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https://github.com/yufree/mzrtsim

Raw data and peaks list simulation for GC/LC-MS based data
https://github.com/yufree/mzrtsim

metabolomics mzml simulation

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Raw data and peaks list simulation for GC/LC-MS based data

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```



# mzrtsim



[![R-CMD-check](https://github.com/yufree/mzrtsim/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/yufree/mzrtsim/actions/workflows/R-CMD-check.yaml)

[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)



The goal of mzrtsim is to make raw data and features table simulation for LC/GC-MS based  data



## Installation



You can install the development version from [GitHub](https://github.com/) with:



```{r eval=FALSE}

# install.packages("remotes")

remotes::install_github("yufree/mzrtsim")

```



## Raw Data simulation



You could use `simmzml` to generate one mzML file.



```{r eval=FALSE}

library(mzrtsim)

data("monams1")

simmzml(db=monams1, name = 'test')

```



You will find `test.mzML` and corresponding `test.csv` with m/z, retention time and compound name of the peaks. Here the `monams1` and `monahrms1` is from the MS1 data of MassBank of North America (MoNA) and could be downloaded from their [website](https://mona.fiehnlab.ucdavis.edu/downloads). You could also use `hmdbcms` to simulate EI source data extracted from [HMDB](https://hmdb.ca/downloads). Here we only use the MS1 full scan data for simulation.



### Data extraction



For HMDB, you need to download their "GC-MS Spectra Files (XML) - Experimental" data [here](https://hmdb.ca/downloads). Then you need the following R code to construct the database for this package.



```{r eval=FALSE}

xmlpath <- list.files('hmdb_experimental_cms_spectra/',full.names = T,recursive = T)

library(xml2)

getcms <- function(i){

  tree = xml2::read_xml(xmlpath[i])

  accession = xml_text(xml2::xml_find_all(tree,'/c-ms/database-id'))

  rti = as.numeric(xml_text(xml2::xml_find_all(tree,'/c-ms/retention-index')))

  prec = as.numeric(xml_text(xml2::xml_find_all(tree,'/c-ms/derivative-exact-mass')))

  formula = xml_text(xml2::xml_find_all(tree,'/c-ms/derivative-formula'))

  ins = xml_text(xml2::xml_find_all(tree,'/c-ms/notes'))

  ins2 = xml_text(xml2::xml_find_all(tree,'/c-ms/instrument-type'))

  mode = xml_text(xml2::xml_find_all(tree,'/c-ms/ionization-mode'))

  idms = xml_text(xml2::xml_find_all(tree,'/c-ms/id'))

  instr = xml_text(xml2::xml_find_all(tree,'instrument-type'))

  masscharge = as.numeric(xml_text(xml2::xml_find_all(tree,'/c-ms/c-ms-peaks/c-ms-peak/mass-charge')))

  intensity =  as.numeric(xml_text(xml2::xml_find_all(tree,'/c-ms/c-ms-peaks/c-ms-peak/intensity')))

  idx <- intensity!=0

  mz <- list(name=accession, idms=idms, ionmode=mode, prec = prec, formula = formula, np = length(masscharge[idx]), rti = rti, instr = ins2, msm = ins, spectra=cbind.data.frame(mz=masscharge[idx],ins=intensity[idx]))

  return(mz)

}



library(parallel)

hmdbcms <- mcMap(getcms,1:length(xmlpath), mc.cores = 4)

saveRDS(hmdbcms,'hmdbcms.RDS')

```



For MoNA, you can download the "LC-MS Spectra" data in format of "msp" from this [website](https://mona.fiehnlab.ucdavis.edu/downloads). Then we need to filter the MS1 data to generate database for LC-MS and subset data base for LC-HRMS.



```{r eval=FALSE}

# you need to install enviGCMS package by 'install.packages("enviGCMS")'.

msp <- enviGCMS::getMSP('MoNA-export-LC-MS_Spectra.msp')

idx <- sapply(msp, function(x) grepl('MS1',x$msm))

idx1 <- sapply(idx, function(x) length(x)>0)

monams1 <- msp[idx1]

idx2 <- sapply(monams1, function(x) grepl('MS1',x$msm))

monams1 <- monams1[idx2]



idx3 <- sapply(monams1, function(x) x$instr )

idx4 <- sapply(idx3, function(x) length(x)>0)

monams12 <- monams1[idx4]

saveRDS(monams12,'monams1.RDS')



idx5 <- sapply(monams12, function(x) x$instr )

monams13 <- monams12[grepl('FT|TOF',idx5)]

idx5 <- sapply(monams13, function(x) x$instr )



nn <- sapply(monams13,function(x) x$np)

median(as.numeric(nn))

mean(as.numeric(nn))

saveRDS(monams13,'monahrms1.RDS')

