 PC48      proteinConc   0.00271   0.0991
 9 PC18      freeThawCycle 0.00336   0.0991
10 PC49      proteinConc   0.00337   0.0991

There are some principle components correlated with technical variables. But the PCs are not top PCs, suggesting the know technical factor do not have impact on the major trends of the dataset.

Associations between technical variables and individual protein expressions

Association test

techTab <- colData(protCLL)[,c("operator", "viability","batch","processDate","proteinConc","freeThawCycle")] %>%
  data.frame() %>%rownames_to_column("patID") %>% as_tibble() %>% mutate(processDate = as.character(processDate)) %>%
  mutate_if(is.character, as.factor)%>% mutate_at(vars(-patID), as.numeric)
testTab <- assays(protCLL)[["QRILC"]] %>% data.frame() %>% 
  rownames_to_column("id") %>% mutate(name = rowData(protCLL)[id,]$hgnc_symbol) %>%
  gather(key = "patID", value = "expr", -id, -name) %>%
  left_join(techTab, by ="patID") %>% gather(key = technical, value = value, -id, -name, -patID, -expr)

Warning: Column `patID` joining character vector and factor, coercing into
character vector

testRes <- filter(testTab, !is.na(value)) %>% 
  group_by(name, technical) %>% nest() %>%
  mutate(m = map(data, ~lm(expr~value,.))) %>%
  mutate(res = map(m, broom::tidy)) %>%
  unnest(res) %>% filter(term=="value") %>%
  mutate(p.adj = p.adjust(p.value, method = "BH"))

sumTab <- filter(testRes, p.adj < 0.1) %>% group_by(technical) %>% summarise(n=length(name)) 
ggplot(sumTab, aes(x=technical, y = n)) + geom_bar(stat = "identity") + coord_flip() +
  xlab("") + ylab("Number of significantly associated proteins")

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Assocation P value histogram for each technical factor

ggplot(testRes, aes(x=p.value)) + geom_histogram() + facet_wrap(~technical) +xlim(0,1)

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 12 rows containing missing values (geom_bar).

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Also based on the p-value histogram, only overall protein concentration may have potential impact on protein abundance detection.

Will the correlation with RNA expression improve if we adjust for total protein concentration?

proteinConc <- techTab[match(colnames(proMat.qrilc), techTab$patID),]$proteinConc
corTab <- lapply(geneOverlap, function(n) {
  rna <- rnaMat[n,]
  pro.q <- proMat.qrilc[n,]
  p.single <- anova(lm(rna ~ pro.q))$`Pr(>F)`[1]
  p.multi <- car::Anova(lm(rna ~ pro.q + proteinConc))$`Pr(>F)`[1]
  tibble(name = n, corrected = c("no","yes"),
         p = c(p.single, p.multi))
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p, method = "BH")) %>% arrange(p)

Number of significant correlations VS FDR cut-off

plotTab <- lapply(seq(0,0.1, length.out = 100), function(fdr) {
  filTab <- dplyr::filter(corTab, p.adj < fdr) %>%
    group_by(corrected) %>% summarise(n = length(name)) %>% mutate(fdr = fdr)
}) %>% bind_rows()

ggplot(plotTab, aes(x=fdr, y = n, col = corrected))+ geom_line() +
  ylab("Number of significant correlations") +
  xlab("FDR cut-off")

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Seems to improve the correlation a little, but not much. We can include this factor in association test later.

Check data structure

Hierarchical clustering

plotMat <- assays(protCLL)[["QRILC"]]

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

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

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

No clear separation can be observed

PCA

pcOut <- prcomp(t(plotMat), center =TRUE, scale. = FALSE)
pcRes <- pcOut$x
eigs <- pcOut$sdev^2
varExp <- structure(eigs/sum(eigs),names = colnames(pcRes))

plotTab <- pcRes[,1:2] %>% data.frame() %>% cbind(colAnno[rownames(.),]) %>%
  rownames_to_column("patID") %>% as_tibble()

ggplot(plotTab, aes(x=PC1, y=PC2, col = IGHV.status, shape = trisomy12)) + geom_point(size=4) +
  xlab(sprintf("PC1 (%1.2f%%)",varExp[["PC1"]]*100)) +
  ylab(sprintf("PC2 (%1.2f%%)",varExp[["PC2"]]*100))

