003_plot.Rmd

---
title: "Correlation, network and heatmap plots"
subtitle: "...and finally, the plots"
date: "`r Sys.Date()`"
output: html_document
header-includes: |
    \usepackage{caption}
    \usepackage{placeins}
    \usepackage{subfig}
---
## Load R packages

```{r message=FALSE}
library(tidyverse)
library(readxl)
library(stringr)
library(psych)
library(gdata)
library(qgraph)
library(kableExtra)
library(ComplexHeatmap)
library(circlize)
```
## Load data from previous steps

```{r}
load(file = paste("./saved_data/time_span_of_interest.RData", sep=""))
time_span_of_interest
```

Step 1
```{r}
if (time_span_of_interest=="timepoint_b"){
  fn ="001_read_and_format_data_and_stat_testing_tpb.RData"
}else if(time_span_of_interest=="timeinterval_cde"){
  fn ="001_read_and_format_data_and_stat_testing_ticde.RData"
}

loaded_objects_from_step_1 = load(file = paste("./saved_data/",fn, sep=""))
loaded_objects_from_step_1
```

Step 2
```{r}
if (time_span_of_interest=="timepoint_b"){
  fn ="002_format_results_of_statistical_testing_tpb.RData"
}else if(time_span_of_interest=="timeinterval_cde"){
  fn ="002_format_results_of_statistical_testing_ticde.RData"
}
loaded_objects_from_step_2 = load(file = paste("./saved_data/",fn, sep=""))
loaded_objects_from_step_2
```


## Heatmap
```{r complexHeatmapConc, fig.cap="The color in each cell of this heatmap indicates the level at which a given cytokine or growth factor changes its interaction with other cytokines and growth factors. The color (and intesity) signifies the sum of the significant and standardized z-scores (mesures of change of correlation) for individual cytokines and growth factors.  Each cytokine/growthfactor name found cell is trailed by an underscore and a number. The number indicates how many other cytokines it interacts with so that weather the sum is due to a few big interactions or many small. Cytokines with no significant change in correlation between children with and without clinical outcomeis excluded (grey)"}
small_mat = as.matrix(ranked_z_tb.0.0)
#ranked_z_tb.0.0 %>% print(.,n = 50)
small_mat_text = as.matrix(ranked_peps_tb.0.0)
max <- max(small_mat)
min <- min(small_mat)
col_fun = colorRamp2(c(min,0.01, max), c("lightgrey", "green", "red"))
Heatmap(small_mat, name = "sum of z-score", col = col_fun,
show_column_dend = FALSE, show_row_dend = FALSE,
#width = unit(15, "cm"), height = unit(15, "cm"),
    cell_fun = function(j, i, x, y, width, height, fill) {
    if(small_mat[i, j] > 0)
        grid.text(sprintf("%s", small_mat_text[i, j]), x, y, gp = gpar(fontsize = 7))#%.1f
})
```

## Correlation plots with difference in correlation
For each plot triplet per row there is one plot for group 0 , one for group 1 and one for the difference between 0 and 1

```{r plot corrplots with difference in correlation, out.width = "30%",fig.cap="Some caption",fig.show='hold'}
for (i in 1:length(corr_lst_clin_group_0.rval)){
   corrplot::corrplot(corr_lst_clin_group_0.rval[[i]],
                      tl.pos="td",
                      tl.col="darkgrey",
                      mar=c(0,0,1,0),
                      number.cex=3,
                      tl.cex=1,  
                      type="upper",
                      pch.cex=0.8, 
                      diag=F,
                      cl.pos="n",
                      addgrid.col=NA,
                      title=paste(group_counts_0$clin_grouping[i],",0, numb samples =  ",corr_lst_clin_group_0.n[[i]],sep=""))
  
    corrplot::corrplot(corr_lst_clin_group_1.rval[[i]],
                      tl.pos="td",
                      tl.col="darkgrey",
                      mar=c(0,0,1,0),
                      number.cex=3,
                      tl.cex=1,  
                      type="upper",
                      pch.cex=0.8, 
                      diag=F,
                      cl.pos="n",
                      addgrid.col=NA,
                      title=paste(group_counts_1$clin_grouping[i],",1, numb samples =  ",corr_lst_clin_group_1.n[[i]],sep=""))
    
    #plot one plot fo rchange in correllation
    A <- paired_corr.lst.zval[[i]] 
    #Normalized Data
    normalized = (A-min(A,na.rm=T))/(max(A,na.rm=T)-min(A,na.rm=T))
    B <- paired_corr.lst.pval[[i]]
    sig_element = ifelse(B < 0.05, normalized, NA)
    sig_element[is.na(sig_element)] <- 0 
    
    corrplot::corrplot(sig_element,
                       tl.pos="td",
                       tl.col="darkgrey", 
                       mar=c(0,0,1,0),
                       number.cex=3,tl.cex=0.8,
                       type="upper",
                       pch.cex=0.8, diag=F,cl.pos="n")
        
      
      # Why am I not using cor_diff.lst here?
      # 
      # ,cor_diff.lst[[i]]
      #                 tl.pos="td",
      #                 tl.col="darkgrey",
      #                 mar=c(0,0,1,0),
      #                 number.cex=3,
      #                 tl.cex=1,  
      #                 type="upper",
      #                 pch.cex=0.8, 
      #                 diag=F,
      #                 cl.pos="n",
      #                 addgrid.col=NA,
      #                 title=paste(group_counts_1$clin_grouping[i],",1vs2",sep="")
      # 

                 
}

```


## Network plots with difference in correlation
For each plot triplet per row there is one plot for group 0 , one for group 1 and one for the difference between 0 and 1

```{r plot networks with difference in correlation, out.width = "30%",fig.cap="Some caption",fig.show='hold'}
#plot maternal delivery "other"
for (i in 1:length(corr_lst_clin_group_0.rval)){
  
  qgraph(sign_corr_lst_clin_group_0[[i]],
         graph= 'default',
         layout = "spring",
         edge.labels = TRUE,
         palette= "ggplot2",
         groups=sign_cyts_and_gfs.lst_0[[i]],
         legend=F,
         bg='lightgrey',
         title.cex=1.2,
         title=paste(group_counts_0$clin_grouping[i],",0, numb samples =  ",corr_lst_clin_group_0.n[[i]],sep=""))
  
    qgraph(sign_corr_lst_clin_group_1[[i]],
         graph= 'default',
         layout = "spring",
         edge.labels = TRUE,
         palette= "ggplot2",
         groups=sign_cyts_and_gfs.lst_1[[i]],
         legend=F,
         bg='lightgrey',
         title.cex=1.2,
         title=paste(group_counts_1$clin_grouping[i],",1, numb samples =  ",corr_lst_clin_group_1.n[[i]],sep=""))
    
      qgraph(cor_diff.lst[[i]],
         graph= 'default',
         layout = "spring",
         edge.labels = TRUE,
         palette= "ggplot2",
         groups=cor_diff_groups.lst[[i]],#sign_cyts_and_gfs.lst_0
         legend=F,
         bg='lightgrey',
         title.cex=1.2,
         title=paste(group_counts_1$clin_grouping[i],",0vs1",sep=""))
   
}
```