The stacked bar charts draw the logarithmically transformed fragment ion intensities for the different ion fragmentations and types, respectively. If marked in the checkbox (left), the unfragmented precursor peaks are removed.
Download stacked bar chart as pdf Download stacked bar chart as png Download selected data as csv
Each panel plots the top three most abundant total ion count (TIC) versus the master intensity of one raw file. The color linking indicates for the raw file grouping.
The boxplots draw the absolute error (in Dalton) distribution.
Some statistics of the overall data and the applied filter setting is shown below:
In the frequency table, the frequency of each compound and fragmentation type is determined under consideration of the ionization mode (positive or negative) and the filter settings (ppm error and absolute error).
The histograms display the distribution of the ppm and the absolute error over the entire data set and the selected compound. The red curve depicts the maximum-likelihood fitting assuming an underlying normal distribution.
The table lists the fragment ion for each fragmentation type.
Each panel displays the fragment ion spectrum for each fragmentation type. The color indicates the file origin.
lists the entire data set applying the filter setting.
This page provides access to the computed scores. The scores are defined as follow:

score1 experimental fragments matched ⁄ theoretically possible.

score2 experimental fragments matched ⁄ all fragments in spectrum.

score3 experimental matched fragment intensities ⁄ master.intensity.

Download Scores
In the frequency table, the frequency of each compound and fragmentation type is determined under consideration of the ionization mode (positive or negative) and the filter settings (ppm error and absolute error). Download the frequency table
By using the method `metfRag::frag.generateFragments` derived fragment ions are listed below. Those ions represent the entire search space for the peak assignment.
Help uvpd - version 0.0.16

Contact us

by E-mail [Andrea Brunner](mailto: Andrea.Brunner@kwrwater.nl?SUBJECT=-> help uvpd shiny) or [Christian Panse](mailto:cp@fgcz.ethz.ch?SUBJECT=-> help uvpd shiny)

Bug Reports

https://github.com/cpanse/uvpd/issues

Run the application

Install the R package

pkgs <- c('shiny', 'ggplot2')
pkgs <- pkgs[(!pkgs %in% unique(installed.packages()[,'Package']))]
if(length(pkgs) > 0){install.packages(pkgs)}

install.packages('http://fgcz-ms.uzh.ch/~cpanse/UVPD/uvpd_0.0.16.tar.gz',repos=NULL)

run the shiny application from your computer

shiny::runApp(file.path(system.file(package = 'uvpd'), 'shiny/stackedbarchart'))

code snipets

stacked bar chart - download the selected data and run

DF <- read.table("Triadimenol.csv", sep=',', header=TRUE)

library(ggplot2)

gg_color_hue <- function(n) {
    hues = seq(15, 375, length = n + 1)
    hcl(h = hues, l = 65, c = 100)[1:n]
}

cm <- gg_color_hue(n)
if (getFormulaPC() %in% as.character(DF$formula)){
    cm <- c(gg_color_hue(n-1),'grey')
}

gp <- ggplot(data = DF,
                 aes(x = factor(fragmode, levels = sort(unique(DF$fragmode))),
                     y = log(intensity, 10),
                     fill=reorder(formula, mZ))) +
      geom_bar(stat="identity", position = position_stack(reverse = FALSE)) +
      scale_x_discrete(drop=FALSE) +
      theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
      guides(fill=guide_legend(title="formula")) +
      xlab("") +
      scale_fill_manual(values = cm) +
      facet_wrap(~ compound * mode, scales="free", drop=FALSE)

md5 checksums of input files

https://doi.org/10.5281/zenodo.4001653

421dcd9bfd60d5ec94f2eaf30a5b28fe  CastellonStds_pos_HCD_20_35_60.raw
c7b3769271afa562b31cc6c4a65e9049  CastellonStds_pos_UVPD_100_150.raw
e8342d31941964ae86c83a0b09922ea8  CastellonStds_pos_UVPD_200_250.raw
9f8d081c4cf6b59bbd265cf3de6c821f  CastellonStds_pos_UVPD_25_800.raw
747a1e3c14effc6f4b4b8c22e982bdda  CastellonStds_pos_UVPD_400_500.raw
fff6852bf964c67fcb181ce6d7835691  CastellonStds_pos_UVPD_50_300.raw
4fd80c97b52bf548875a9aeffae66083  DBP_neg_HCD_20_35_60.raw
c22724a557de3813626b419a2eb7f1be  DBP_neg_UVPD_100_150.raw
b8f913378eb46b845a18f7236cbe35e1  DBP_neg_UVPD_200_250.raw
bcfad7bdd36f5cf22becc8b97251e51c  DBP_neg_UVPD_25_800.raw
ae9edc64893afc2ef6aefde5a1f60d01  DBP_neg_UVPD_400_500.raw
5de71cbbaf799d423ad34e15924c2626  DBP_neg_UVPD_50_300.raw
6736bfac31233a13ee151b8cb8579af6  KWRstds_HCD_20_35_60_met1.raw
ef73b5b9fd0bbe5a2b11df3023e0d6b6  KWRstds_UVPD_100_150_met1.raw
06c3cf282a41fbd6f1ea5cb24e044f48  KWRstds_UVPD_100_150_met2.raw
8e28ce2fbc6594df647fd48e31ba026c  KWRstds_UVPD_200_250_met1.raw
b9dd45a4496698cdfa4d93cede6605fa  KWRstds_UVPD_200_250_met2.raw
83a04a7e6216ca3f98e50b34cb1e95c1  KWRstds_UVPD_25_800_met1.raw
6fe92ed1b8243d302108148d88fa591b  KWRstds_UVPD_25_800_met2.raw
3248cbb1bb1e444cece64f5c8bf35a17  KWRstds_UVPD_400_500_met1.raw
66b589fd55d80273cd2527079283ab02  KWRstds_UVPD_400_500_met2.raw
00d3caedfd61cc05b1df53549a2ef3f0  KWRstds_UVPD_50_300_met1.raw
16fc1617015cb7d57970dccb569222b1  KWRstds_UVPD_50_300_met2.raw
22cbc525114ac7c121f28fac0f86e15d  stds_pos_neg_MS_highconc_HCD_mz100-800.raw
56849e8763d0e1c250b46c4a4434235a  stds_pos_neg_MS_highconc_UVPD_100_150_mz50.raw
dcf62100663404a354eb546127f7eaaa  stds_pos_neg_MS_highconc_UVPD_200_250_mz50.raw
c469eee791cbf9919a2f87d875299ca5  stds_pos_neg_MS_highconc_UVPD_25_800_mz50.raw
81ce2b575dc4bf9b60c0701ba083463a  stds_pos_neg_MS_highconc_UVPD_400_500_mz50.raw
94e4852ce183317288d2dc537ad57655  stds_pos_neg_MS_highconc_UVPD_50_300_mz50.raw

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Session Information

                below is the output of the method call sessionInfo()