Introduction

PathoStat is a R shiny package, designed for performing Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files.

The package includes:

• Data Summary and Filtering
• Relative Abundance plots (Stacked Bar Plot, Heatmap)
• Multiple species boxplot visualization
• Diversity analysis (Alpha and Beta diversity)
• Differential Expression (DEseq2, edgeR)
• Dimension Reduction (PCA, PCoA)
• Biomarker identification

Installation

To begin, install Bioconductor and simply run the following to automatically install PathoStat and all the dependencies as follows.

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("PathoStat")

If you want to install the latest development version of PathoStat from Github, use devtools to install it as follows:

require(devtools)
install_github("mani2012/PathoStat", build_vignettes=TRUE)

Run Pathostat

require(PathoStat)
runPathoStat()

Session info

#> R version 3.6.1 (2019-07-05)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.10-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.10-bioc/R/lib/libRlapack.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] compiler_3.6.1  magrittr_1.5    htmltools_0.4.0 tools_3.6.1    
#>  [5] yaml_2.2.0      Rcpp_1.0.2      stringi_1.4.3   rmarkdown_1.16 
#>  [9] knitr_1.25      stringr_1.4.0   digest_0.6.22   xfun_0.10      
#> [13] rlang_0.4.1     evaluate_0.14