Analysis of the correlations between RNA and protein expression
Junyan Lu
2021-02-16
Last updated: 2021-03-05
Checks: 6 1
Knit directory: CLLproteomics_batch13/analysis/
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Calculate protein-RNA correlations
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)$ensembl_gene_id), sampleOverlap]
#how many gene don't have RNA expression at all?
noExp <- rowSums(counts(ddsSub)) == 0
#remove those genes in both datasets
ddsSub <- ddsSub[!noExp,]
protSub <- protSub[!noExp,]
#remove proteins with duplicated identifiers
protSub <- protSub[!duplicated(rowData(protSub)$name)]
geneOverlap <- intersect(rowData(protSub)$ensembl_gene_id, rownames(ddsSub))
ddsSub.vst <- varianceStabilizingTransformation(ddsSub)Calculate correlations between protein abundance and RNA expression
rnaMat <- assay(ddsSub.vst)
proMat <- assays(protSub)[["QRILC_combat"]]
rownames(proMat) <- rowData(protSub)$ensembl_gene_id
corTab <- lapply(geneOverlap, function(n) {
rna <- rnaMat[n,]
pro.raw <- proMat[n,]
res.raw <- cor.test(rna, pro.raw, use = "pairwise.complete.obs")
tibble(id = n,
p = res.raw$p.value,
coef = res.raw$estimate)
}) %>% bind_rows() %>%
arrange(desc(coef)) %>% mutate(p.adj = p.adjust(p, method = "BH"),
symbol = rowData(dds[id,])$symbol,
chr = rowData(dds[id,])$chromosome)Plot the distribution of correlation coefficient
ggplot(corTab, aes(x=coef)) + geom_histogram(position = "identity", col = "grey50", alpha =0.3, bins =100) +
geom_vline(xintercept = 0, col = "red", linetype = "dashed") +
xlab("Pearson's correlation coefficient") 
Most of the correlations are positive, which is reasonable.
ggplot(filter(corTab,!is.na(chr)), aes(x=coef)) + geom_histogram(position = "identity", col = "grey50", alpha =0.3, bins =100) +
geom_vline(xintercept = 0, col = "red", linetype = "dashed") + facet_wrap(~chr,ncol=3) +
xlab("Pearson's correlation coefficient") 
Similar trend can be observed for each chromosome.
For proteins in complex and not in complex
int_pairs = read_csv2("../output/int_pairs.csv")
sourceTab <- int_pairs %>%
dplyr::select(ProtA, ProtB, database) %>% gather(key = "prot", value = "id", -database) %>%
distinct(database, id) %>%
mutate(id = rowData(protCLL)$ensembl_gene_id[match(id, rownames(protCLL))])
comGene <- sourceTab$idcorTab <- corTab %>% mutate(inComplex = ifelse(id %in% comGene, "yes","no")) %>%
mutate(database = sourceTab[match(id, sourceTab$id),]$database)
ggplot(corTab, aes(x=coef, fill = inComplex, y=..density..)) + geom_histogram(position = "identity", col = "grey50", alpha =0.3, bins =100) +
geom_vline(xintercept = 0, col = "red", linetype = "dashed") +
xlab("Pearson's correlation coefficient") 
There's a slight trend that proteins not in complex tend to have higher protein-RNA expression correlations, which is reasonable, as proteins form complexes may be more regulated by buffering effect.
T-test
t.test(coef~inComplex, corTab, var.equal = TRUE )
Two Sample t-test
data: coef by inComplex
t = 11.823, df = 3289, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.07544552 0.10544501
sample estimates:
mean in group no mean in group yes
0.2536236 0.1631783 ggplot(corTab, aes(x=inComplex, y = coef))+geom_boxplot(aes(fill = inComplex)) + geom_point() +
theme_bw()
pList <- lapply(na.omit(unique(corTab$database)), function(n) {
plotTab <- filter(corTab, database %in% c(n,NA)) %>%
mutate(database = ifelse(is.na(database),"none",database)) %>%
mutate(database = factor(database, levels = c("none",n)))
p1 <- ggplot(plotTab, aes(x=coef, fill = database, y=..density..)) +
geom_histogram(position = "identity", col = "grey50", alpha =0.3, bins =100) +
geom_vline(xintercept = 0, col = "red", linetype = "dashed") +
xlab("Pearson's correlation coefficient")
p2 <- ggplot(plotTab, aes(x=database, fill = database, y=coef)) +
geom_boxplot() + geom_point() +
ylab("Pearson's correlation coefficient")
cowplot::plot_grid(p1,p2)
})
cowplot::plot_grid(plotlist = pList, ncol=1)
It seems the difference of correlations coefficient between proteins in complex and not in complex is stronger if we use more stringent criteria for proteins in complexes (annotated as in complexes by both Corum and reactome).
