Sorting data set 20000616 tetrode C (channels 9, 11, 13, 16)

1. Introduction
- 1.1. Getting the data
- 1.2. Getting the code
2. Tetrode D (channels 9, 11, 13, 16) analysis
3. Analyzing a sequence of trials
- 3.1. Create directories were results get saved
- 3.2. Define a "taylored" version of sort_many_trials
4. Systematic analysis of the 50 trials with "pure" Cis-3-Hexen-1-ol
5. The second minute of spontaneous activity recording
6. The third minute of spontaneous activity recording
- 6.1. Do the job
- 6.2. Save results
7. 30 remaining trials with Cis-3-Hexen-1-ol diluted 100 times
8. 50 trials with Cis-3-hexen-1-ol diluted 10 times

1 Introduction

This is the description of how to do the (spike) sorting of tetrode C (channels 9, 11, 13, 16) from data set locust20000616.

1.1 Getting the data

The data are in file locust20000616.hdf5 located on zenodo and can be downloaded interactivelly with a web browser or by typing at the command line:

wget https://zenodo.org/record/21589/files/locust20000616.hdf5

In the sequel I will assume that R has been started in the directory where the data were downloaded (in other words, the working direcory should be the one containing the data.

The data are in HDF5 format and the easiest way to get them into R is to install the rhdf5 package from Bioconductor. Once the installation is done, the library is loaded into R with:

library(rhdf5)

We can then get a (long and detailed) listing of our data file content with (result not shown):

h5ls("locust20000616.hdf5")

We can get the content of LabBook metadata from the shell with:

h5dump -a "LabBook" locust20000616.hdf5

1.2 Getting the code

The code can be sourced as follows:

source("https://raw.githubusercontent.com/christophe-pouzat/zenodo-locust-datasets-analysis/master/R_Sorting_Code/sorting_with_r.R")

2 Tetrode D (channels 9, 11, 13, 16) analysis

We now want to get our "model", that is a dictionnary of waveforms (one waveform per neuron and per recording site). To that end we are going to use trial_01 (20 s of data) contained in the Cis-3-Hexen-1-ol (10^-0) first Group (in HDF5 jargon).

2.1 Loading the data

So we start by loading the data from channels 9, 11, 13, 16 into R, using the 60 s of data contained in Spontaneous first:

lD = rbind(cbind(h5read("locust20000616.hdf5", "/Spontaneous first/ch09"),
                 h5read("locust20000616.hdf5", "/Spontaneous first/ch11"),
                 h5read("locust20000616.hdf5", "/Spontaneous first/ch13"),
                 h5read("locust20000616.hdf5", "/Spontaneous first/ch16")))
dim(lD)

892858

2.2 Five number summary

We get the Five number summary with:

summary(lD,digits=2)

Min. :1432	Min. :1646	Min. :1518	Min. :1630
1st Qu.:2013	1st Qu.:2016	1st Qu.:2013	1st Qu.:2003
Median :2050	Median :2050	Median :2050	Median :2050
Mean :2049	Mean :2049	Mean :2049	Mean :2049
3rd Qu.:2086	3rd Qu.:2083	3rd Qu.:2087	3rd Qu.:2095
Max. :2508	Max. :2362	Max. :2398	Max. :2388

The minimum is on the first channel. This suggests that the largest spikes are going to be found here (remember that spikes are going mainly downwards).

2.3 Plot the data

We "convert" the data matrix lD into a time series object with:

lD = ts(lD,start=0,freq=15e3)

We can then plot the whole data with (not shown since it makes a very figure):

plot(lD)

2.4 Data normalization

As always we normalize such that the median absolute deviation (MAD) becomes 1:

lD.mad = apply(lD,2,mad)
lD = t((t(lD)-apply(lD,2,median))/lD.mad)
lD = ts(lD,start=0,freq=15e3)

Once this is done we explore interactively the data with:

explore(lD,col=c("black","grey70"))

There are many neurons. The signal to noise ratio is OK.

