I have tried the approach 2 and got a rmsip.dat file. But it contained only
2 lines. Is it how the rmsip file look like? The result looked like this
#evecs2.dat_X_evecs3.dat
0.396164
Input:
parm parmsg.prmtop
trajin protsg.nc
trajin protdg.nc
readdata evecs2.dat
readdata evecs3.dat
modes rmsip out rmsip.dat name evecs2.dat name2 evecs3.dat beg 1 end 5
Regards,
Sruthi Sudhakar
On Thu, Jun 17, 2021 at 9:36 PM Sruthi Sudhakar <
sruthisudhakarraji.gmail.com> wrote:
> Trajin is given Actually it was a typo. trajin fit1.nc was loaded and
> like I said above T1.dat has 250000 frames but T2.dat has given the error.
> I had attached the files in the mail I had directly send you. Is there any
> other error in the pca1.in, pca2.in and pca3.in. If not I am not sure
> what is happening to the T2.dat. Because almost all the kde and hist
> commands seems to give odd results.
> Regards,
> Sruthi Sudhakar
>
>
> On Thu, Jun 17, 2021 at 8:36 PM Daniel Roe <daniel.r.roe.gmail.com> wrote:
>
>> Hi,
>>
>> It looks like in your 'pca3.in' file you're missing the 'trajin'
>> statements, i.e. there is nothing for the 'projection' commands to
>> work with.
>>
>> -Dan
>>
>> On Thu, Jun 17, 2021 at 7:23 AM Sruthi Sudhakar
>> <sruthisudhakarraji.gmail.com> wrote:
>> >
>> > Thanks Dr. Anselm for adding the comments. I had some more follow up
>> > questions on the approach one , which I had directly mailed Dan. But for
>> > beginners like me who might face similar issue, I would like to post the
>> > question here. And I would really appreciate it if someone would just
>> > verify if my script is right or not. Because it has been really a
>> > tough task to find the loophole in this since I feel my input is right.
>> > I have tried approach one, where I stripped off the nucleic acid part
>> in
>> > both (box info not removed) and used a common prmtop file. But in that
>> > case, I do face some errors in the modes of the second trajectory. The
>> > method I used is
>> > 1. load both the stripped trajectories, rms fit to the first frame, and
>> > write out the average file;
>> > 2. the second stage loaded the 2 stripped trajectories, rms fit the
>> > trajectories to the average, calculated covariance matrix and wrote out
>> a
>> > fit nc file (500000 frames - the sum of both trajectories) and used the
>> > diagmatrix command;
>> > 3. In the third stage where all the analysis were done I had faced the
>> > error. In the third stage, the fit trajectory with 500000 frames was
>> loaded
>> > and I used the projection command to get the PC 1 and 2 for the
>> > trajectories separately by dividing the frames which gave odd results.
>> The
>> > KL-PC1 (according to the script below, all inputs are attached too) was
>> not
>> > formed at all. T1.dat read 250000 frames but the T2.dat had 500000
>> > coordinates. Projection of T2 was supposed to begin from the 250001th
>> frame
>> > and end in the last frame. The histogram of T1 gave a Gaussian
>> distribution
>> > but the histogram of T2 was not giving a Gaussian distribution. T2.dat
>> > contained all values of mode 1 and mode 2 as zero for the first 250000
>> > frames.
>> >
>> > Could you give any insights regarding why this might be happening? I
>> > suspect it is some error in the script below (input.txt), since, in
>> > independent analysis, only the loaded frames appeared in the modes,
>> unlike
>> > the T2 of this case. Also, would you recommend combining the
>> trajectories
>> > much before fitting the trajectory, where instead of loading 2 stripped
>> > trajectories in the first stage and then creating a combined fit
>> > trajectory, we could directly create a single trajectory in the very
>> first
>> > stage?
