# Getting Started with the peakPantheR package

#### 2019-10-01

#### Package

peakPantheR 1.0.0

Package: peakPantheR
Authors: Arnaud Wolfer

Package for Peak Picking and ANnoTation of High resolution Experiments in R, implemented in `R` and `Shiny`

# 1 Overview

`peakPantheR` implements functions to detect, integrate and report pre-defined features in MS files (e.g. compounds, fragments, adducts, …).

It is designed for:

- Real time feature detection and integration (see Real Time Annotation)
  - process `multiple` compounds in `one` file at a time
- Post-acquisition feature detection, integration and reporting (see Parallel Annotation)
  - process `multiple` compounds in `multiple` files in `parallel`, store results in a `single` object

`peakPantheR` can process LC/MS data files in NetCDF, mzML/mzXML and mzData format as data import is achieved using Bioconductor’s mzR package.

# 2 Installation

To install `peakPantheR` from Bioconductor:

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

    BiocManager::install("peakPantheR")

Install the development version of `peakPantheR` directly from GitHub with:

    # Install devtools
    if(!require("devtools")) install.packages("devtools")
    devtools::install_github("phenomecentre/peakPantheR")

# 3 Input Data

Both real time and parallel compound integration require a common set of information:

- Path(s) to `netCDF` / `mzML` MS file(s)
- An expected region of interest (`RT` / `m/z` window) for each compound.

## 3.1 MS files

For demonstration purpose we can annotate a set a set of raw MS spectra (in NetCDF format) provided by the faahKO package. Briefly, this subset of the data from (Saghatelian et al. 2004) invesigate the metabolic consequences of knocking out the fatty acid amide hydrolase (FAAH) gene in mice. The dataset consists of samples from the spinal cords of 6 knock-out and 6 wild-type mice. Each file contains data in centroid mode acquired in positive ion mode form 200-600 m/z and 2500-4500 seconds.

Below we install the faahKO package and locate raw CDF files of interest:

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

    BiocManager::install("faahKO")

    library(faahKO)
    ## file paths
    input_spectraPaths  <- c(system.file('cdf/KO/ko15.CDF', package = "faahKO"),
                            system.file('cdf/KO/ko16.CDF', package = "faahKO"),
                            system.file('cdf/KO/ko18.CDF', package = "faahKO"))
    input_spectraPaths
    #> [1] "/home/biocbuild/bbs-3.10-bioc/R/library/faahKO/cdf/KO/ko15.CDF"
    #> [2] "/home/biocbuild/bbs-3.10-bioc/R/library/faahKO/cdf/KO/ko16.CDF"
    #> [3] "/home/biocbuild/bbs-3.10-bioc/R/library/faahKO/cdf/KO/ko18.CDF"

## 3.2 Expected regions of interest

Expected regions of interest (targeted features) are specified using the following information:

- `cpdID` (numeric)
- `cpdName` (character)
- `rtMin` (sec)
- `rtMax` (sec)
- `rt` (sec, optional / `NA`)
- `mzMin` (m/z)
- `mzMax` (m/z)
- `mz` (m/z, optional / `NA`)

Below we define 2 features of interest that are present in the faahKO dataset and can be employed in subsequent vignettes:

    # targetFeatTable
    input_targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(),
                            c("cpdID", "cpdName", "rtMin", "rt", "rtMax", "mzMin",
                            "mz", "mzMax"))), stringsAsFactors=FALSE)
    input_targetFeatTable[1,] <- c(1, "Cpd 1", 3310., 3344.888, 3390., 522.194778,
                                    522.2, 522.205222)
    input_targetFeatTable[2,] <- c(2, "Cpd 2", 3280., 3385.577, 3440., 496.195038,
                                    496.2, 496.204962)
    input_targetFeatTable[,c(1,3:8)] <- sapply(input_targetFeatTable[,c(1,3:8)],
                                                as.numeric)

cpdID | cpdName | rtMin | rt | rtMax | mzMin | mz | mzMax
--- | --- | --- | --- | --- | --- | --- | ---
1 | Cpd 1 | 3310 | 3344.888 | 3390 | 522.194778 | 522.2 | 522.205222
2 | Cpd 2 | 3280 | 3385.577 | 3440 | 496.195038 | 496.2 | 496.204962

# 4 See Also

- Real Time Annotation
- Parallel Annotation

# References

Saghatelian, A., S. A. Trauger, E. J. Want, E. G. Hawkins, G. Siuzdak, and B. F. Cravatt. 2004. “Assignment of Endogenous Substrates to Enzymes by Global Metabolite Profiling.” Biochemistry 43:14332–9. http://dx.doi.org/10.1021/bi0480335.