Qualitative and quantitative analysis of contaminants are the core of the Environmental Science. GC/LC-MS might be one of the most popular instruments for such analytical methonds. Previous works such as
xcms were devoloped for GC-MS data. However, such packages have limited functions for environmental analysis. In this package. I added functions for various GC/LC-MS data analysis purposes used in environmental analysis. Such feature could not only reveal certain problems, but also help the user find out the unknown patterns in the dataset of GC/LC-MS.
GC/LC is used for separation and MS is used for detection in a GC/LC-MS system. The collected data are intensities of certain mass at different retention time. When we perform analysis on certain column in full scan mode, the counts of different mass were collected in each scan. The drwell time for each scan might only last for 500ms or less. Then the next scan begins with a different retention time. Here we could use a matrix to stand for those data. Each column stands for each mass and row stands for the retention time of that scan. Such matrix could be treated as time series data. In this package, we treat such data as
For high-resolution MS, building such matrix is tricky. We might need to bin the RAW data to make alignment for different scans into a matrix. Such works could be done by
When you perform a selected ions monitor(SIM) mode analysis, only few mass data were collected and each mass would have counts and retention time as a time seris data. In this package, we treat such data as
You could use
getmd to import the mass spectrum data as supported by
xcms and get the profile of GC-MS data matrix.
mzstep is the bin step for mass:
data <- enviGCMS:::getmd('data/data1.CDF', mzstep = 0.1)
You could also subset the data by the mass(m/z 100-1000) or retention time range(40-100s) in
data <- enviGCMS:::getmd(data,mzrange=c(100,1000),rtrange=c(40,100))
You could also combined the mass full-scan data with the same range of retention time by
data <- cbmd(data1,data2,data3)
You could plot the Total Ion Chromatogram(TIC) for certain RT and mass range.
You could also plot the mass spectrum for certain RT range. You could use the returned MSP files for NIST search:
The Extracted Ion Chromatogram(EIC) is also support by
enviGCMS and the returned data could be analysised for molecular isotopes:
You could use
plotmz to show the heatmap or scatter plot for LC/GC-MS data, which is very useful for exploratory data analysis.
You could change the retention time into the temprature if it is a constant speed of temperature rising process. But you need show the temprature range.
plott(data,temp = c(100,320))
enviGCMS supplied many functions for decreasing the noise during the analysis process.
findline could be used for find line of the boundary regression model for noise.
comparems could be used to make a point-to-point data subtraction of two full-scan mass spectrum data.
plotgroup could be used convert the data matrix into a 0-1 heatmap according to threshold.
plotsub could be used to show the self backgroud subtraction of full-scan data.
plotsms shows the RSD of the intensity of full scan data.
plothist could be used to find the data distribution of the histgram of the intensities of full scan data.
Some functions could be used to caculate the molecular isotope ratio. EIC data could be import into
GetIntergration and return the infomation of found peaks.
Getisotoplogues could be used to caculate the molecular isotope ratio of certain molecular. Some shortcut function such as
qbatch could be used to caculate molecular isotope ratio for mutiple and single molecular in EIC data.
enviGCMS supply function to perform Quantitative analysis for short-chain chlorinated paraffins(SCCPs) with Q-tof data. Use
getsccp to make Quantitative analysis for SCCPs.
If you want a graphical user interface for SCCPs analysis, a shiny application is developed in this package. You could use
runsccp() to power on the application in a browser.
In environmetnal non-target analysis, when multiple samples are collected, problem will raise from the heterogeneity among samples. For example, retention time would shift due to the column. In those cases,
xcms package could be used to get a peaks list across samples within certain retention time and m/z.
enviGCMS package has some wrapped function to get the peaks list. Besides, some specific functions such as group comparision, batch correction and visulization are also included.
getdata could be used to get the
xcmsSet object in one step with optimized methods
getdata2 could be used to get the
XCMSnExp object in one step with optimized methods
getmzrt could get a list as
mzrt object with peaks list, mz, retention time and class of samples from
XCMSnExp object. You could also save related
xcmsEIC object for further analysis. It also support to output the file for metaboanalyst, xMSannotator, Mummichog pathway analysis and paired mass distance(PMD) analysis.
getmzrtcsv could read in the csv files and return a list for peaks list as
getimputation could impute NA in the peaks list.
getfilter could filter the data based on row and colomn index.
getdoe could filter the data based on rsd and intensity and generate group mean, standard deviation, and group rsd.
getpower could compute the power for known peaks list or the sample numbers for each peak
getoverlappeak could get the overlap peaks by mass and retention time range for two different list objects
plotmr could plot the scatter plot for peaks list with threshold
plotmrc could plot the differences as scatter plot for peaks list with threshold between two group of data
plotrsd could plot the rsd influnces of data in different groups
gifmr could plot scatter plot for peaks list and output gif file for mutiple groups
plotpca could plot pca score plot
plothm could plot heapmap
plotden could plot density of multiple samples
plotrla could plot Relative Log Abundance (RLA) plots
plotridges could plot Relative Log Abundance Ridge (RLAR) plots
enviGCMS could be used to explore single data or peaks list from GC/LC-MS and extract certain patterns in the data with various purposes.