>> > Regards,
>> > Sruthi Sudhakar
>> >
>> > parm parmsg.prmtop
>> > trajin protsg.nc
>> > trajin protdg.nc
>> > # Fit to the first frame to remove global rotation/translation
>> > rms first :1-1362.CA
>> > # Write out the restart
>> > average gaccAvg1.rst7 restart
>> >
>> > pca2.in
>> > parm parmsg.prmtop
>> > trajin protsg.nc
>> > trajin protdg.nc
>> > # Read in average structure, tag as [avg]
>> > reference gaccAvg1.rst7 [avg]
>> > # RMS-fit coordinates to average
>> > rms ref [avg] :1-1362.CA
>> > # Calculate coordinate covariance matrix
>> > matrix covar :1-1362.CA name gaccCovar
>> > # Write out the fit trajectory
>> > trajout fit1.nc
>> > # Diagonalize coordinate covariance matrix for eigenmodes (only 2)
>> > diagmatrix gaccCovar out evecs1.dat vecs 20 nmwiz nmwizvecs 5 nmwizfile
>> > prot.nmd nmwizmask :1-1362.CA
>> >
>> > Pca3.in
>> > parm parmsg.prmtoptrajin fit1.nc
>> > # Read in modes
>> > readdata evecs1.dat name MyModes
>> > # Now create separate PC projections for each trajectory
>> > projection T1 modes MyModes beg 1 end 2 :1-1362.CA start 1 stop 250000
>> out
>> > T1.dat
>> > projection T2 modes MyModes beg 1 end 2 :1-1362.CA start 250001 stop
>> 500000
>> > out T2.dat
>> > # Calculate Kullback-Leibler Divergence vs time for PC histogramskde
>> T1:1
>> > kldiv T2:1 klout KL-PC1.dat bins 200 name SG-DG-1
>> > kde T1:2 kldiv T2:2 klout KL-PC2.dat bins 200 name SG-DG-2
>> > kde T1:1 out kde1-PC1.dat bins 200 name KDE1-1
>> > kde T2:1 out kde2-PC1.dat bins 200 name KDE2-1hist T1:1 bins 200 out
>> > T1-hist1.dat normint name HIST1-1
>> > hist T1:2 bins 200 out T1-hist2.dat normint name HIST1-2
>> > hist T2:1 bins 200 out T2-hist1.dat normint name HIST2-1
>> > hist T2:2 bins 200 out T2-hist2.dat normint name HIST2-2
>> >
>> >
>> >
>> > On Thu, Jun 17, 2021 at 2:52 PM Dr. Anselm Horn <anselm.horn.fau.de>
>> wrote:
>> >
>> > > Hi,
>> > >
>> > > many thanks to Dan for his explaining comments regarding the PCA
>> analysis!
>> > >
>> > > Since this issue (different systems) bothered me for many years and I
>> > > had many discussions about that I'd like to add my thoughts here.
>> > >
>> > > I see some disadvantages of approach 2, i.e. performing isolated PCA
>> > > analyses on the different systems. As Dan pointed out, the underlying
>> > > eigenvectors will certainly not be the same. Although a pairwise
>> > > comparison (e.g. via RMSIP) between the eigenvectors of the two
>> systems
>> > > is possible, in praxi there may arise problems:
>> > > 1) the eigenvectors will have a different size, if you include all
>> atoms
>> > > of the full systems, or the vector entries may represent different
>> > > atoms. Thus, a comparison may not be that straightforward.
>> > > 2) from the pairwise comparison between the eigenvectors of the two
>> > > systems you deduce a correspondence between them. Maybe in real
>> systems
>> > > the most important eigenvectors can clearly be matched, but in
>> principle
>> > > the matching may not be that distinct. And this problem may arise even
>> > > if you restrict your analysis to atoms common on both system (e.g.
>> > > backbone or CA).
>> > > 3) The ease of application of isolated PCA-analysis may seduce the
>> user
>> > > to omit that matching step and just proceed to the well-known
>> > > PCA1:PCA2-histogram plots.
>> > >
>> > > That said, I totally agree with Dan that you may use approach 2 in a
>> > > very elegant way to obtain histogram plots of a similar pattern, if
>> you
>> > > put an extra efford into the comparison step and be aware that the
>> basis
>> > > eigenvectors resemble a similar basis, i.e. describe a similar overall
>> > > motion, but are not the same.
>> > >
>> > > It's my feeling that approach 1, where you restrict you analysis to a
>> > > subset of atoms occuring in all systems investigated (backbone or CA),
>> > > is more "save" and thus should be considered first. In that way, you
>> are
>> > > sure that the basis of all your PCA histograms is the same, and
>> > > differences and equalities in the plot between different systems can
>> > > directly be compared and discussed.
