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

```{r setup, include=FALSE}
library(canprot)
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 
# read 'basis' option
basis <- getOption("basis")
```

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 liver cancer compared to normal tissue.
The analysis is described in more detail in a manuscript ([<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)).
Abbreviations:

  * <i>Z</i><sub>C</sub> &ndash; average oxidation state of carbon.
  * <i>n</i><sub>H<sub>2</sub>O</sub> &ndash; stoichiometric hydration state per residue ([<span title="Uncovering chemical signatures of salinity gradients through compositional analysis of protein sequences">Dick et al., 2020</span>](https://doi.org/10.5194/bg-2020-146)) computed using the **`r basis`** basis species (`r basis.text(basis)`, H~2~O, O~2~).
  * 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).

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

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

```{r comptab, results="hide", message=FALSE, echo = FALSE}
pdat1 <- lapply(datasets, pdat_liver)
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% confidence 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; EGF &ndash; epidermal growth factor; G1, G2, G3 &ndash; well-differentiated, moderately differentiated, poorly differentiated tumors; T1, T2, T3 &ndash; T1N0M0, T2N0M0, T3N0M0 tumor-node-metastasis classification.

```{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)
file <- paste0("liver_", basis, ".csv")
write.csv(out, file, row.names = FALSE, quote = 2)
```

## Data Sources
Gene names or other identifiers were converted to UniProt accession numbers using the <a href="https://www.uniprot.org/mapping/">UniProt mapping tool</a>, except for IPI accession numbers, which were converted using the <a href="https://david-d.ncifcrf.gov/conversion.jsp">DAVID 6.7 conversion tool</a>.

__a__. Table III of @LHT+04.
__b__. Tables 2 and 3 of @BLP+05.
__c__. Table 2 of @LTZ+05.
__d__. Table 1 of @DTS+07, including proteins identified in either tumor homogenates or laser microdissected samples.
__e__. Supplemental Table S1 of @SXS+07.
__f__. Tables 1--3 of @CHN+08.
__g__. Supplemental Tables S3A and S3C of @RLA+10, filtered to include proteins with _p_-value < 0.05.
__h__. __i__. IPI numbers from Supplemental Tables S3 (nuclear proteins) and S4 (cytoskeletal proteins) of @LMG+11, filtered to include proteins with median fold-change > 2 or < 0.5.
__j__. Supplemental Table 5 of @LRL+12.
__k__. Supplementary Table 3 of @KOK+13.
__l__. Supplemental Data S5 (sheet "LF_proteins") of @MBK+13.
__m__. Supporting Information SI-S2 of @XWS+14.
__n__. Table 1 of @BSG15.
__o__. Supplementary Table S1 of @RPM+15, filtered to include proteins with > 1 peptide used for quantification and fold change > 2 in either direction.
__p__. __q__. __r__. Supplementary Data Table S2 of @NBM+16 (sheets "G1 vs C", "G2 vs C", and "G3 vs C"), filtered to include proteins with _p_-value < 0.05, quantified in at least half of both tumor and control samples, and median log~2~ fold change > 1 or < -1.
__s__. Supporting Information Table S-4 (sheet "Filtered protein list") of @NMB+16.
__t__. __u__. __v__. Supplementary Tables S3--S5 of @QXC+16.
__w__. Table 4 of @GJZ+17.
__x__. Supplementary Table S5 of @GWS+17.
__y__. Supplementary Table 5 of @QPP+17, filtered to include proteins with median fold change > 2 or < 0.5 (UniProt IDs from Supplementary Table 1).
__z__. __A__. __B__. __C__. Supplementary Table S3 of @WLL+17, filtered to include proteins with _p_-value < 0.05 and fold change > 2 or < 0.5.
__D__. Supplemental Table S2 of @BOK+18 (data for tumor vs. peritumor), filtered to include proteins with _q_-value < 0.05, quantified in at least 3 tumors, same direction of change in all tumors, and median log~2~ fold change > 1 or < -1.
__E__. Supplementary Table S2 of @YXZ+18.
__F__. [Dataset](https://github.com/saezlab/Berndt_Egners_Mastrobuoni/blob/master/data/Proteom-Daten%20Bl6%20vs%2016w%20AG%20Kempa.xlsx) of @BEM+20, filtered to include proteins quantified in more than half of each of control and cancer samples and median fold change > 2 or < 0.5.
__G__. __H__. Supplemental Table S3 of @GZD+19 (sheets "1. 1,274 DF proteins" and "2. 859 DF phosphoproteins"), filtered to include proteins with log~2~ fold change > 1 or < -1.
__I__. Supplementary Table 6 of @JSZ+19.
__J__. Supporting Information Table S5 of @ZZL+19 (sheet "c_proteins"), filtered to include proteins quantified in at least half of tumor and normal samples and with fold change > 2 or < 0.5. 
__K__. Supporting Information Table S4 of @GZL+20 (sheet "B_Molecule").
__L__. __M__. Supplementary Tables S1--S2 (proteins with differential expression between non-tumor and tumor regions) and S3--S4 (all proteins identified in each region) of @SCL+20. Common accession numbers in Tables S3 and S4 were eliminated to yield proteins uniquely identified in either tumor or non-tumor regions.

## References