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

PC2 separates trisomy12

Correlation PCs with trisomy12 and IGHV status

corTab <- lapply(colnames(pcRes),  function(pc) {
  ighvCor <- t.test(pcRes[,pc] ~ colAnno$IGHV.status)
  tri12Cor <- t.test(pcRes[,pc] ~ colAnno$trisomy12)
  tibble(PC = pc, 
         feature=c("IGHV", "trisomy12"),
         p = c(ighvCor$p.value, tri12Cor$p.value))
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p, method = "BH")) %>%
  filter(p <= 0.05) %>% arrange(p)
corTab

# A tibble: 6 x 4
  PC    feature             p     p.adj
  <chr> <chr>           <dbl>     <dbl>
1 PC2   trisomy12 0.000000348 0.0000342
2 PC6   IGHV      0.0000912   0.00447  
3 PC5   trisomy12 0.00197     0.0642   
4 PC4   IGHV      0.0295      0.598    
5 PC3   IGHV      0.0305      0.598    
6 PC49  IGHV      0.0470      0.682

PC6 is for IGHV

PCA plot using PC2 and PC7

plotTab <- pcRes[,1:10] %>% data.frame() %>% cbind(colAnno[rownames(.),]) %>%
  rownames_to_column("patID") %>% as_tibble()

ggplot(plotTab, aes(x=PC2, y=PC6, col = IGHV.status, shape = trisomy12, label = patID)) + geom_point() + ggrepel::geom_text_repel() +
  xlab(sprintf("PC2 (%1.2f%%)",varExp[["PC2"]]*100)) +
  ylab(sprintf("PC6 (%1.2f%%)",varExp[["PC6"]]*100))

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Using PC3 and PC4 better seperates IGHV and trisomy12.

Biological annotation of PC1

Annotate PC1

Correlation test

Assocation test

proMat <- assays(protCLL)[["QRILC"]]
pc <- pcRes[,1][colnames(proMat)]
designMat <- model.matrix(~1+pc)
fit <- lmFit(proMat,  designMat)
fit2 <- eBayes(fit)
corRes.pc1 <- topTable(fit2, number ="all", adjust.method = "BH", coef = "pc") %>% rownames_to_column("id") %>%
    mutate(symbol = rowData(protCLL[id,])$hgnc_symbol)

Number of significant associations (10% FDR)

hist(corRes.pc1$P.Value,breaks=100)

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Table of significant associations (5% FDR)

resTab.sig <- filter(corRes.pc1, adj.P.Val < 0.05) %>% 
  select(symbol, id,logFC, P.Value, adj.P.Val) %>%
  arrange(P.Value)
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
  DT::datatable()

Heatmap of associated genes

colAnno <- tibble(patID = colnames(proMat), PC1 = pcRes[colnames(proMat),1]) %>%
  arrange(PC1) %>% data.frame() %>% column_to_rownames("patID")

plotMat <- proMat[resTab.sig$id[1:100],rownames(colAnno)]
plotMat <- jyluMisc::mscale(plotMat, censor = 6)

pheatmap(plotMat, scale = "none", annotation_col = colAnno, clustering_method = "ward.D2",
         cluster_cols = FALSE,
         labels_row = resTab.sig$symbol[1:100], color = colorRampPalette(c("navy","white","firebrick"))(100),
         breaks = seq(-6,6, length.out = 101))

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Enrichment using Camera

Hallmarks

gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
            C6 = "../data/gmts/c6.all.v6.2.symbols.gmt",
            KEGG = "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt")

res <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

res <- runCamera(proMat, designMat, gmts$C6, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

KEGG

res <- runCamera(proMat, designMat, gmts$KEGG, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Association with lymphocyte count (potential contamination?)