>> > > However, using approach 2 in addition can provide further insights
>> into
>> > > the systems' dynamics.
>> > > And I also agree with Dan that visual inspection might be very
>> helpful.
>> > >
>> > > Best regards,
>> > >
>> > > Anselm
>> > >
>> > >
>> > >
>> > > On 06/16/2021 03:18 PM, Daniel Roe wrote:
>> > > > Hi,
>> > > >
>> > > > I know I just responded to you directly but I'll post the reply here
>> > > > as well in case people search for it in the future.
>> > > >
>> > > > So when you have different systems and you want to perform principal
>> > > > component analysis there are two ways you can go. You can either 1)
>> > > > modify one or both of the systems so you have a common core (i.e.
>> > > > remove the parts of each system via 'strip' which don't match the
>> > > > other) and perform a combined PCA on that, or 2) perform PCA on each
>> > > > system separately and then compare the resulting eigenvectors via
>> > > > something like RMSIP (root mean square inner product).
>> > > >
>> > > > The advantage of approach 1 is the usual advantage of the combined
>> PCA
>> > > > approach - you're guaranteed that each eigenvector is the same for
>> the
>> > > > separate systems. This works best when the differences between the
>> > > > systems are not too extreme, but if the systems are too different
>> this
>> > > > should show up as e.g. non-overlapping PC projection histograms. The
>> > > > disadvantage is that if you have important contributions to the
>> > > > overall motion from parts that you've removed this of course will
>> not
>> > > > show up in your final analysis.
>> > > >
>> > > > The advantage of approach 2 is that you can perform PCA on each full
>> > > > system, so you won't miss any motion. The disadvantage is that
>> you're
>> > > > not guaranteed that the eigenvectors from each analysis will match
>> > > > (and it's likely they won't), i.e. PC 1 in system A will not be the
>> > > > same as PC 1 in system B. This is where you would use RMSIP to
>> compare
>> > > > the different PCs to see which (if any) match.
>> > > >
>> > > > My feeling is that both approaches should be tried. Approach 2 will
>> > > > give you a sense for whether the motions in the systems are similar
>> > > > enough for approach 1 to make sense. Also, really make use of the
>> > > > 'modes trajout' analysis to generate pseudo trajectories (and also
>> the
>> > > > 'nmwiz' keyword for the 'diagmatrix' command for use with the VMD
>> > > > nmwiz plugin) for the first 2-3 PCs for each system to get a sense
>> for
>> > > > what the motions actually look like. In my opinion there's really no
>> > > > substitute for visualization when it comes to PCA.
>> > > >
>> > > > Hope this helps!
>> > > >
>> > > > -Dan
>> > > >
>> > > > On Sat, Jun 12, 2021 at 5:16 PM Sruthi Sudhakar
>> > > > <sruthisudhakarraji.gmail.com> wrote:
>> > > >>
>> > > >> This method of breaking the analysis into stages actually worked
>> and I
>> > > was
>> > > >> able to complete the analysis overcoming the memory issues.
>> > > >> An additional query: Incase we are using 2 different trajectories
>> with
>> > > >> different number of residues (1530 and 1526) to do a combined
>> trajectory
>> > > >> analysis, would it be enough to load gamd2.nc (1530 residues) and
>> > > >> gamd3.nc (1526
>> > > >> residues) together? Each has 250000 frames. And in that case do we
>> need
>> > > to
>> > > >> fit into a single trajectory (fit.nc) or separately create 2 fit
>> > > >> trajectory? I am doubtful about the third stage where we load the
>> fit
>> > > >> trajectory, should we load both the parm files in that case? Also
>> would
>> > > the
>> > > >> difference in the total number of residues create any problem?
