1. Is there relation between minfrac and minsamp in xcms::group.density step? For example, if I have two classes and each class contains 6 samples. If my minfrac is set at 0.5, must I set my minsamp to 3?
2. My data is a little complex. I have 2 classes, control and treatment. Each class has 9 subclasses of time and each subclasses has 5 samples. At first, I created just 2 folders for 2 classes (45 samples for 1 file), and I applied xcms. 1090 peak groups were found. Then, I tried to subdivide samples. I created 9 subfolders in every folders and each subfolder contians just 5 samples. I reapplied xcms with the same script, but this time only 251peaks groups were found. So I don't know for my experiment, which way is better for me?
Be aware that minsamp and minfrac are counted in each sample class separately.
I'm interesting to know how xcms counts sample class. Should we create enough class files (equal to the number of sample classes) to tell xcms our classes? If there are subclasses? Is there correlation between minfrac and minsamp?
For example, if I have 16 samples for 2 classes. I create 2 files and each file contains 8 samples. If for my studies, the feature is seen in at least 2 samples of any one class, it can be a valid group. My minfrac must be set to 0.25 and my minsamp must be set to 2? And, how does xcms settle features detected in both classes?
Your explication is interesting for me. Could you tell me please how we should arrange our data before applying xcms? Should we create 3 files for example, for theses 3 classes or just put all the 18 samples in one single file?
Up to now, I always create files equal to the number of my classes, but my minfrac is 0.05. I think it's too small, isn't it?
I want to set a RT range, because the RT of my hugest group in CAMERA result is 1233s. It's almost the end of my chromatogram and there are some contamination. I don't know the usefulness of this test, I just try it.
Hi Jan, I appreciate your suggestions and your patience. You are right, the intval is not the same, when I set intval="maxo" in annotateDiffreport, it's ok.
I met a new problem... I think I made a grave mistake at the beginning of xcmsSet. My MS data is centroid mode, but in the findpeak step, I used matchedFliter method which is for profile mode MS data. I don't know if we can use matcheFliter for centroid mode. Maybe that's the reason for my unsatisfactory CAMERA list. I try xcms with centWave method, but no peak group was found. I study on centwave. Maybe see you later in the XCMS column. Thanks again for your help.
Thank you very much for your helpful explication, you are so so so great!!!
For yesterday’s CAMERA, I didn’t get all my samples so I just used serval samples to test CAMERA. The evolution of group 1 and 2 means the chromatographic profile. Group 1 and 2 have the same expression. They are all strongly expressed in class 2. Today I did a new CAMERA with all my samples. I have 3 classes, control and treatment 1 and 2. In these three classes, there are 9 subclasses of time and each subclass contains 5 replicate samples. I try to list my questions clearly : )
1. In my XCMS, I use obiwarp method for the retention time correction, I don’t know if it will influence my CAMERA. For example, the perfwhm value.
2. According to your explication, I use both calcCiS and calcCaS this time. But I don’t want to use isotopic relationship for peak grouping, the calcIso is FALSE. I do first with your CAMERA rules each step separately for ploting EICs :
I used the same parameter value for these two processes, but I have different results. When I did each step separately, CAMERA find 59 isotopes, 320 adduct and 646 annotation groups. When I did annotateDiffreport, CAMERA find 50 isotopes, 245 adducts and 802 annotation groups. Do you have any idea for this difference? Did you combine the xcms matrix and camera matrix before? If not, how do you integrate these two matrixes?
3. In your CAMERA rules, all the quasi set to 0 except [M-H]-, does it mean if there are only adducts in the annotation group, this group is excluded? So, for each [nM+ions]-, there should be a [M]- in the same annotation group?
4. I sort my annotation groups in sequence (A to Z). Most adducts present in first 50 groups, and after group 252 there is no adducts. I think the order of groups maybe has some sense?
5. In my result, I have some huge groups. For example, my group 6 contains 92 features. There are 56 adducts in this group, the mass from472 to 1195 and the retention time from 1227s to 1233s. Do you know how to plot the two right graphs of figure 2 in Carsten Kuhl’s paper (Anal Chem. 2012 January 3; 84(1): 283–289. doi:10.1021/ac202450g) ? I try to do calcPC, but I have problems: > calcPC.hcs(xsaFA.neg) Error in `colnames<-`(`*tmp*`, value = c(NA, NA, "weight")) : attempt to set 'colnames' on an object with less than two dimensions > calcPC.lpc(xsaFA.neg) Error in `colnames<-`(`*tmp*`, value = c(NA, NA, "weight")) : attempt to set 'colnames' on an object with less than two dimensions
That's all the questions for today~~~ Thank you very much in advance!!!
I have the same question. What I understand by Jan and Carsten is that: features in the same group are not the same compound, it may be two or more compounds with high correlation. This group is independent of the annotations iso and adduct.
In my experiment, there are 3 classes, and each class has 5 replicate samples. Because of multiple samples, I use calcCas method not calcCiS.
I give you my CAMERA script:
diffreportcombi.neg1<-annotateDiffreport(xset4,perfwhm=0.4,calcCiS=FALSE,calcIso=TRUE,calcCaS=TRUE,maxcharge=3,maxiso=4,minfrac=0.05,ppm=5, mzabs=0.015,polarity="negative") write.csv(diffreportcombi.neg1,file="diffreport test 1.csv")
My questions are: Peak M401T723 and M836T723 are in the same group, what this means? I don't think they mean same compound. May be the are co-eluting? If they are co-elution, How CAMERA separates these two groups which have the same retention time? I checked intensities of these 11 features. They present same evolution across all the samples. If features in group 1 have high correlation, features in group 2 also have high correlation with group 1 because of the same evolution?
I know there are a lot of mysterious for me in CAMERA. Thank you for your help.