In jedick/JMDplots: Plots from Papers by Jeffrey M. Dick

library(JMDplots)
library(knitr)
## use pngquant to reduce size of PNG images
knit_hooks$set(pngquant = hook_pngquant)
pngquant <- "--speed=1 --quality=0-25"
# in case pngquant isn't available (R-Forge?)
if (!nzchar(Sys.which("pngquant"))) pngquant <- NULL 

This vignette from the R package JMDplots version r packageDescription("JMDplots")$Version shows chemical metrics for proteins that are differentially expressed in cancer compared to normal tissue (data from The Human Protein Atlas). The analysis is described in more detail in a paper (Dick, 2021). Abbreviations:

• Z_C – average oxidation state of carbon.
• n_H2O – stoichiometric hydration state per residue (Dick et al., 2020) computed using the QEC basis species (glutamine, glutamic acid, cysteine, H~2~O, O~2~).

options(width = 90)

datasets <- pdat_HPA()

pdat1 <- lapply(datasets, pdat_HPA)
comptab1 <- lapply(pdat1, get_comptab)
comptab2 <- lapply(pdat1, get_comptab, "nAA", "MW")

Differences are calculated as (median value for up-regulated proteins) - (median value for down-regulated proteins). Dashed lines enclose the 50% credible region for highest probability density.

par(mar = c(4, 4, 1, 1))
diffplot(comptab1, labtext = NA)

In the table, values of ΔZ_C and Δn_H2O are multiplied by 1000, values of ΔMW are multiplied by 100, and negative values are shown in bold. Abbreviations:

• n_down – number of down-regulated proteins; n_up – number of up-regulated proteins.
• n_AA – protein length; MW – molecular weight per residue.

library(xtable)
out <- xsummary2(comptab1, comptab2)
# round values and include dataset tags
tags <- sapply(sapply(strsplit(datasets, "="), "[", -1), paste, collapse = ";")
out <- cbind(out[, 1:2], tags = tags, out[, 3:16])
out[, 6:17] <- round(out[, 6:17], 4)
file <- paste0("HPA.csv")
write.csv(out, file, row.names = FALSE, quote = 2)

Data Sources

a. – r. Human Protein Atlas [@UFH+15].

References

jedick/JMDplots documentation built on April 12, 2025, 1:35 p.m.