>> > > >>
>> > > >> My sample script following your guidelines would be
>> > > >> pca1.in
>> > > >> parm noionsa.prmtop [ag]
>> > > >> parm noionsb.prmtop [bg]
>> > > >> trajin gamd2.nc parm [ag]
>> > > >> trajin gamd3.nc parm [bg]
>> > > >> # Fit to the first frame to remove global rotation/translation
>> > > >> rms first :1-1362.CA
>> > > >> # Write out the restart
>> > > >> average gaccAvg.rst7 restart
>> > > >>
>> > > >> pca2.in
>> > > >> parm noionsa.prmtop [ag]
>> > > >> parm noionsb.prmtop [bg]
>> > > >> trajin gamd2.nc parm [ag]
>> > > >> trajin gamd3.nc parm [bg]
>> > > >> # Read in average structure, tag as [avg]
>> > > >> reference gaccAvg.rst7 [avg]
>> > > >> # RMS-fit coordinates to average
>> > > >> rms ref [avg] :1-1362.CA
>> > > >> # Calculate coordinate covariance matrix
>> > > >> matrix covar :1-1362.CA name gaccCovar
>> > > >> # Write out the fit trajectory
>> > > >> trajout fit.nc
>> > > >> # Diagonalize coordinate covariance matrix for eigenmodes (only 2)
>> > > >> diagmatrix gaccCovar out evecs.dat vecs 20 nmwiz nmwizvecs 5
>> > > >> nmwizfile prot.nmd nmwizmask :1-1362.CA
>> > > >>
>> > > >> pca3.in
>> > > >>
>> > > >> parm noionsa.prmtop
>> > > >> parm noionsb.prmtop
>> > > >> trajin fit.nc
>> > > >> # Read in modes
>> > > >> readdata evecs.dat name MyModes
>> > > >> # Now create separate PC projections for each trajectory
>> > > >> projection T1 modes MyModes beg 1 end 20 :1-1362.CA start 1 stop
>> > > 250000 out
>> > > >> T1.dat
>> > > >> projection T2 modes MyModes beg 1 end 20 :1-1362.CA start 250001
>> stop
>> > > >> 500000 out T2.dat
>> > > >> # Calculate Kullback-Leibler Divergence vs time for PC histograms
>> > > >> kde T1:1 kldiv T2:1 klout KL-PC1.dat bins 200 name SG-DG-1
>> > > >> kde T1:2 kldiv T2:2 klout KL-PC2.dat bins 200 name SG-DG-2
>> > > >> kde T1:1 out kde1-PC1.dat bins 200 name KDE1-1
>> > > >> kde T2:1 out kde2-PC1.dat bins 200 name KDE2-1
>> > > >> hist T1:1 bins 200 out T1-hist1.dat normint name HIST1-1
>> > > >> hist T1:2 bins 200 out T1-hist2.dat normint name HIST1-2
>> > > >> hist T2:1 bins 200 out T2-hist1.dat normint name HIST2-1
>> > > >> hist T2:2 bins 200 out T2-hist2.dat normint name HIST2-2
>> > > >>
>> > > >> Kindly suggest any corrections required.
>> > > >> Regards,
>> > > >> Sruthi Sudhakar
>> > > >>
>> > > >>
>> > > >> On Sat, Jun 12, 2021 at 12:11 PM Sruthi Sudhakar <
>> > > >> sruthisudhakarraji.gmail.com> wrote:
>> > > >>
>> > > >>> This method actually worked and I was able to complete the
>> analysis. An
>> > > >>> additional query: Incase we are using 2 different trajectories to
>> do a
>> > > >>> combined trajectory analysis, would it be enough to load gamd2.nc
>> and
>> > > >>> gamd3.nc together? And in that case do we need to fit into a
>> single
>> > > >>> trajectory (fit.nc) or separately create 2 fit trajectory?
>> > > >>>
>> > > >>> Regards,
>> > > >>> Sruthi Sudhakar
>> > > >>>
>> > > >>>
>> > > >>> On Wed, Jun 2, 2021 at 11:32 PM Daniel Roe <
>> daniel.r.roe.gmail.com>
>> > > wrote:
>> > > >>>
>> > > >>>> Hi,
>> > > >>>>
>> > > >>>> First, if you haven't already I really recommend that you look
>> at some
>> > > >>>> literature to familiarize yourself with what principal component
>> > > >>>> analysis actually does. It's important to understand what each
>> part of
>> > > >>>> the analysis is for. I like the old "essential dynamics" papers,
>> but a
>> > > >>>> very rough explanation is given in the cpptraj PCA tutorial:
>> > > >>>>
>> > > >>>>
>> > >
>> https://amberhub.chpc.utah.edu/introduction-to-principal-component-analysis/
>> > > >>>>
>> > > >>>> Also, read the cpptraj manual so you know what each command is
>> > > actually
>> > > >>>> doing.