load("~/CLLproject_jlu/ShinyApps/sampleTimeline//timeline.RData")
plotTab <- pcRes[,1:9] %>% data.frame()  %>%
  rownames_to_column("patID") %>% as_tibble() %>%
  mutate(sampleID = protCLL[,patID]$sampleID) %>%
  mutate(lymCount = sampleTab[match(sampleID, sampleTab$sampleID),]$leukCount) %>%
  gather(key = "pc", value = "val",-patID,-sampleID,-lymCount)

ggplot(plotTab, aes(x=val, y=lymCount)) + geom_point() + geom_smooth(method = "lm") +
  facet_wrap(~pc, ncol =3, scale = "free")

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

corRes <- plotTab %>% group_by(pc) %>% nest() %>%
  mutate(m= map(data, ~cor.test(~ val+ lymCount,.))) %>%
  mutate(res = map(m, broom::tidy)) %>%
  unnest(res) %>% select(pc, estimate, p.value) %>%
  arrange(p.value)
corRes

# A tibble: 9 x 3
# Groups:   pc [9]
  pc    estimate p.value
  <chr>    <dbl>   <dbl>
1 PC2    -0.420  0.00265
2 PC1    -0.230  0.111  
3 PC9     0.211  0.145  
4 PC6     0.159  0.275  
5 PC4     0.0781 0.594  
6 PC8     0.0571 0.697  
7 PC7     0.0482 0.742  
8 PC5    -0.0356 0.808  
9 PC3    -0.0338 0.818

Association wit %Lymphocyte PB

library(DBI)

con <- dbConnect(RPostgreSQL::PostgreSQL(),
                 dbname = "tumorbank",
                 host = "huber-vm01.embl.de",
                 user = "admin",
                 password = "bloodcancertumorbank")

PBtab <- tbl(con, "patient") %>% 
  left_join(tbl(con, "sample"), by = c(patid = "smppatidref")) %>% 
  left_join(tbl(con, "analysis"), by = c(smpid = "anlsmpidref")) %>%
  collect() %>%
  select(patpatientid, smpsampleid, smpleukocytes, smpsampledate, smppblymphocytes)

dbDisconnect(con)

[1] TRUE

plotTab <- pcRes[,1:9] %>% data.frame()  %>%
  rownames_to_column("patID") %>% as_tibble() %>%
  mutate(sampleID = protCLL[,patID]$sampleID) %>%
  mutate(perPB = PBtab[match(sampleID, PBtab$smpsampleid),]$smppblymphocytes) %>%
  gather(key = "pc", value = "val",-patID,-sampleID,-perPB)

ggplot(plotTab, aes(x=val, y=perPB)) + geom_point() + geom_smooth(method = "lm") +
  facet_wrap(~pc, ncol =3, scale = "free")

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

corRes <- plotTab %>% group_by(pc) %>% nest() %>%
  mutate(m= map(data, ~cor.test(~ val+ perPB,.))) %>%
  mutate(res = map(m, broom::tidy)) %>%
  unnest(res) %>% select(pc, estimate, p.value) %>%
  arrange(p.value)
corRes

# A tibble: 9 x 3
# Groups:   pc [9]
  pc    estimate p.value
  <chr>    <dbl>   <dbl>
1 PC1    -0.398  0.00463
2 PC2    -0.325  0.0228 
3 PC3     0.204  0.160  
4 PC9     0.126  0.389  
5 PC4    -0.0705 0.630  
6 PC7    -0.0591 0.687  
7 PC8    -0.0557 0.704  
8 PC6     0.0527 0.719  
9 PC5    -0.0476 0.745

PC1 and PC2 both seem to correlation with %PB. But PC2 is also associate with trisomy12?

Will the correlation with transcriptomics increase if PC1 is regressed out?

pc1 <- pcRes[colnames(rnaMat),1]

corTab <- lapply(geneOverlap, function(n) {
  rna <- rnaMat[n,]
  pro.q <- proMat.qrilc[n,]
  p.single <- anova(lm(rna ~ pro.q))$`Pr(>F)`[1]
  p.multi <- car::Anova(lm(rna ~ pro.q + pc1))$`Pr(>F)`[1]
  tibble(name = n, corrected = c("no","yes"),
         p = c(p.single, p.multi))
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p, method = "BH")) %>% arrange(p)

Number of significant correlations VS FDR cut-off

plotTab <- lapply(seq(0,0.1, length.out = 100), function(fdr) {
  filTab <- dplyr::filter(corTab, p.adj < fdr) %>%
    group_by(corrected) %>% summarise(n = length(name)) %>% mutate(fdr = fdr)
}) %>% bind_rows()

ggplot(plotTab, aes(x=fdr, y = n, col = corrected))+ geom_line() +
  ylab("Number of significant correlations") +
  xlab("FDR cut-off")

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Seems to improve the correlation a little, but not much. We can include this factor in association test later.