2.5 Spike detection

Since the spikes are mainly going downwards, we will detect valleys instead of peaks:

lDf = -lD
filter_length = 3
threshold_factor = 4
lDf = filter(lDf,rep(1,filter_length)/filter_length)
lDf[is.na(lDf)] = 0
lDf.mad = apply(lDf,2,mad)
lDf_mad_original = lDf.mad
lDf = t(t(lDf)/lDf_mad_original)
thrs = threshold_factor*c(1,1,1,1)
bellow.thrs = t(t(lDf) < thrs)
lDfr = lDf
lDfr[bellow.thrs] = 0
remove(lDf)
sp0 = peaks(apply(lDfr,1,sum),15)
remove(lDfr)
sp0

eventsPos object with indexes of 1444 events. 
  Mean inter event interval: 618.21 sampling points, corresponding SD: 686.65 sampling points 
  Smallest and largest inter event intervals: 18 and 7814 sampling points.

Every time a filter length / threshold combination is tried, the detection is checked interactively with:

explore(sp0,lD,col=c("black","grey50"))

2.6 Cuts

We proceed as usual to get the cut length right:

evts = mkEvents(sp0,lD,49,50)
evts.med = median(evts)
evts.mad = apply(evts,1,mad)
plot_range = range(c(evts.med,evts.mad))
plot(evts.med,type="n",ylab="Amplitude",
     ylim=plot_range)
abline(v=seq(0,400,10),col="grey")
abline(h=c(0,1),col="grey")
lines(evts.med,lwd=2)
lines(evts.mad,col=2,lwd=2)

Figure 1: Setting the cut length for the data from tetrode C (channels 9, 11, 13, 16). We see that we need 20 points before the peak and 20 after.

We see that we need roughly 20 points before the peak and 20 after.

2.7 Events

We now cut our events:

evts = mkEvents(sp0,lD,19,20)
summary(evts)

events object deriving from data set: lD.
 Events defined as cuts of 40 sampling points on each of the 4 recording sites.
 The 'reference' time of each event is located at point 20 of the cut.
 There are 1444 events in the object.

We can as usual visualize the first 200 events with:

evts[,1:200]

Figure 2: First 200 events for the data from tetrode C (channels 1, 3, 5, 7).

There are few superposition and the best way to detect them without excluding good events seems to look for too large negative deviations on both sides of the central valley.

2.8 Removing obvious superposition

We define function goodEvtsFct with:

goodEvtsFct = function(samp,thr=3) {
    samp.med = apply(samp,1,median)
    samp.mad = apply(samp,1,mad)
    under <- samp.med < 0
    samp.r <- apply(samp,2,function(x) {x[under] <- 0;x})
    apply(samp.r,2,function(x) all(x-samp.med > -thr*samp.mad))
}

We apply it with a threshold of 4 times the MAD:

goodEvts = goodEvtsFct(evts,4)

If we look at all the remaining "good" events with (not shown):

evts[,goodEvts]

and at all the "bad" ones (not shown):

evts[,!goodEvts]

we see that our sample cleaning does its job.

2.9 Dimension reduction

We do a PCA on our good events set:

evts.pc = prcomp(t(evts[,goodEvts]))

We look at the projections on the first 4 principle components:

panel.dens = function(x,...) {
  usr = par("usr")
  on.exit(par(usr))
  par(usr = c(usr[1:2], 0, 1.5) )
  d = density(x, adjust=0.5)
  x = d$x
  y = d$y
  y = y/max(y)
  lines(x, y, col="grey50", ...)
}
pairs(evts.pc$x[,1:4],pch=".",gap=0,diag.panel=panel.dens)

Figure 3: Events from tetrode C (channels 9, 10, 11, 12) projected onto the first 4 PCs.

I see at least 4/5 clusters. We can also look at the projections on the PC pairs defined by the next 4 PCs:

pairs(evts.pc$x[,5:8],pch=".",gap=0,diag.panel=panel.dens)

Figure 4: Events from tetrode C (channels 9, 10, 11, 12) projected onto PC 5 to 8.

There is not much structure left beyond the 4th PC.

2.10 Exporting for `GGobi`

We export the events projected onto the first 8 principle components in csv format:

write.csv(evts.pc$x[,1:8],file="tetC_evts.csv")

Using the rotation display of GGobi with the first 3 principle components and the 2D tour with the first 4 components I see 5 clusters or more (depending on the sampling jitter effect). So we will start with a kmeans with ( centers.