Per chromosome
ggplot(filter(corTab,!is.na(chr)), aes(x=coef,y=..density.., fill =inComplex)) + geom_histogram(position = "identity", col = "grey50", alpha =0.3, bins =100) +
geom_vline(xintercept = 0, col = "red", linetype = "dashed") + facet_wrap(~chr,ncol=3, scale = "free") +
xlab("Pearson's correlation coefficient") 
For proteins on chr12
plotTab <- filter(corTab, chr == "12")
ggplot(plotTab, aes(x=coef,y=..density.., fill =inComplex)) +
geom_histogram(position = "identity", col = "grey50", alpha =0.3, bins =100) +
geom_vline(xintercept = 0, col = "red", linetype = "dashed") +
xlab("Pearson's correlation coefficient") 
T-test
t.test(coef~inComplex, plotTab, var.equal = TRUE )
Two Sample t-test
data: coef by inComplex
t = 1.5986, df = 176, p-value = 0.1117
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.01179482 0.11236151
sample estimates:
mean in group no mean in group yes
0.3556980 0.3054147 ggplot(plotTab, aes(x=inComplex, y = coef))+geom_boxplot(aes(fill = inComplex)) + geom_point() +
theme_bw()
For proteins on chr19
plotTab <- filter(corTab, chr == "19")
ggplot(plotTab, aes(x=coef,y=..density.., fill =inComplex)) +
geom_histogram(position = "identity", col = "grey50", alpha =0.3, bins =100) +
geom_vline(xintercept = 0, col = "red", linetype = "dashed") +
xlab("Pearson's correlation coefficient") 
T-test
t.test(coef~inComplex, plotTab, var.equal = TRUE )
Two Sample t-test
data: coef by inComplex
t = 3.2206, df = 229, p-value = 0.001465
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.0350777 0.1456474
sample estimates:
mean in group no mean in group yes
0.2588620 0.1684995 ggplot(plotTab, aes(x=inComplex, y = coef))+geom_boxplot(aes(fill = inComplex)) + geom_point() +
theme_bw()
Protein-RNA correlation across proteins
load("../output/exprCNV.RData")overPat <- intersect(unique(allProtTab$patID), unique(allRnaTab$patID))
plotList <- lapply(overPat, function(pat) {
ttPro <- dplyr::filter(allProtTab, patID %in% pat) %>%
select(id, expr, ChromID)
ttRna <- dplyr::filter(allRnaTab, patID %in% pat) %>%
select(id, expr) %>% dplyr::rename(exprRna = expr)
ttComb <- left_join(ttRna, ttPro, by = "id") %>%
filter(!is.na(exprRna),!is.na(expr)) %>%
mutate(chr12 = ifelse(ChromID %in% "chr12", "yes","no"))
q <- ggplot(ttComb, aes(x=exprRna, y=expr, col = chr12)) + geom_point() +
ggtitle(pat) +
theme_bw()
q
})
makepdf(plotList, "protRNACor_eachPat.pdf", ncol=3, nrow=3, height = 12, width = 12)
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.16
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] gridExtra_2.3 forcats_0.5.0
[3] stringr_1.4.0 dplyr_1.0.0
[5] purrr_0.3.4 readr_1.3.1
[7] tidyr_1.1.0 tibble_3.0.3
[9] ggplot2_3.3.2 tidyverse_1.3.0
[11] proDA_1.1.2 DESeq2_1.26.0
[13] SummarizedExperiment_1.16.1 DelayedArray_0.12.3
[15] BiocParallel_1.20.1 matrixStats_0.56.0
[17] Biobase_2.46.0 GenomicRanges_1.38.0
[19] GenomeInfoDb_1.22.1 IRanges_2.20.2
[21] S4Vectors_0.24.4 BiocGenerics_0.32.0
[23] jyluMisc_0.1.5 limma_3.42.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.8 Hmisc_4.4-0
[4] fastmatch_1.1-0 drc_3.0-1 workflowr_1.6.2
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[28] haven_2.3.1 xfun_0.15 crayon_1.3.4
[31] RCurl_1.98-1.2 jsonlite_1.7.0 genefilter_1.68.0
[34] survival_3.2-3 zoo_1.8-8 glue_1.4.1
[37] survminer_0.4.7 gtable_0.3.0 zlibbioc_1.32.0
[40] XVector_0.26.0 car_3.0-8 abind_1.4-5
[43] scales_1.1.1 mvtnorm_1.1-1 DBI_1.1.0
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[55] Formula_1.2-3 DT_0.14 httr_1.4.1
[58] htmlwidgets_1.5.1 fgsea_1.12.0 gplots_3.0.4
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[103] stringi_1.4.6 lattice_0.20-41 Matrix_1.2-18
[106] shinyjs_1.1 KMsurv_0.1-5 vctrs_0.3.1
[109] pillar_1.4.6 lifecycle_0.2.0 data.table_1.12.8
[112] cowplot_1.0.0 bitops_1.0-6 httpuv_1.5.4
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