Reconstruct data with PC1 and PC2 removed

protMat <- t(assays(protCLL)[["QRILC"]])
mu <- colMeans(protMat)

Xpca <- prcomp(protMat, center = TRUE, scale. = FALSE)

#reconstruct without the first two components
protMat.new <- Xpca$x[,2:ncol(Xpca$x)] %*% t(Xpca$rotation[,2:ncol(Xpca$x)])
protMat.new <- scale(protMat.new, center = -mu, scale = FALSE)

protMat.new <- t(protMat.new)

Hierarchical clustering using reconstructed matrix

plotMat <- protMat.new

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

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

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Better separation for trisomy12 can be observed

PCA using reconstructed matrix

pcOut <- prcomp(t(plotMat), center =TRUE, scale. = FALSE)
pcRes.new <- pcOut$x
eigs <- pcOut$sdev^2
varExp <- structure(eigs/sum(eigs),names = colnames(pcRes.new))

plotTab <- pcRes.new[,1:2] %>% data.frame() %>% cbind(colAnno[rownames(.),]) %>%
  rownames_to_column("patID") %>% as_tibble()

ggplot(plotTab, aes(x=PC1, y=PC2, col = IGHV.status, shape = trisomy12)) + geom_point(size=4) +
  xlab(sprintf("PC1 (%1.2f%%)",varExp[["PC1"]]*100)) +
  ylab(sprintf("PC2 (%1.2f%%)",varExp[["PC2"]]*100))

Version	Author	Date
46534c2	Junyan Lu	2020-02-27

Some outliers dominate the variance

Save the post process object

assays(protCLL)[["QRILC_re"]] <- protMat.new
protCLL$PC1 <- pcRes[colnames(protCLL),1]
protCLL$PC2 <- pcRes[colnames(protCLL),2]
save(protCLL, file = "../output/timsTOF_processed.RData")

timsTOF has some quality issues, and it’s not very clear what the major variance in this dataset represents.

sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

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] DBI_1.0.0                   forcats_0.4.0              
 [3] stringr_1.4.0               dplyr_0.8.3                
 [5] purrr_0.3.3                 readr_1.3.1                
 [7] tidyr_1.0.0                 tibble_2.1.3               
 [9] tidyverse_1.3.0             DESeq2_1.24.0              
[11] SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
[13] BiocParallel_1.18.0         matrixStats_0.54.0         
[15] Biobase_2.44.0              GenomicRanges_1.36.0       
[17] GenomeInfoDb_1.20.0         IRanges_2.18.1             
[19] S4Vectors_0.22.0            BiocGenerics_0.30.0        
[21] jyluMisc_0.1.5              pheatmap_1.0.12            
[23] piano_2.0.2                 cowplot_0.9.4              
[25] ggplot2_3.2.1               limma_3.40.2               

