# Re: [AMBER] Error in pca analysis

From: Daniel Roe <daniel.r.roe.gmail.com>
Date: Mon, 21 Jun 2021 10:33:07 -0400

Hi,

Sorry for the delay. This email thread has gotten long, and has also
been split up between the mailing list and directly so I may have
missed something, but I think your issue is that in step three you're
doing the projection on both trajectories. So with that in mind I will
try to just post a version of the example I gave previously, modified
for two separate systems. Note an additional first step where I create
a stripped "combined" trajectory that only keeps common elements from
each system:

1) Create a combined, stripped trajectory from 2 separate
trajectories. Keep in mind that the topology you get from the 'strip'
command will probably have different numbering (atom and residue
indexing in Amber always starts from 1 and goes up in integer
increments).

parm system1.parm
trajin system1.nc parmindex 0
parm system2.parm
trajin system2.nc parmindex 1
strip <mask that removes anything different> parmout combined.parm7
trajout combined.nc

2) Create average coordinates from combined trajectory

parm combined.parm7
trajin combined.nc
# Fit to the first frame to remove global rotation/translation
# Write out the restart
average Avg.rst7 restart

3) 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 combined.parm7
trajin combined.nc
# Read in average structure, tag as [avg]
reference Avg.rst7 [avg]
# RMS-fit coordinates to average
# Calculate coordinate covariance matrix
# Write out the fit trajectory
trajout fit.nc
# Diagonalize coordinate covariance matrix for eigenmodes (only 2)
diagmatrix Covar out evecs.dat vecs 2 nmwiz nmwizvecs 2 nmwizfile

4) Read in the "modes" data, calculate principal component projections
from the fit trajectory, do the Kullback-Leibler divergence analysis.

parm combined.parm7
trajin combined.nc
# Now create separate PC projections for each trajectory
projection T1 modes MyModes beg 1 end 2 <mask> start 1 stop <# frames
in system1.nc> out T1.dat
projection T2 modes MyModes beg 1 end 2 <mask> start <# frames in
system1.nc + 1> stop <# frames in combined.nc> 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 Sat, Jun 19, 2021 at 2:13 AM Sruthi Sudhakar
<sruthisudhakarraji.gmail.com> wrote:
>
> 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
> 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:
> >> >
> >> > 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
> >> > 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
> >> > 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
> >> > >
> >> > > 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
> >> > >
> >> > > 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
> >> > > 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
> >> > > >>>>> 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
> >> > > >>>>>
> >> > > >>>>> 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
> >> > > 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
> >> > > >>>> 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
> >> > > >>>>>>>
> >> > > >>>>>>> _______________________________________________
> >> > > >>>>>>> AMBER mailing list
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> >> > > >>>>>>>
> >> > > >>>>>>
> >> > > >>>>> _______________________________________________
> >> > > >>>>> AMBER mailing list
> >> > > >>>>> AMBER.ambermd.org
> >> > > >>>>> http://lists.ambermd.org/mailman/listinfo/amber
> >> > > >>>>
> >> > > >>>> _______________________________________________
> >> > > >>>> AMBER mailing list
> >> > > >>>> AMBER.ambermd.org
> >> > > >>>> http://lists.ambermd.org/mailman/listinfo/amber
> >> > > >>>>
> >> > > >>>
> >> > > >> _______________________________________________
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> >> > > > _______________________________________________
> >> > > > AMBER mailing list
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> >> > > >
> >> > >
> >> > >
> >> > > _______________________________________________
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> >> > AMBER mailing list
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> >>
> >> _______________________________________________
> >> AMBER mailing list
> >> AMBER.ambermd.org
> >> http://lists.ambermd.org/mailman/listinfo/amber
> >>
> >
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Received on Mon Jun 21 2021 - 08:00:02 PDT
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