But may I occupy this thread by asking almost the same question the other way round.
I am using Ribitol as internal STD (which is not recommended ) and the peak is correctly annotated.
But even if I tick: Keep identified and annotated metabolites TRUE Keep removable features and assign the tag for checking TRUE
The ISTD does not show up in the final alignment result table. Has anyone experienced the same problem?! I solved this issue by exporting all metabolites and filtering myself, but this can be annoying.
when I saw the title of this section I was sure that my request fits in here. After reading some of the other post I became sceptical but still I would like to place my question somewhere.
I found some interesting Peaks in my GC-EI-TOF data (MEOX + TMS derivatized polar extracts of natural microbial communities) and the library's couldn't help in understanding the compound I found here. The RI of the requested analyte is 2003.3 and I have a very similar spectrum occuring at RI 2055.2. It seems to me that there one analyte with different configurations (like the sugars) but m/z 278 is a black box for me. Does anyone knows which fragment m/z 278 typically represents? Or can someone help me with the compound class?
You can find the spectrum table of this compound attached to this post. Many thanks for any comment!
I was busy with paper writing so I haven't checked the forum for a while. @ Hiroshi:I will supply some data for testing by the end of the week.
I could imagine a simple solution to this problem by adding the option "clear gap-filled values after alignment" in the alignment tab (to guarantee that the barplot of the peak is not affected by the gap filled values) or adding this option at least in the alignment export. The location of the gap-filled spots is tracked so this shouldn't represent a big hurdle.
But I know there are so many requests to Hiroshi so I don't want to underestimate the workload and rather say thank you for this handy tool!
Even though there were some manuals how to make the GMD run, I still experience trouble with respect to the recognition of entrys (like RI, etc). The GMD contains this information but following the instruction of Biswa does not allow to use those features in MS DIAL. Furthermore, if I follow the instruction of Jan Hummel published on the Max-Planck-Institute Website (Golm) AMDIS does not recognize features that have spaces in their name. A short example:
Name: M000000_A097001-101-xxx_NA_959,45_PRED_VAR5_ALK_Unknown#bth-pae-001 is displayed correctly (first entry)
Name: M000880_A098001-101-xxx_NA_986,97_TRUE_VAR5_ALK_Glycine, N,N-dimethyl- (1TMS) is skipped (second entry)
I would like to ask Dmitry to provide a short instruction on the replacements he did. Does anyone has a corrected Version of the GMD_20111121_VAR5_ALK_MSP in MSP format he or she could provide?
In the meantime I could identify the origin of my problem. This error occured because one sample was not analysed properly. I have inspected the sample manually and I could not detect problems in the measurement. But MS-DIAL assigned negative peak areas to some of the peaks and this is where I recognized the cause of error.
To avoid this error you need to restart the analysis from scratch without the sample. If you deselect the problematic sample from the file property setting the error remains active. So MS-DIAL always takes the full dataset for alignment. Samples which are excluded from the file property setting are just removed from the display (PCA, Boxplots, etc...)
I have another question regarding the default gap filling. Is any peak that was in-silico generated (and marked by -2) excluded from the filtering of the blanks. In a worst case you do not have peaks in the blank but after the alignment in-silico generated peaks have replaced the empty spots. Now those peaks might have the same avarage as the sample and therefore the peak is excluded from the alignment. Have you considered this scenario?
I know this option already. But I am wondering if this could be done automatically, since non-normalized samples mess up the statistics anyway (so they need to be deactivated). Another issue with those samples is the Normalization Plot (which I really like!!!), where those sample show up even after deselcting them from the "File property setting". See the big peaks in "Norm_plot_example.PNG".
I have moved away from using RT as alignment criterion since the RI works now (after I switched the OS etc). This also solved the problem of the huge RT differences. I won't dig deeper into the posted error message since I facing other issues which are more striking than this one.
thank you for your reply! I really appreciate having contact to the developer directly (which wasn't the case with any other software so far). Now I ran the alignment with and without the "Gap filling by compulsion option". There is basically no difference in the output. Still there were many peaks filled by values higher than the measured values. Please have a look to the attached Plot below. This time I have evaluated another dataset, so there is a slight difference in the Plot (but the main message stays the same).
If someone else want's to explore his/her alignment results, here is my R Code. Please adapt all red marked spots to your data.
my questions might be annoying to the expert users but I still have a longer wish list for MS-DIAL
These days I have realized that MS-DIAL does not remove samples from the statistics that lack the normalization STD. In my case I analyzed some solvent blanks which I treated as additional blanks (beside my process blanks). When I normalize to my internal STD almost all peaks are normalized, except for these blanks. This is a plausible behaviour but a further analysis of these (non-normalized) samples should be avoided, right?
What if MS-Dial deselects all samples that haven't been normalized properly for the further analysis?
After I have learned how to identify the gap filled values in my dataset I went deeper into the analysis how much this might influence my dataset. What I basically did was:
1) I filtered all gap filled spots and compared their mean with the mean of the real values (measured) of this peak (row-wise). 2) I calculated a ratio of real value / gap filled values and converted this ratio into a factor.
What I received from this comparison is plotted in Plot_gap_filling - Copy.png. Most of you will find this plot self explaining but still I want to highlight what I want to show.
Peaks on the left of the red line have (in my case ~22% of the data) have higher values in the gap filled cells than the measured values and all the bars between 1 (equal values) and 5 (measured values 5x higher) depict cases (43 % of all peaks) where the real values and the interpolated values are very close.
This has a great impact on my results and I am wondering if I am doing something wrong?! Is there someone in the metabolomics community who want to comment on this?
I greatly appreciate your comment or explaination!
Thank you lh1989 for asking this question and Hiroshi for your explaination. I have to admit that I am not really satisfied with the automated gap filling and up to this moment I thought I can switch on/off the gap filling according to my demands. I also wondered about the fact that there weren't gaps in my dataset, even when "Data processing" -> "Analysis parameter setting" -> "Alignment" -> "gap filling by compulsion" was off.
Now I am considering to export the aligned data and replace the gap filled spots with 1/10 of the peak minimum to concentrate on measured values instead of interpolations. I have the feeling that this modification of my data has consequences on the final assessment (especially if there were many gaps filled). But I really like the statistics etc. of MS Dial and really don't want to leave the softwarte! How do the others feel about the gap filling? Or is there another option to avoid the gap filling?
) and the peak is correctly annotated. 