loaded via a namespace (and not attached):
  [1] utf8_1.1.4             shinydashboard_0.7.1   tidyselect_0.2.5      
  [4] RSQLite_2.1.1          AnnotationDbi_1.46.0   htmlwidgets_1.3       
  [7] grid_3.6.0             maxstat_0.7-25         munsell_0.5.0         
 [10] codetools_0.2-16       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] bit64_0.9-7            rprojroot_1.3-2        vctrs_0.2.0           
 [25] generics_0.0.2         TH.data_1.0-10         xfun_0.8              
 [28] sets_1.0-18            R6_2.4.0               locfit_1.5-9.1        
 [31] bitops_1.0-6           fgsea_1.10.0           assertthat_0.2.1      
 [34] promises_1.0.1         scales_1.0.0           multcomp_1.4-10       
 [37] nnet_7.3-12            gtable_0.3.0           sandwich_2.5-1        
 [40] workflowr_1.6.0        rlang_0.4.1            zeallot_0.1.0         
 [43] genefilter_1.66.0      cmprsk_2.2-8           splines_3.6.0         
 [46] lazyeval_0.2.2         acepack_1.4.1          broom_0.5.2           
 [49] checkmate_1.9.3        yaml_2.2.0             abind_1.4-5           
 [52] modelr_0.1.5           crosstalk_1.0.0        backports_1.1.4       
 [55] httpuv_1.5.1           Hmisc_4.2-0            tools_3.6.0           
 [58] relations_0.6-8        RPostgreSQL_0.6-2      ellipsis_0.2.0        
 [61] gplots_3.0.1.1         RColorBrewer_1.1-2     Rcpp_1.0.1            
 [64] base64enc_0.1-3        visNetwork_2.0.7       zlibbioc_1.30.0       
 [67] RCurl_1.95-4.12        ggpubr_0.2.1           rpart_4.1-15          
 [70] zoo_1.8-6              ggrepel_0.8.1          haven_2.2.0           
 [73] cluster_2.1.0          exactRankTests_0.8-30  fs_1.3.1              
 [76] magrittr_1.5           data.table_1.12.2      openxlsx_4.1.0.1      
 [79] reprex_0.3.0           survminer_0.4.4        mvtnorm_1.0-11        
 [82] whisker_0.3-2          hms_0.5.2              shinyjs_1.0           
 [85] mime_0.7               evaluate_0.14          xtable_1.8-4          
 [88] XML_3.98-1.20          rio_0.5.16             readxl_1.3.1          
 [91] gridExtra_2.3          compiler_3.6.0         KernSmooth_2.23-15    
 [94] crayon_1.3.4           htmltools_0.3.6        later_0.8.0           
 [97] Formula_1.2-3          geneplotter_1.62.0     lubridate_1.7.4       
[100] dbplyr_1.4.2           MASS_7.3-51.4          Matrix_1.2-17         
[103] car_3.0-3              cli_1.1.0              marray_1.62.0         
[106] gdata_2.18.0           igraph_1.2.4.1         pkgconfig_2.0.2       
[109] km.ci_0.5-2            foreign_0.8-71         xml2_1.2.2            
[112] annotate_1.62.0        XVector_0.24.0         drc_3.0-1             
[115] rvest_0.3.5            digest_0.6.19          rmarkdown_1.13        
[118] cellranger_1.1.0       fastmatch_1.1-0        survMisc_0.5.5        
[121] htmlTable_1.13.1       curl_3.3               shiny_1.3.2           
[124] gtools_3.8.1           lifecycle_0.1.0        nlme_3.1-140          
[127] jsonlite_1.6           carData_3.0-2          fansi_0.4.0           
[130] pillar_1.4.2           lattice_0.20-38        httr_1.4.1            
[133] plotrix_3.7-6          survival_2.44-1.1      glue_1.3.1            
[136] zip_2.0.2              bit_1.1-14             stringi_1.4.3         
[139] blob_1.1.1             latticeExtra_0.6-28    caTools_1.17.1.2      
[142] memoise_1.1.0

Quality control of the proteomic data from timsTOF machine

Junyan Lu

2020-02-27

Test association with RNA expression

Assocation with technical factors

Are thechnical variables confounded with major genomic variabes?

Association between technical variables and priciple components of protein expression

Associations between technical variables and individual protein expressions

Will the correlation with RNA expression improve if we adjust for total protein concentration?

Check data structure

Hierarchical clustering

PCA

Biological annotation of PC1

Annotate PC1

Correlation test

Enrichment using Camera

Association with lymphocyte count (potential contamination?)

Association wit %Lymphocyte PB

Will the correlation with transcriptomics increase if PC1 is regressed out?

Reconstruct data with PC1 and PC2 removed

Hierarchical clustering using reconstructed matrix

PCA using reconstructed matrix

Save the post process object