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 breast cancer compared to normal tissue.
The analysis is described in more detail in a paper (Dick, 2021).
Abbreviations:
datasets <- pdat_breast(2020)
pdat1 <- lapply(datasets, pdat_breast) 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% confidence region for highest probability density.
par(mar = c(4, 4, 1, 1)) diffplot(comptab1, labtext = NA)
In the table, values of ΔZC and ΔnH2O are multiplied by 1000, values of ΔMW are multiplied by 100, and negative values are shown in bold. Abbreviations:
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("breast.csv") write.csv(out, file, row.names = FALSE, quote = 2)
Gene names or other identifiers were converted to UniProt accession numbers using the UniProt mapping tool.
a. Extracted from Supporting Information RTF files of @AMG+08. Proteins identified by any number of peptides in both cancer and matched periphery were excluded; of the remaining proteins those identified by at least two peptides were used. b. Table S2(a) of @CIR+10 (proteins differentially abundant at or above 99% confidence level). c. Table 4 of @SRG+10. d. Tables 1 and 2 of @HTP+11. e. f. Supporting Table 6 of @GTM+12. g. Table S1 of @LLL+13. h. i. Table 2 of @SRS+13. For DCIS (3 patients), proteins were classified as up/down regulated if 2 or more ratios were greater/less than 1, and no ratios were less/greater than 1. For IC (4 patients), proteins were classified as up/down regulated if 3 or more ratios were greater/less than 1, and no ratios were less/greater than 1. j. Extracted from Table S2 of @GSB+14. Values in all LFQ columns (distant, near, tumor) were quantile normalized, then the ratio between tumor and distant was calculated. Proteins with normalized LFQ ratios > 1.2 or < 1/1.2 and p-value < 0.05 were identified as differentially expressed. k. Supplementary Tables 12, 13, 14 and 15 of @PPH+14, filtered to include proteins that are up- or down-regulated in all subtypes (LUM, LUMHER, HER, TN). l. Supplemental Data S3 of @CVJ+15, filtered to include proteins with min and max credible intervals for expression ratios that are both <1 or >1. m. n. Supplementary Table S3 of @PGT+16. o. Table S4A of @PBR+16. p. Supplemental Table 4 of @BST+17, filtered to include proteins with p-value < 2. q. r. Tables S5-1 (differentially expressed proteins for 52 tumor / non-cancerous tissue pairs) and S5-2 (13 basal-like tumor / non-cancerous tissue pairs) of @TZD+18, filtered to include proteins with log~2~ fold change > 1 or < -1. s. t. Supplementary File S1 of @GCS+19 (PT: primary breast tissue; NCT: non tumor contralateral breast tissue; ANT: non tumor adjacent breast tissue). u. Supplementary Table 2 of @LLC+19. v. w. Supplementary Tables S2 and S3 of @LLF+20.
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