ttest.Rmd

---
title: 'TENTAKKL test'
author: Sean Conlan
output:
  html_document:
    toc: true
    df_print: paged
---

# Introduction
This R notebook displays a couple of bar charts based on list output from tentakkl 

## Input

* **test5.txt** - 20 samples, list output

## Output

* None

# Setup
## Paths
```{r}
project_path <- getwd()
ttable <- "test5.txt"
```

## Load libraries
```{r echo=FALSE}
#load libraries
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, error = FALSE)
knitr::opts_chunk$set(fig.width=12, fig.height=8)
library(ggplot2); packageVersion("ggplot2")
library(RColorBrewer); packageVersion("RColorBrewer")
#library(readxl); packageVersion("readxl")
library(reshape2); packageVersion("reshape2") #now a bit outdated, but how I learned to reshape data
library(dplyr); packageVersion("dplyr")
library(tidyverse); packageVersion("tidyverse")
```

## Load table
```{r}
dat<-read.table(file.path(project_path,ttable),sep="\t",header=T)
#make sure taxa are ordered like in the file
dat.tr<-dat %>%
    mutate(taxon = fct_reorder(taxon, order))

#these should be named, don't be lazy like me
pal<-c("gray","black","antiquewhite3",brewer.pal(3, "RdPu"),brewer.pal(5, "Blues"),"green","Yellow",brewer.pal(6, "PuRd"),"gold",brewer.pal(3, "Purples"))
```

## Plot bracken normalized barplot

In this plot, the total bracken reads are used as the denominator for normalization but we have also added the unclassified reads pulled from the matched kraken report. This results in a chart where we can visualize classified read relative abundances without losing a sense of the unclassified fraction.

```{r fig.width=8,fig.height=6}
p1 =  ggplot(dat.tr, aes(sample, b_fraction, fill=taxon)) +
        geom_bar(stat="identity", position="stack") + 
        ylab("Relative Abundance")+  xlab("Timepoint")  +
        ggtitle("Relative abundance by Subject") +
        #add_barplot_elements()  +
        #scale_colour_manual(values=classic_palette) + 
        scale_fill_manual(values=pal) +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
        guides(fill = guide_legend(ncol = 1)) +
        theme(panel.background = element_rect(fill="darkgray")) +
        theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank())
        #facet_grid(vars(site_specific), vars(subject_id),switch="y")
        #facet_wrap(~subject_id+site_specific, ncol=12)

p1
```

## Plot kraken normalized barplot

In this plot, the total kracken reads are used as the denominator for normalization (classified+unclassified). As above, we have added the unclassified reads pulled from the matched kraken report. In this case, unclassified reads are shown as part of the relative abundances.

```{r fig.width=8,fig.height=6}
p2 =  ggplot(dat.tr, aes(sample, k_fraction, fill=taxon)) +
        geom_bar(stat="identity", position="stack") + 
        ylab("Relative Abundance")+  xlab("Timepoint")  +
        ggtitle("Relative abundance by Subject") +
        #add_barplot_elements()  +
        #scale_colour_manual(values=classic_palette) + 
        scale_fill_manual(values=pal) +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
        guides(fill = guide_legend(ncol = 1)) +
        theme(panel.background = element_rect(fill="darkgray")) +
        theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank())
        #facet_grid(vars(site_specific), vars(subject_id),switch="y")
        #facet_wrap(~subject_id+site_specific, ncol=12)

p2
```