Load Samples

We are loading files that were acquired with data dependent fragmentation events. There are two groups of files, water sample and yeast samples spiked in with known standards from SIGMA metabolite library (see methods). Click Open, and Select all positive mode mzXML Files.

These files may be retrieved from the Metabolights online database, accession number MTBLS3097

Recolor Samples and Add Metadata

User can change color, order, grouping of samples in the Samples Widget, for example, here we are coloring water samples in blue, and yeast samples in orange. Under “Set”, adjust the set for each sample to either “yeast” or “water” as appropriate. This can be done by double-clicking on the “A” next to each sample, and typing in “yeast” or “water” in its place.

Save Project Data into mzrollDB File

Now we can save the project information into project file example.mzrollDB . This file is actually an SQLite database, and will store information about samples, peaks, identified compounds, and search settings.

Load Fragmentation Library

MAVEN9 support import of NIST formatted .msp fragmentation libraries. To import new library select “Import New Library”. This will cache data. If library already already has been previously imported, simply select “Load”.

We will assume that the library has not been previously loaded. Launch the library dialog by clicking the button in the main window named “Library”. A dialog box will appear. Once the dialog has appeared, select “Import New Library”, and navigate to the file “MS-Method-OO2-A_ZIC-pHILIC_Polar_Positive-unnormalized-matched_single_energy.msp”, which is available in the supplementary files. After the file has imported, it will appear in the list of available libraries.

View Library Contents

Loading a library populates “Compound Widget” with detailed information on the compounds contained in a spectral library. This includes the compound name, formula, adduct form, retention time, precursor mass, SMILES string, and other metadata.

The Compounds widget may be found my clicking on the “Compounds” tab, or the “Compound Library” icon on the toolbar on the far right of the main window.

Load Adducts

When this library is loaded, all adducts of all compounds featured in the library are automatically added to the list of available adducts. To adjust the set of available adducts, you can click on the “Adducts” button.

In the following dialog that appears, clicking on the “enabled” header will show all adducts that are currently active in MAVEN. These adducts will appear in the main window, and will be used for library searches.

Examine a Single Compound

For example, lets examine EIC and fragmentation specta for Glutathione.

Type “Glutathione” in the Compound Name Filter and hit enter. The filtered list will show 3 compounds, the bottom of which is Glutathione. Select this Glutathione from the list.

Make sure that the “Fragmentation Widget” is showing. This can be shown by clicking on the “MS2 Spectra” button on the toolbar on the far right. Next, in the “Widgets” main program drop-down menu, select “MS2 Scans List”.

Now, zoom in to this large peak by dragging a rectangle over the peak, starting in the top left corner and moving to the right. Clicking on the top circle will select all MS2 scans currently associated with all samples with this peak group, and show a comparison of a consensus spectrum of this peak group to the library spectrum for Glutathione.

Clicking on individual rows in the MS2 List while holding down CTRL or SHIFT adjusts the MS2 scans used to build the consensus spectrum. Clicking on only one row shows that individual scan. The process by which consensus spectra are generated may be controlled by navigating to “Options” and choosing the “Spectral Agglomeration” tab.

Peak Detection

Now we going to search database for compounds that match MS2 spectra. Clicking on “Peaks” will brings up search parameter dialog. We will use default settings, and limit our seach to peaks that have MS2 events by keeping the “Must have MS2” check box checked. We will also click on “Match Retention Time”, to require that retention time of compound is within 2 min of annotated retention time of compound.

We will also require that minimum hypertgeometric score match is >10 with at least 3 fragmentation matches. Click “Find Peaks” to initiate the search.

Upon completion, search results will appear in the “Detected Features” widget. Detected features can be sorted by retention time, precursor mass, etc. For example sorting by “MS2 Score” column lists best matches first.

Click Save or Press “CTRL + S” to save the results into the mzrollDB file.

Peak Group Tagging

To show the power of tagging, we will now tag some the peak groups, and filter to show only these groups.

Right click on the first peak group and select “Tag Group library”. click on the second peak group in the list, hold the shift key, and click the fifth peak group in the list. right-click and select “Tag group revisit”. Click the sixth group in the list, and hold down CTRL and type the “g” key. Scroll down to the very end of the table, select the last row, and click the red “X” button at the top of the “Detected Features” table. The tooltip for this button will read “Mark Peak as Bad”. Find the Filter buttonin the “Detected Features” table directly to the left of the Filter bar. This button will have four smalelr shape icons in it. Click this button to launch a filter dialog. Once the dialog appears, uncheck the top row, which will hide all peak groups that have not been tagged. Click “Apply Filter”.

After carrying out these steps, the peak group table should now look like this:

Tagged Peak Groups

Load Data from the Project File (mzrollDB)

There are three main tables in the database.

1. samples - information about samples, sample attributes, sample groups,

2. peakgroups - information about detected features, matched compound and adduct infomation

3. peaks - individual peak data, one per sample with information about retention time, peak intensity, width etc..