2.11 kmeans clustering with 5

I use here the whole waveform (for the kmeans) but nothing changes if I use evts.pc$x[,1:4] as I usually do:

nbc=5
set.seed(20110928,kind="Mersenne-Twister")
km = kmeans(t(evts[,goodEvts]),centers=nbc,iter.max=100,nstart=100)
label = km$cluster
cluster.med = sapply(1:nbc, function(cIdx) median(evts[,goodEvts][,label==cIdx]))
sizeC = sapply(1:nbc,function(cIdx) sum(abs(cluster.med[,cIdx])))
newOrder = sort.int(sizeC,decreasing=TRUE,index.return=TRUE)$ix
cluster.mad = sapply(1:nbc, function(cIdx) {ce = t(evts[,goodEvts]);ce = ce[label==cIdx,];apply(ce,2,mad)})
cluster.med = cluster.med[,newOrder]
cluster.mad = cluster.mad[,newOrder]
labelb = sapply(1:nbc, function(idx) (1:nbc)[newOrder==idx])[label]

We write a new csv file with the data and the labels:

write.csv(cbind(evts.pc$x[,1:4],labelb),file="tetC_sorted.csv")

It gives almost what was expected (one splitting ).

We get a plot showing the events attributed to each of the 5 units with:

layout(matrix(1:nbc,nr=nbc))
par(mar=c(1,1,1,1))
for (i in (1:nbc)) plot(evts[,goodEvts][,labelb==i],y.bar=5)

Figure 5: The events of five clusters of tetrode C

The events of clusters 2 and 3 look very similar (if one abstracts from the few events from cluster 1 wrongly attributed to 2), so I fuse them:

nbc=4
labelb[labelb==3]=2
labelb[labelb==4]=3
labelb[labelb==5]=4

2.12 Long cuts creation

For the peeling process we need templates that start and end at 0 (we will otherwise generate artifacts when we subtract). We proceed "as usual" with (I tried first with the default value for parameters before and after but I reduced their values after looking at the centers, see the next figure):

c_before = 49
c_after = 80
centers = lapply(1:nbc, function(i)
    mk_center_list(sp0[goodEvts][labelb==i],lD,
                   before=c_before,after=c_after))
names(centers) = paste("Cluster",1:nbc)

We then make sure that our cuts are long enough by looking at them:

layout(matrix(1:nbc,nr=nbc))
par(mar=c(1,4,1,1))
the_range=c(min(sapply(centers,function(l) min(l$center))),
            max(sapply(centers,function(l) max(l$center))))
for (i in 1:nbc) {
    template = centers[[i]]$center
    plot(template,lwd=2,col=2,
         ylim=the_range,type="l",ylab="")
    abline(h=0,col="grey50")
    abline(v=(1:2)*(c_before+c_after)+1,col="grey50")
    lines(filter(template,rep(1,filter_length)/filter_length),
          col=1,lty=3,lwd=2)
    abline(h=-threshold_factor,col="grey",lty=2,lwd=2)
    lines(centers[[i]]$centerD,lwd=2,col=4)
}

Figure 6: The four templates (red) together with their first derivative (blue) all with the same scale. The dashed black curve show the templates filtered with the filter used during spike detection and the horizontal dashed grey line shows the detection threshold.

Units 1 and 2 should be reliably detected, we should miss some events from clusters 3 and 4.

2.13 Peeling

We can now do the peeling.

2.13.1 Round 0

We classify, predict, subtract and check how many non-classified events we get:

round0 = lapply(as.vector(sp0),classify_and_align_evt,
                data=lD,centers=centers,
                before=c_before,after=c_after)
pred0 = predict_data(round0,centers,data_length = dim(lD)[1])
lD_1 = lD - pred0
sum(sapply(round0, function(l) l[[1]] == '?'))

