Date: 2020-10-11

# Getting Started with the peakPantheR package

#### 2020-10-11

#### Package

peakPantheR 1.27.0

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")

## 2.1 Getting Started

To get started `peakPantheR`’s graphical user interface implements all the functions to detect and integrate multiple compounds in multiple files in parallel and store results in a single object. It can be employed to integrate a large number of expected features across a dataset:

    library(peakPantheR)

    peakPantheR_start_GUI(browser = TRUE)
    #  To exit press ESC in the command line

The GUI is to be preferred to understand the methodology, select the best parameters on a subset of the samples before running the command line, or to visually explore results.

If a very high number of samples and features is to be processed, `peakpantheR`’s command line functions are more efficient, as they can be integrated in scripts and the reporting automated.

# 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-3.24-bioc/R/site-library/faahKO/cdf/KO/ko15.CDF"
    #> [2] "/home/biocbuild/bbs-3.24-bioc/R/site-library/faahKO/cdf/KO/ko16.CDF"
    #> [3] "/home/biocbuild/bbs-3.24-bioc/R/site-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` (character)
- `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("ID-1", "Cpd 1", 3310., 3344.888, 3390., 522.194778,
                                    522.2, 522.205222)
    input_targetFeatTable[2,] <- c("ID-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)

    #> Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

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

# 4 Preparing input for the graphical user interface

While the command line functions accept Data.Frame and vectors as input, the graphical user interface (GUI) will read the same information from a set of `.csv` files, or an already set-up `peakPantheRAnnotation` object in `.RData` format.

We can now generate GUI input files for the faahKO example dataset presented previously:

## 4.1 peakPantheRAnnotation .RData

A `peakPantheRAnnotation` (previously annotated or not) can be passed as input in a `.RData` file. The `peakPantheRAnnotation` object must be named annotationObject:

    library(faahKO)

    # Define the file paths (3 samples)
    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"))

    # Define the targeted features (2 features)
    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("ID-1", "Cpd 1", 3310., 3344.888, 3390.,
                                    522.194778, 522.2, 522.205222)
    input_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 3280., 3385.577, 3440.,
                                    496.195038, 496.2, 496.204962)
    input_targetFeatTable[,3:8] <- sapply(input_targetFeatTable[,3:8], as.numeric)

    # Define some random compound and spectra metadata
    # cpdMetadata
    input_cpdMetadata     <- data.frame(matrix(data=c('a','b',1,2), nrow=2, ncol=2,
                                dimnames=list(c(), c('testcol1','testcol2')),
                                byrow=FALSE), stringsAsFactors=FALSE)
    # spectraMetadata
    input_spectraMetadata <- data.frame(matrix(data=c('c','d','e',3,4,5), nrow=3,
                                ncol=2,
                                dimnames=list(c(),c('testcol1','testcol2')),
                                byrow=FALSE), stringsAsFactors=FALSE)

    # Initialise a simple peakPantheRAnnotation object
    # [3 files, 2 features, no uROI, no FIR]
    initAnnotation <- peakPantheRAnnotation(spectraPaths=input_spectraPaths,
                                            targetFeatTable=input_targetFeatTable,
                                            cpdMetadata=input_cpdMetadata,
                                            spectraMetadata=input_spectraMetadata)

    # Rename and save the annotation to disk
    annotationObject <- initAnnotation
    save(annotationObject,
            file = './example_annotation_ppR_UI.RData',
            compress=TRUE)

## 4.2 CSV file input

Another input option for the GUI input consists of a set of `.csv` files.

### 4.2.1 Targeted features

Targeted features are defined in a `.csv` with as rows each feature to target (the first row must be the column name), and as columns the fit parameters to use. At minimum the following parameters must be defined:

`cpdID`, `cpdName`, `rtMin`, `rt`, `rtMax`, `mzMin`, `mz`, `mzMax`

If `uROI` and `FIR` are to be set, the following columns must be provided:

`cpdID`, `cpdName`, `ROI_rt`, `ROI_mz`, `ROI_rtMin`, `ROI_rtMax`, `ROI_mzMin`, `ROI_mzMax`, `uROI_rtMin`, `uROI_rtMax`, `uROI_mzMin`, `uROI_mzMax`, `uROI_rt`, `uROI_mz`, `FIR_rtMin`, `FIR_rtMax`, `FIR_mzMin`, `FIR_mzMax`

    # Define targeted features without uROI and FIR (2 features)
    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("ID-1", "Cpd 1", 3310., 3344.888, 3390.,
                                    522.194778, 522.2, 522.205222)
    input_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 3280., 3385.577, 3440.,
                                    496.195038, 496.2, 496.204962)
    input_targetFeatTable[,3:8] <- sapply(input_targetFeatTable[,3:8], as.numeric)

    # save to disk
    write.csv(input_targetFeatTable,
                file = './1-fitParams_example_UI.csv',
                row.names = FALSE)

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

### 4.2.2 Files to process and spectra metadata (optional)

It is possible to select the files on disk directly through the GUI, or to select a `.csv` file containing each file path as well as spectra metadata. Each row correspond to a different spectra (the first row must define the column names) while columns correspond to the path on disk and metadata. At minimum a column `filepath` must be present, with subsequent columns defining metadata properties.

    # Define the spectra paths and metada
    input_spectraMeta <- data.frame(matrix(vector(), 3, 3,
                            dimnames=list(c(),c("filepath","testcol1","testcol2"))),
                            stringsAsFactors=FALSE)
    input_spectraMeta[1,] <- c(system.file('cdf/KO/ko15.CDF', package = "faahKO"),
                                "c", 3)
    input_spectraMeta[2,] <- c(system.file('cdf/KO/ko16.CDF', package = "faahKO"),
                                "d", 4)
    input_spectraMeta[3,] <- c(system.file('cdf/KO/ko18.CDF', package = "faahKO"),
                                "e", 5)

    # save to disk
    write.csv(input_spectraMeta,
                file = './2-spectraMetaWPath_example_UI.csv',
                row.names = FALSE)

Table continues below
filepath | testcol1
--- | ---
/home/biocbuild/bbs-3.24-bioc/R/site-library/faahKO/cdf/KO/ko15.CDF | c
/home/biocbuild/bbs-3.24-bioc/R/site-library/faahKO/cdf/KO/ko16.CDF | d
/home/biocbuild/bbs-3.24-bioc/R/site-library/faahKO/cdf/KO/ko18.CDF | e

testcol2
3
4
5

### 4.2.3 Feature meatadata (optional)

It is possible to define some feature metadata, with targeted features as rows (same order as the fitting parameters, first row defining the column names), and as columns the metadata.

    # Define the feature metada
    input_featMeta <- data.frame(matrix(vector(), 2, 2,
                        dimnames=list(c(),c("testcol1","testcol2"))),
                        stringsAsFactors=FALSE)
    input_featMeta[1,] <- c("a", 1)
    input_featMeta[2,] <- c("b", 2)

    # save to disk
    write.csv(input_featMeta,
                file = './3-featMeta_example_UI.csv',
                row.names = FALSE)

testcol1 | testcol2
--- | ---
a | 1
b | 2

# 5 See Also

- Real Time Annotation
- Parallel Annotation
- Graphical user interface use

# 6 Session Information

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# 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.