To execute these scripts, you will need to set your working directory to the folder containing this script. You will also need to install various dependencies (shown below)

install.packages("tidyverse")
install.packages("reshape2")
install.packages("ggplot2")
install.packages("uwot")
install.packages("dbscan")

library(tidyverse)
library(reshape2)
library(ggplot2)

con <- DBI::dbConnect(RSQLite::SQLite(), dbname = "./example.mzrollDB")

##sample information
samples <- tbl(con, "samples")
SAMPLES <- samples %>% select(sampleId,samplename=name) %>% collect()
SAMPLES$sampleType <- "sample"
SAMPLES$sampleType[ grepl("blank",ignore.case = T,SAMPLES$samplename) ] = "blank"

SAMPLES$niceNames=gsub("Pos_standards_yeast_background_...",  "", SAMPLES$samplename)
SAMPLES$niceNames=gsub(".mzXML",  "", SAMPLES$niceNames)

##groups
SAMPLES$sampleSet= "";
SAMPLES$sampleSet[ grepl("yeast",ignore.case = T,SAMPLES$samplename) ] = "yeast_spikein"
SAMPLES$sampleSet[ grepl("water",ignore.case = T,SAMPLES$samplename) ] = "water_spikein"
SAMPLES$sampleSet[ grepl("blank",ignore.case = T,SAMPLES$samplename) ] = "blank"

#peakgroup information
peakgroups <- tbl(con, "peakgroups") 
PEAKGROUPS = peakgroups %>% collect()

##peak information
PEAKS=tbl(con, "peaks") %>% left_join(peakgroups, by="groupId") %>%  left_join(samples, by="sampleId") %>% collect()

Group Statistics

groupStats=PEAKS %>% left_join(SAMPLES) %>% group_by(groupId,sampleSet) %>%
          summarise(log2intensity=log2(mean(peakAreaTop)),
                    groupMz = median(peakMz),
                    groupRt = median(rt),
                    hyperGeomScore=median(ms2Score),
                    npeaks=n()) %>%
          mutate(goodmatch=hyperGeomScore>50)

## Joining, by = "sampleId"
## `summarise()` has grouped output by 'groupId'. You can override using the
## `.groups` argument.

groupStats=groupStats %>% left_join(PEAKGROUPS)

## Joining, by = "groupId"

ggplot(groupStats,aes(y=log2intensity,x=groupId,size=log2intensity, color=sampleSet)) + geom_point(aes(alpha=goodmatch),show.legend = T) + 
  theme_bw(base_size = 14) +  scale_color_brewer(palette="Set1")

## Warning: Using alpha for a discrete variable is not advised.

Correlation between Samples

GOODSAMPLES=SAMPLES %>% filter(sampleType == "sample");

XX=PEAKS %>% filter(sampleId %in% GOODSAMPLES$sampleId) %>% left_join(SAMPLES) %>% 
                     reshape2::dcast(.,groupId ~ niceNames,
                                     value.var = "peakAreaTop", 
                                     fun.aggregate = median,
                                     fill = 100)

## Joining, by = "sampleId"

for(col in 2:ncol(XX)) { XX[,col] = log2(XX[,col]); }
XXcor=cor(XX[,c(-1)],use = "complete.obs")
pheatmap::pheatmap(XXcor)

#referenceSample="water_01"
#reference=XX[,referenceSample];
#for(col in 2:ncol(XX)) { XX[,col] = log2(XX[,col]/(reference+10)) }
## plot heatmap  

UMAP Clustering of Metabolites

library("dbscan")
library("uwot")

## Loading required package: Matrix

## 
## Attaching package: 'Matrix'

## The following objects are masked from 'package:tidyr':
## 
##     expand, pack, unpack

#project compounds
Z.umap = uwot::umap(XX[,-1],n_neighbors = 2,min_dist = 0.03);
umap_layout = data.frame( groupId=XX[1], umap1=Z.umap[,1], umap2= Z.umap[,2]);

#cluster compounds
dbclust=hdbscan(Z.umap,minPts = 3)
umap_layout$umap_cluster= as.factor(dbclust$cluster)

p0=ggplot(umap_layout, aes(umap1,umap2,color=umap_cluster)) + 
    geom_point(size=2) + theme_bw() + theme(legend.position = "none") +
    ggtitle("UMAP Projection")
print(p0)

complete_groups=PEAKS %>% left_join(SAMPLES) %>% 
  group_by(groupId,sampleSet) %>%
  summarise(nsamples=n(),
            log2intensity=mean(log2(peakAreaTop)),
            variance=sd(log2(peakAreaTop))) %>% 
  reshape2::dcast(.,groupId ~ sampleSet,
                                     value.var = "log2intensity", 
                                     fun.aggregate = max,
                                    fill=)

## Joining, by = "sampleId"
## `summarise()` has grouped output by 'groupId'. You can override using the
## `.groups` argument.

## Warning in .fun(.value[0], ...): no non-missing arguments to max; returning -Inf

ggplot(complete_groups,aes(y=water_spikein,x=blank)) + geom_point() +
  theme_bw(base_size = 14) +  scale_color_brewer(palette="Set1")