jorainer/xcms3_featurebasedmn

Introduction

The Feature-Based Molecular Networking (FBMN) workflow is a computational method that bridges popular mass spectrometry data processing tools for LC-MS/MS and molecular networking analysis on GNPS. The tools supported are: MZmine2, OpenMS, MS-DIAL, MetaboScape, XCMS, and the mzTab-M format.

The main documentation for Feature-Based Molecular Networking with XCMS can be accessed here. See our preprint Nothias, L.F. et al bioRxiv 812404 (2019).

This repository contains example scripts in Python and R showing how XCMS version >= 3 (XCMS3) can be used for the FBMN workflow in GNPS using a subset of samples of the American Gut Project (MSV000082678) and soil bacteria (MSV000079204).

Installation of the XCMS-GNPS workflow for FBMN

Install the latest version of XCMS3 (version >= 3.4) from Bioconductor in R with:

install("BiocManager")
BiocManager::install("xcms")

For more information, also refer to the xcms Bioconductor package.

For utility functions specific to this workflow refer to the Github repository: https://github.com/jorainer/xcms-gnps-tools.

Citations and development

This work builds on the efforts of our many colleagues, please cite their work:

Nothias, L.F. et al Feature-based Molecular Networking in the GNPS Analysis Environment bioRxiv 812404 (2019).

https://github.com/sneumann/xcms

Tautenhahn R, Boettcher C, Neumann S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics, 9:504 (2008).

Smith, C.A., Want, E.J., O'Maille, G., Abagyan,R., Siuzdak, G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching and identification.. Analytical Chemistry, 78, 779–787 (2006).

Running Feature Based Molecular Networking on GNPS

The main documentation for Feature-Based Molecular Networking with GNPS can be accessed here.

Contributions

The XCMS-GNPS integration was developed by Johannes Rainer and Michael Witting, in coordination with Louis-Félix Nothias and Daniel Petras. This tutorial was prepared by Madeleine Ernst and Ricardo da Silva.

xcms3_featurebasedmn's Issues

CAMERA and residual issues with xcmsSet in here

For a while I think I was getting away with calling xcmsSets, but I think there has been a relatively recent shift to not allowing xcmsSet at all. I had XCMS3_Proprocessing.Rmd working last summer (though my notes are not good enough that I saved my sessionInfo, oops. Now the processing at the step where CAMERA is used to generate information needed for GNPS has a step (line 367) that seems to require an xcmsSet, the line is this:

xsaC <- groupCorr(xsaF, cor_eic_th = 0.6, pval = 0.05, graphMethod = "hcs", calcCiS = TRUE, calcCaS = TRUE, calcIso = FALSE)

The error I am getting is
Error in validObject(.Object) : invalid class “xcmsFileSource” object: superclass "characterORconnection" not defined in the environment of the object's class

I honestly do not know how to fix this, nor do I have a sense of the magnitude of the change required. Any thoughts?

Thanks for any help.
Krista

> sessionInfo()
R version 4.0.5 (2021-03-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] RColorBrewer_1.1-2

loaded via a namespace (and not attached):
 [1] BiocManager_1.30.12 compiler_4.0.5      htmltools_0.5.1.1   tools_4.0.5         tinytex_0.31       
 [6] rmarkdown_2.7       knitr_1.31          digest_0.6.27       xfun_0.20           rlang_0.4.10       
[11] rJava_0.9-13        evaluate_0.14      

Create a function to convert CAMERA output to edge list file

Define a function getEdgelist that takes the getPeaklist output and returns a data.frame with columns "ID1", "ID2", "EdgeType", "Score" and "Annotation" (and optional additional columns such as "PCGroup").

Add additional required information to the feature table

Add the following columns to the feature table:

• "annotation_network_number": the pcgroup from CAMERA.
• "best ion": Best ion identity choice (sometimes there are multiple possibilities – the best Ion Identity Network wins) - not clear where and how to define that.
• "correlation group ID": We use the correlation to group aligned feature list rows – in our workflow, this does not mean that all features in a group are actually correlated.
• "Auto MS2 verify": Verification of multimers and in-source fragments
• "Identified by n=": Ion Identity Network size
• "Partners": List of all other IDs in this network
• "Neutral M mass": Neutral mass of the molecule which is described by this network. (average over all m/z and ion identities).