>> > > >>>>
>> > > >>>> That said, I now realize my examples in the manual and the
>> tutorial
>> > > >>>> both rely on having the trajectories stored in memory. So I will
>> give
>> > > >>>> a rough outline of how to do it all on-disk.
>> > > >>>>
>> > > >>>> 1) Create average coordinates.
>> > > >>>>
>> > > >>>> parm noions.prmtop
>> > > >>>> trajin gamd2.nc
>> > > >>>> # Fit to the first frame to remove global rotation/translation
>> > > >>>> rms first :1-1530&!.H=
>> > > >>>> # Write out the restart
>> > > >>>> average gaccAvg.rst7 restart
>> > > >>>>
>> > > >>>> 2) Rms fit your trajectory to average coordinates, calculate the
>> > > >>>> covariance matrix, write out the fit trajectory, diagonalize the
>> > > >>>> matrix and write out the "modes" data (i.e. eigenvectors and
>> > > >>>> eigenvalues).
>> > > >>>>
>> > > >>>> parm noions.prmtop
>> > > >>>> trajin gamd2.nc
>> > > >>>> # Read in average structure, tag as [avg]
>> > > >>>> reference gaccAvg.rst7 [avg]
>> > > >>>> # RMS-fit coordinates to average
>> > > >>>> rms ref [avg] :1-1530&!.H=
>> > > >>>> # Calculate coordinate covariance matrix
>> > > >>>> matrix covar :1-1530&!.H= name gaccCovar
>> > > >>>> # Write out the fit trajectory
>> > > >>>> trajout fit.nc
>> > > >>>> # Diagonalize coordinate covariance matrix for eigenmodes (only
>> 2)
>> > > >>>> diagmatrix gaccCovar out evecs.dat vecs 2 nmwiz nmwizvecs 2
>> nmwizfile
>> > > >>>> cas.nmd nmwizmask :1-1530&!.H=
>> > > >>>>
>> > > >>>>
>> > > >>>> 3) Read in the "modes" data, calculate principal component
>> projections
>> > > >>>> from the fit trajectory, do the Kullback-Leibler divergence
>> analysis.
>> > > >>>>
>> > > >>>> parm noions.prmtop
>> > > >>>> trajin fit.nc
>> > > >>>> # Read in modes
>> > > >>>> readdata evecs.dat name MyModes
>> > > >>>> # Now create separate PC projections for each trajectory
>> > > >>>> projection T1 modes MyModes beg 1 end 2 :1-1530&!.H= start 1 stop
>> > > >>>> 50000 out T1.dat
>> > > >>>> projection T2 modes MyModes beg 1 end 2 :1-1530&!.H= start 50001
>> stop
>> > > >>>> 100000 out T2.dat
>> > > >>>> <other projection commands>
>> > > >>>> # Calculate Kullback-Leibler Divergence vs time for PC histograms
>> > > >>>> kde T1:1 kldiv T2:1 klout KL-PC.agr bins 400 name AMD-MREMD-1
>> > > >>>> <other kde commands and analyses>
>> > > >>>>
>> > > >>>> Hope this helps,
>> > > >>>>
>> > > >>>> -Dan
>> > > >>>>
>> > > >>>> On Tue, Jun 1, 2021 at 3:23 PM Sruthi Sudhakar
>> > > >>>> <sruthisudhakarraji.gmail.com> wrote:
>> > > >>>>>
>> > > >>>>> The available memory in the beginning of cpptraj is shown as 32
>> Gb
>> > > and
>> > > >>>>> estimated memory usage is 82gb. I am really confused about this
>> > > memory
>> > > >>>>> allotment statistics. I am doing the process in a disk with
>> more than
>> > > >>>> 3TB
>> > > >>>>> space. I am not well versed with this technicality. Could
>> someone
>> > > please
>> > > >>>>> exaplain how to overcome the issue in this principal component
>> > > analysis
>> > > >>>>> part? I did understand that we have to separate the analysis
>> into 3
>> > > >>>> phases
>> > > >>>>> but not clear as to how the inputs should be changed. Kindly
>> advise.