We can see the difference before / after peeling for the data between 0.9 and 1.0 s:

ii = 1:1500 + 0.9*15000
tt = ii/15000
par(mar=c(1,1,1,1))
plot(tt, lD[ii,1], axes = FALSE,
     type="l",ylim=c(-50,10),
     xlab="",ylab="")
lines(tt, lD_1[ii,1], col='red')
lines(tt, lD[ii,2]-15, col='black')
lines(tt, lD_1[ii,2]-15, col='red')
lines(tt, lD[ii,3]-25, col='black')
lines(tt, lD_1[ii,3]-25, col='red')
lines(tt, lD[ii,4]-40, col='black')
lines(tt, lD_1[ii,4]-40, col='red')

Figure 7: The first peeling illustrated on 100 ms of data, the raw data are in black and the first subtration in red.

2.13.2 Round 1

We keep going, using the subtracted data lD_1 as "raw data", detecting on all sites using the original MAD for normalization:

lDf = -lD_1
lDf = filter(lDf,rep(1,filter_length)/filter_length)
lDf[is.na(lDf)] = 0
lDf = t(t(lDf)/lDf_mad_original)
thrs = threshold_factor*c(1,1,1,1)
bellow.thrs = t(t(lDf) < thrs)
lDfr = lDf
lDfr[bellow.thrs] = 0
remove(lDf)
sp1 = peaks(apply(lDfr,1,sum),15)
remove(lDfr)
sp1

eventsPos object with indexes of 132 events. 
  Mean inter event interval: 6705.77 sampling points, corresponding SD: 8439.31 sampling points 
  Smallest and largest inter event intervals: 26 and 42503 sampling points.

We classify, predict, subtract and check how many non-classified events we get:

round1 = lapply(as.vector(sp1),classify_and_align_evt,
                data=lD_1,centers=centers,
                before=c_before,after=c_after)
pred1 = predict_data(round1,centers,data_length = dim(lD)[1])
lD_2 = lD_1 - pred1
sum(sapply(round1, function(l) l[[1]] == '?'))

We look at what's left with (not shown):

explore(sp1,lD_2,col=c("black","grey50"))

That looks fine and we stop here.

2.14 Getting the spike trains

round_all = c(round0,round1)
spike_trains = lapply(paste("Cluster",1:nbc),
                      function(cn) sort(sapply(round_all[sapply(round_all,
                                                           function(l) l[[1]]==cn)],
                                          function(l) l[[2]]+l[[3]])))
names(spike_trains) = paste("Cluster",1:nbc)

2.15 Getting the inter spike intervals and the forward and backward recurrence times

2.15.1 ISI distributions

We first get the ISI (inter spike intervals) of each unit:

isi = sapply(spike_trains, diff)
names(isi) = names(spike_trains)

We get the ISI ECDF for the five units with:

layout(matrix(1:(nbc+nbc %% 2),nr=ceiling(nbc/2)))
par(mar=c(4,5,6,1))
for (cn in names(isi)) plot_isi(isi[[cn]],main=cn)

Figure 8: ISI ECDF for the four units.

2.15.2 Forward and Backward Recurrence Times

The forward recurrence time (FRT) between neuron A and B is the elapsed time between a spike in A and the next spike in B. The backward recurrence time (BRT) is the same thing except that we look for the former spike in B. If A and B are not correlated, the expected density of the FRT is the survival function (1-CDF) of the ISI from B divided by the mean ISI of B (the same holds for the BRT under the null hypothesis after taking the opposite). All that is correct if the data are stationary.

On the data at hand that gives:

layout_matrix = matrix(0,nr=nbc,nc=nbc)
counter = 1
for (i in 1:nbc)
    for (j in 1:nbc)
        if (i != j) {
            layout_matrix[i,j] = counter
            counter = counter +1
        }
layout(layout_matrix)
par(mar=c(4,3,4,1))
for (i in 1:nbc)
    for (j in 1:nbc)
        if (i != j)
            test_rt(spike_trains[[i]],
                    spike_trains[[j]],
                    ylab="",main=paste("Units",i,"and",j))

Figure 9: Graphical tests of the Backward and Forward Reccurrence Times distrution agaisnt the null hypothesis (no interaction). If the null is correct, the curves should be IID draws from a standard normal distribution.

