LipidomR.Rmd

---
title: "lipidomeR"
author: "Mona Khorani"
date: "February 2, 2022"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#-------------------------------------------------------------
# Load the humanlipidome data set.
```{r}
install.packages( "lipidomeR" )
library( "lipidomeR" )
```
#cancerlipidome
# Load the cancerlipidome data set.
```{r}

# Convert the data into wide format, where each lipid is one column and
# each sample is one row.
dataa=read.csv("CFC-Wide-NRTRepRem145-HipCortxMixPosNTIAPareLogProg08Ap2022.csv")
Names=read.csv("CFC-Names-NRTRepRem145-HipCortxMixPosNTIAPareLogProg08Ap2022.csv")
```
# Inspect the data frame.
# View( cancerlipidome.wide )
# Create a mapping of the lipid names.
```{r}
names.mapping <- map_lipid_names(x = unique(Names$Names ) )
```


# Compute the regression models.
```{r}
result.limma <-
   compute_models_with_limma(
       x = dataa,
       dependent.variables = names.mapping$"Name.checked",
       independent.variables = c( "Group" )
   )
```

## Fitting models: ~ Group

```{r}
png(file="Fig1.tiff", width=3000, height=1300, res=350)

figure.output <-
   heatmap_lipidome_from_limma(
       x = result.limma$"model",
       names.mapping = names.mapping,
       axis.x.carbons = TRUE,
       class.facet = "wrap",
       plot.all = TRUE,
       plot.individual = TRUE,
       print.figure = TRUE,
       scales = "free",
       space = "free",
       wrap.contrast.name = TRUE,class.subset = c('PE', 'PC', 'DG')
   )
print( figure.output[[ "GroupTreatment" ]] )
graphics.off()
```
# Create the figure of all lipids and factors.
```{r}
png(file="Fig2.tiff", width=3500, height=3500, res=350)

figure.output <-
   heatmap_lipidome_from_limma(
       x = result.limma$"model",
       names.mapping = names.mapping,
       axis.x.carbons = FALSE,
       class.facet = "wrap",
       plot.all = TRUE,
       plot.individual = FALSE,
       print.figure = TRUE,
       scales = "fixed",
       space = "fixed",
       class.subset = c('DG','TG', 'PG', 'MG'),
       range.min.N.double.bonds = 0,
       p.val.label.bg.size=2
       
   )
print( figure.output[[ "GroupTreatment" ]] )
graphics.off()
```


```{r}
png(file="Fig3.tiff", width=3700, height=3700, res=400)

figure.output <-
   heatmap_lipidome_from_limma(
       x = result.limma$"model",
       names.mapping = names.mapping,
       axis.x.carbons = FALSE,
       class.facet = "wrap",
       plot.all = TRUE,
       plot.individual = FALSE,
       print.figure = TRUE,
       scales = "fixed",
       space = "fixed",
       wrap.contrast.name = TRUE,
       class.subset = c('Cer','Cer-2OH', 'Cer-3OH', 'HexCer', 'HexCer-2OH'),
       range.min.N.double.bonds = 0,
   )
print( figure.output[[ "GroupTreatment" ]] )
graphics.off()
```

# Create factor-specific figures.
```{r}
png(file="Fig f2-3.tiff", width=4000, height=4000, res=450)

figure.output <-
   heatmap_lipidome_from_limma(
       x = result.limma$"model",
       names.mapping = names.mapping,
       axis.x.carbons = TRUE,
       class.facet = "wrap",
       plot.all = TRUE,
       plot.individual = TRUE,
       scales = "fixed",
       space = "fixed",
       range.min.N.double.bonds = 0
   )
print( figure.output[[ "GroupTreatment" ]] )
graphics.off()
   
```