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MS-DIAL / alignment table with areas per file?
Last post by wenzig -
Hello everyone,
I tested MS DIAL for the first time and I have a question concerning the alignment table:
The alignment table I get only contains bar charts (with mean abundance +/-SD?)  but not an information on the abundance of all detected features in every sample included in the data processing.
This is what I would actually need for MVDA. Is it possible to get this type of table in MS DIAL?
MS-DIAL / Re: Problems with the alignment process
Last post by Sukis -
Hi viniciusgw,
Sorry that I don't know what is your problem. Maybe you can try even a lower cut-off, for example, 100, just to see if the sample indeed does not contain peaks?
For the use of MS-DIAL and MS-FINDER in windows 10, I don't have to set anything with the English version Window 10 (version 2004).

MS-DIAL / Problems with the alignment process
Last post by viniciusgw -
Hello, colleagues,

I´ve been having some problems with the alignment process in MS-DIAL. I´m only using one sample and it's blank file and some errors are occurring. I´m using minimum amplitude cut off as 1000, but even lowering its value the same error still occurs. I checked the MZml files of the sample and blank and the files aren´t destroyed, as they can be analyzed in other software. Does anybody else have these troubles? I also would like to know if there is a need to set some configuration in the windows 10 to MS-DIAL and MS-FINDER work well.


MS-DIAL / Re: Alignment spot viewer
Last post by matos2102 -

In the alignment navigator (left-bottom of MS-DIAL), please double click the result file name of peak alignment.


Good night, I'm a new user of the program, I'm loving the friendly interface!
I would like to know if it is possible to make the PCA without this "QC" I have only 5 samples and their respective whites, thankful for already
MS-DIAL / Re: MS-Finder search parameters
Last post by Leon -
Hi Hirsoshi,

I was using the library for LC-MS and also noticed those few entries for LC-MS..
However just a few days ago I tried to do Identification for some spiked samples with the provided GNPS-Library. Out of the samples which were measured with a DIA method I received about 100 identified metabolites from over thousand(s) of features. Even when I "lowered" the identification parameters the number of identified metabolites did not change. Also, if I remember correctly, not all of the spiked metabolites were identified. Maybe you do already have an idea of what could be the reason for this with those information. Nevertheless I can post my parameters as soon as possible to give you more details.



For the identification I used a tolerance of 0.1 Da each and a 50% score cut off. This should definitely give me more than my 92 identified features...
What also came in mind when I reviewed the data today was that even though the DIA data should contain MS2 spectra for 75 Da to 325 Da (window width is evenly distributed: 75-125, 125-175,...) MS-DIAL only displayed a range from 75 Da to about 140 Da. I do not know why that is the case.. My experiment file is just as the one that is provided and I also did not set any m/z limitation in the settings.

It would be such a relief to know what the reasons for my failure with MS-DIAL are :(
MS-DIAL / Re: Processing CCS files from Waters Vion ?
Last post by Sukis -
Dear Hiroshi,
Do you have a solution to work with Waters Vion ccs file (UNIFI) now? or Could you tell me which equipment did you use for testing MS-DIAL? It could be useful information when considering to buy a new equipment.

R / Re: Find samples with high deviation in retentiontime, XCMS
Last post by Jan Stanstrup -
I did this a few days ago for a colleague exactly the way that you describe.
Replace `xcms_p_POS_g_r_g` with your object.

Code: [Select]

investigate_data <- tibble(rtime =        rtime(xcms_p_POS_g_r_g, adjusted = FALSE, bySample = TRUE),
                          adjustedRtime = rtime(xcms_p_POS_g_r_g, adjusted = TRUE, bySample = TRUE),
                          file =          basename(fileNames(xcms_p_POS_g_r_g))
                          ) %>%
    unnest(c(rtime, adjustedRtime)) %>%
    mutate(diff =          adjustedRtime - rtime)

investigate_data %>%
    filter(abs(adjustedRtime-250)<1) %>% # <-- focus on a small RT range where the outlier is clear
    group_by(file) %>%
    arrange(diff) %>% # <-- use desc if looking for a positive difference
    slice(1) %>%
    ungroup %>%
    arrange(diff)     # <-- use desc if looking for a positive difference

R / Find samples with high deviation in retentiontime, XCMS
Last post by enn -
The plot from "PlotadjustedRtime", show there is one sample with great deviation and I want to find which sample this is.

With adjustedRtime(df)
and rtime(df)
I can compare the adj rt for each feature.

I though I could check which features they are (the one with the extreme diff in rt) and then see in which sample they are present after correspondence, but then features are merged and they have got other names.

Does anyone have a suggestion on how I can identify the sample?

Many thanks!