>> > > >>>>>
>> > > >>>>> On Tue, 1 Jun 2021 at 7:21 PM, Sruthi Sudhakar <
>> > > >>>> sruthisudhakarraji.gmail.com>
>> > > >>>>> wrote:
>> > > >>>>>
>> > > >>>>>>
>> > > >>>>>> Thank you for the reply. Since I am doing this for the first
>> time, I
>> > > >>>>>> wanted to know if I am supposed to create 3 separate inputs
>> to run
>> > > in
>> > > >>>>>> cpptraj to do the methodology you suggested.
>> > > >>>>>>
>> > > >>>>>> Regards,
>> > > >>>>>> Sruthi
>> > > >>>>>>
>> > > >>>>>> On Tue, 1 Jun 2021 at 6:33 PM, Daniel Roe <
>> daniel.r.roe.gmail.com>
>> > > >>>> wrote:
>> > > >>>>>>
>> > > >>>>>>> Hi,
>> > > >>>>>>>
>> > > >>>>>>> You are likely running out of memory. This is why your
>> problems
>> > > >>>> during
>> > > >>>>>>> clustering went away when you reduced the number of input
>> frames
>> > > by a
>> > > >>>>>>> factor of 5. The solution is to do everything on disk. So
>> instead
>> > > of
>> > > >>>>>>> loading all coordinates into memory, separate the principal
>> > > component
>> > > >>>>>>> analysis into three separate phases:
>> > > >>>>>>>
>> > > >>>>>>> 1) Create average coordinates.
>> > > >>>>>>> 2) Rms fit your trajectory to average coordinates, calculate
>> the
>> > > >>>>>>> covariance matrix, write out the fit trajectory, diagonalize
>> the
>> > > >>>>>>> matrix and write out the "modes" data (i.e. eigenvectors and
>> > > >>>>>>> eigenvalues).
>> > > >>>>>>> 3) Read in the "modes" data, calculate principal component
>> > > >>>> projections
>> > > >>>>>>> from the fit trajectory, do the Kullback-Leibler divergence
>> > > analysis.
>> > > >>>>>>>
>> > > >>>>>>> This way, the most memory you need is to store the covariance
>> > > matrix,
>> > > >>>>>>> modes, and other data of that type. Hope this helps,
>> > > >>>>>>>
>> > > >>>>>>> -Dan
>> > > >>>>>>>
>> > > >>>>>>> PS - Note that there appears to be a small error in the input
>> you
>> > > >>>>>>> posted (in pca.in). The 'nmwiz' keyword should be part of the
>> > > >>>>>>> diagmatrix command, not on a separate line.
>> > > >>>>>>>
>> > > >>>>>>>
>> > > >>>>>>> On Mon, May 31, 2021 at 1:22 PM Sruthi Sudhakar
>> > > >>>>>>> <sruthisudhakarraji.gmail.com> wrote:
>> > > >>>>>>>>
>> > > >>>>>>>> Dear all,
>> > > >>>>>>>>
>> > > >>>>>>>> I have been doing pca analysis on an accelerated MD
>> trajectory of
>> > > >>>> 500ns
>> > > >>>>>>>> (250,000 frames). I have attached the input file I have used
>> for
>> > > >>>> the
>> > > >>>>>>> study.
>> > > >>>>>>>> The job stops at the createcrd stage. Basically, the job gets
>> > > >>>> killed at
>> > > >>>>>>>> 30%. The same happened during the cluster analysis reading
>> every
>> > > >>>> frames.
>> > > >>>>>>>> The clustering error was solved when I changed the input to
>> read
>> > > >>>> every
>> > > >>>>>>> 5th
>> > > >>>>>>>> frame. Now since this is repeating in pca analysis, kindly
>> help
>> > > >>>>>>> regarding
>> > > >>>>>>>> the same.
>> > > >>>>>>>>
>> > > >>>>>>>>
>> > > >>>>>>>> Regards,
>> > > >>>>>>>> Sruthi Sudhakar
>> > > >>>>>>>> _______________________________________________
>> > > >>>>>>>> AMBER mailing list
>> > > >>>>>>>> AMBER.ambermd.org
>> > > >>>>>>>> http://lists.ambermd.org/mailman/listinfo/amber
>> > > >>>>>>>
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Received on Fri Jun 18 2021 - 23:30:02 PDT
