---
title: "High Glucose"
output:
  html_vignette:
    mathjax: null
vignette: >
  %\VignetteIndexEntry{High Glucose}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
bibliography: cpdat.bib
csl: peerj.csl
---

```{r setup, include=FALSE}
library(canprot)
library(CHNOSZ)
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 [canprot](https://github.com/jedick/canprot) version `r packageDescription("canprot")$Version` shows compositional metrics and phylostrata for proteins that are differentially expressed in high-glucose compared to control conditions.
Abbreviations:

  * <i>Z</i><sub>C</sub> &ndash; carbon oxidation state; <i>n</i><sub>H<sub>2</sub>O</sub> &ndash; stoichiometric hydration state per residue ([<span title="Distinct trends in chemical composition of proteins from metagenomes in redox and salinity gradients">Dick et al., 2020</span>](https://doi.org/10.1101/2020.04.01.020008); [<span title="Water as a reactant in the differential expression of proteins in cancer">Dick, 2020</a>](https://doi.org/10.1101/2020.04.09.035022)).
  * PS &ndash; phylostratum; <i>n</i><sub>AA</sub> &ndash; protein length; MW &ndash; molecular weight per residue.

Gene ages, expressed as phylostratum numbers, are taken from [<span title="Altered interactions between unicellular and multicellular genes drive hallmarks of transformation in a diverse range of solid tumors">Trigos et al., 2017</span>](https://doi.org/10.1073/pnas.1617743114) (TPPG17) or [<span title="Towards consensus gene ages ">Liebeskind et al, 2016</span>](https://doi.org/10.1093/gbe/evw113) (LMM16).

```{r options, echo=FALSE}
options(width = 90)
```

<style type="text/css">
body {
  max-width: 800px;
  margin-left: auto;
  margin-right: auto;
}
</style>

```{r datasets}
datasets <- pdat_glucose(2020)
```

```{r comptab, results="hide", message=FALSE, echo = FALSE}
pdat1 <- lapply(datasets, pdat_glucose)
comptab1 <- lapply(pdat1, get_comptab)
comptab2 <- lapply(pdat1, get_comptab, "PS_TPPG17", "PS_LMM16")
comptab3 <- lapply(pdat1, get_comptab, "nAA", "MW")
```

Differences are calculated as (median value for up-regulated proteins) - (median value for down-regulated proteins), except for phylostrata, where mean values are used.
Dashed lines enclose the 50% credible region for highest probability density.

```{r diffplot, fig.width=9, fig.height=8, fig.align = "center", echo = FALSE, pngquant = pngquant}
layout(matrix(c(0, 2, 1, 2, 1, 3, 0, 3), nrow = 2))
par(cex = 1, mar = c(4, 4, 1, 1), mgp = c(2.5, 1, 0))
diffplot(comptab1, labtext = NA)
diffplot(comptab2, c("PS_TPPG17", "PS_LMM16"))
diffplot(comptab3, c("nAA", "MW"), labtext = NA)
```

In the table, values of &Delta;<i>Z</i><sub>C</sub> and &Delta;<i>n</i><sub>H<sub>2</sub>O</sub> are multiplied by 1000, values of &Delta;PS and &Delta;MW are multiplied by 100, and negative values are shown in bold.
Abbreviations:

  * _n_~down~ &ndash; number of down-regulated proteins; _n_~up~ &ndash; number of up-regulated proteins; HUVEC &ndash; human umbilical vein endothelial cells; eMP &ndash; endothelial-derived microparticles.

```{r xsummary, results="asis", echo = FALSE}
library(xtable)
out <- xsummary2(comptab1, comptab2, comptab3)
# round values and include dataset tags
tags <- sapply(sapply(strsplit(datasets, "="), "[", -1), paste, collapse = ";")
out <- cbind(out[, 1:2], tags = tags, out[, 3:19])
out[, 6:20] <- round(out[, 6:20], 4)
write.csv(out, "glucose.csv", row.names = FALSE, quote = 2)
```

## Data Sources
__a__. __b__. __c__. Supporting Information Table of @PW08, filtered to include proteins with expression ratios < 0.9 or > 1.1 and with _p_-values < 0.05.
__d__. Supplemetary Table ST4 of @WCM+09, filtered to include the proteins with ANOVA _p_-value < 0.01 (red- and blue-highlighted rows in the source table), and applying the authors' criterion that proteins be identified by 2 or more unique peptides in at least 4 of the 8 most intense LC-MS/MS runs.
__e__. [Supplementary Table](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2818370/) of @WFSL09, filtered to include proteins with fold change > 1.2 or < 0.8.
__f__. Table 1 of @MFD+10.
__g__. __h__. Table 1 of @CCC+12.
__i__. Proteins identified as differentially abundant in Supporting Information Table S5 of @SFG+12, filtered to include proteins with fold change > 2 or < 0.5.
__j__. __k__. Table 1 of @CCCC13.
__l__. Supplementary Table 1 of @CCW+13, filtered to include proteins with average fold change > 2.5 or < 0.4.
__m__. Supporting Information Table S4 of @LDB+15 for up- (Cluster 1) and down- (Cluster 5) regulated proteins.
__n__. __o__. Electronic supplementary material Table 1 of @BTX+17.
__p__. __q__. Supplementary Table S1 of @SFKD17, filtered to include proteins with fold change > 2 or < 0.5 (ratios were computed from medians of iBAQ values for three replicates after quantile normalization).
__r__. Supplementary Figure 1 of @HGC+18.
__s__. Supplementary Tables 1 and 2 of @IXA+19.
__t__. __u__. Supplementary Table 2 of @MHP+20 (sheets "H9c2" and "HEK").
__v__. __w__. __x__. __y__. Treatment with high glucose (12.5 mmol/L) or high mannitol (7.0 mmol/L + 5.5 mmol/L glucose) followed by insulin, compared to normal glucose (5.5 mmol/L) followed by insulin. Source: Supplementary Tables S2--S6 of @MPR+20 (proteins uniquely identified in treatment and control conditions).

## References
