# PathoStat package Introduction

#### Solaiappan Manimaran

#### 2016-10-17

The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis.

The package includes:

    1. Relative Abundance plots (Stacked Bar Plot, Heatmap)
    2. Diversity plots (Alpha and Beta diversity, Exploratory Tree, BiPlot,
        Co-Occurrence)
    3. Differential Expression (Expression Plots, Limma)
    4. Confidence Region Plots
    5. PCA plots
    6. PCoA plots
    7. Alluvial Plots for longitudinal data
    8. Core OTU analysis

`runPathoStat` is the pipeline function that generates the PathoStat report and launches shiny app when in interactive mode. It combines all the functions into one step.

## Installation

To begin, install Bioconductor and simply run the following to automatically install PathoStat and all the dependencies, except pandoc, which you have to manually install as follows.

    source("http://bioconductor.org/biocLite.R")
    biocLite("PathoStat")

Install ‘pandoc’ package by following the instructions at the following URL: http://pandoc.org/installing.html

Rstudio also provides pandoc binaries at the following location for Windows, Linux and Mac: https://s3.amazonaws.com/rstudio-buildtools/pandoc-1.13.1.zip

If all went well you should now be able to load PathoStat. Here are some examples usage of the pipeline.

### Example 1: Run PathoStat

    runPathoStat()

### Example 2: L1 set

    example2_data_dir <- system.file("data", package = "PathoStat")
    # Load and run PathoStat
    pstat <- loadPstat(indir=example2_data_dir, infileName="pstat_data_2_L1.rda")
    runPathoStat(pstat)

### Example 2: L2 set

    example2_data_dir <- system.file("data", package = "PathoStat")
    # Load and run PathoStat
    pstat <- loadPstat(indir=".", infileName="pstat_data_2_L2.rda")
    runPathoStat(pstat)