```



If you have your own database in "msp" format, you can generate the custom database by the following code:



```{r eval=FALSE}

msp <- enviGCMS::getMSP('MoNA-export-LC-MS_Spectra.msp')

saveRDS(msp,'custom.RDS')

```



To use the "*.RDS" database, you need to load them and refer their name for 'simmzml' function.



```{r eval=FALSE}

custom <- readRDS('custom.RDS')

simmzml(db=custom, name = 'test')

```



### Multiple files with experiment design



You could stimulate two groups of raw data with different peak widths for the same compounds. Retention time could follow a uniform distribution. 100 compounds could be selected randomly and base peaks' signal to noise ratio could be sample from 100 to 1000. Each group contain 10 samples and 30% compounds are changed between case and control groups.



```{r eval=FALSE}

dir.create('case')

dir.create('control')

# set different peak width for 100 compounds

pw1 <- c(rep(5,30),rep(10,40),rep(15,30))

pw2 <- c(rep(5,20),rep(10,30),rep(15,50))

# set retention time for 100 compounds. When the peak width is set as a single value, the simulated base peak width will be from a poisson distribution with this value as lambda. 

rt <- seq(10,590,length.out=100)

set.seed(1)

# select compounds from database

compound <- sample(c(1:4000),100)

set.seed(2)

# select signal to noise ration

sn <- sample(c(100:10000),100)

for(i in c(1:10)){

  simmzml(name=paste0('case/case',i),db=monahrms1,pwidth = pw1,compound=compound,rtime = rt, sn=sn)

}



for(i in c(1:10)){

  simmzml(name=paste0('control/control',i),db=monahrms1,pwidth = pw2,compound=compound,rtime = rt, sn=sn)

}

```



Then you could find 10 mzML files in case sub folder and another 10 mzML files in control sub folder, as well as corresponding csv files with m/z, retention time and compound name of the peaks. 



### Chromatography peaks



You could also use `simmzml` to stimulate tailing/leading peaks by defining the tailing factor of the peaks. When the tailing factor is lower than 1, the peaks are leading peaks. When the tailing factor is larger than 1, the peaks are tailing peaks. 



```{r eval=FALSE}

# leading peaks

simmzml(name='test',db=monahrms1,pwidth = 10,compound=1,rtime = 100, sn=10,tailingfactor = 0.8)

# tailing peaks

simmzml(name='test',db=monahrms1,pwidth = 10,compound=1,rtime = 100, sn=10,tailingfactor = 1.5)

```



### matrix stimulation



You could also input a m/z vector as matrix masses. Those masses will generate background baseline signals. By default, the mass vector is from matrix samples previous [published](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-022-00586-8). 



```{r eval=FALSE}

data(mzm)

simmzml(name='test',db=monahrms1,pwidth = 10,compound=1,rtime = 100, sn=10,matrixmz = mzm,matrix = TRUE)

```



## Peaks list simulation



You could also use `mzrtsim` to make simulation of peak list.



Here we make a simulation of 100 compounds from selected database with two conditions and three batches. 5 percentage of the peaks were influenced by conditions and 10 percentage of the peaks were influenced by batch effects. Three different type could be simulated: monotonic, random and block. You could also bind batch type, for example, 'mb' means the simulation would contain both monotonic and block batch effects. 'db' means the spectra database to be used for simulation as metioned in raw data simulation section.



```{r mzrtsim}

library(mzrtsim)

data("monams1")

simdata <- mzrtsim(ncomp = 100, ncond = 2, ncpeaks = 0.05,

  nbatch = 3, nbpeaks = 0.1, npercond = 10, nperbatch = c(8, 5, 7), seed = 42, batchtype = 'mb', db=monams1)

```



You could save the simulated data into multiple csv files by `simdata` function. `simraw.csv` could be used for metaboanalyst. `simraw2.csv` show the raw peaks list. `simcon.csv` show peaks influenced by conditions only.`simbatchmatrix.csv` show peaks influnced by batch effects only. `simbat.csv` show peaks influenced by batch effects and conditions. `simcomp.csv` show independent peaks influenced by conditions and batch effects. `simcompchange.csv` show the conditions changes of each groups. `simblockbatchange.csv` show the block batch changes of each groups. `simmonobatchange.csv` show the monotonic batch changes of each group.



```{r simdata, eval=F}

simdata(sim,name = "sim")

```