2.16 Testing `all_at_once`

We test the function with:

## We need again an un-normalized version of the data
ref_data = rbind(cbind(h5read("locust20000616.hdf5", "/Spontaneous first/ch09"),
                       h5read("locust20000616.hdf5", "/Spontaneous first/ch11"),
                       h5read("locust20000616.hdf5", "/Spontaneous first/ch13"),
                       h5read("locust20000616.hdf5", "/Spontaneous first/ch16")))
## We can now use our function
aao=all_at_once(data=ref_data, centers, thres=threshold_factor*c(1,1,1,1), 
                filter_length_1=filter_length, filter_length=filter_length, 
                minimalDist_1=15, minimalDist=15, 
                before=c_before, after=c_after, 
                detection_cycle=c(0,0), verbose=2)

The five number summary is:
       V1             V2             V3             V4      
 Min.   :1432   Min.   :1646   Min.   :1518   Min.   :1630  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2086   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2508   Max.   :2362   Max.   :2398   Max.   :2388  

Doing now round 0 detecting on all sites
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
     1443       284       411       285       440        23 

Doing now round 1 detecting on all sites
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      132         3         0         9        94        26 

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
     1564       287       411       294       534        38

We see that we are getting back the numbers we obtained before step by step.

We can compare the "old" and "new" centers with (not shown):

layout(matrix(1:nbc,nr=nbc))
par(mar=c(1,4,1,1))
for (i in 1:nbc) {
    plot(centers[[i]]$center,lwd=2,col=2,
         ylim=the_range,type="l")
    abline(h=0,col="grey50")
    abline(v=(c_before+c_after)+1,col="grey50")
    lines(aao$centers[[i]]$center,lwd=1,col=4)
}

They are not exactly identical since the new version is computed with all events (superposed or not) attributed to each neuron.

3 Analyzing a sequence of trials

3.1 Create directories were results get saved

We will carry out an analysis of sequences of many trials with a given odor. At the end of the analysis of the sequence we will save some intermediate R object in a directory we are now creating.:

if (!dir.exists("tetC_analysis"))
    dir.create("tetC_analysis")

We will moreover save the individual spike trains in text format in a directory:

if (!dir.exists("locust20000616_spike_trains"))
    dir.create("locust20000616_spike_trains")

3.2 Define a "taylored" version of `sort_many_trials`

In order to save space and to avoid typos, we define next a taylored version of sort_many_trials:

smt = function(stim_name,
               trial_nbs,
               centers,
               counts) {
    sort_many_trials(inter_trial_time=20*15000,
                     get_data_fct=function(i,s)
                         get_data(i,s,
                                  channels = c("ch09","ch11","ch13","ch16"),
                                  file="locust20000616.hdf5"),
                     stim_name=stim_name,
                     trial_nbs=trial_nbs,
                     centers=centers,
                     counts=counts,
                     all_at_once_call_list=list(thres=threshold_factor*c(1,1,1,1), 
                                                filter_length_1=filter_length, filter_length=filter_length, 
                                                minimalDist_1=15, minimalDist=15, 
                                                before=c_before, after=c_after, 
                                                detection_cycle=c(0,0), verbose=1),
                     layout_matrix=matrix(c(1,1:5),nr=3),new_weight_in_update=0.01
                     )
}

4 Systematic analysis of the 50 trials with "pure" `Cis-3-Hexen-1-ol`

4.1 Doing the job

a_Cis_3_Hexen_1_ol_0_tetC=smt(stim_name="Cis-3-Hexen-1-ol (10^-0)",
                              trial_nbs=1:50,
                              centers=aao$centers,
                              counts=aao$counts)

***************
Doing now trial 1 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1391   Min.   :1600   Min.   :1602   Min.   :1663  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2086   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2567   Max.   :2391   Max.   :2442   Max.   :2391  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      515        69       132       112       182        20 
Trial 1 done!
******************
***************
Doing now trial 2 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1470   Min.   :1666   Min.   :1604   Min.   :1664  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2086   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2461   Max.   :2347   Max.   :2374   Max.   :2360  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      467        64       102       110       170        21 
Trial 2 done!
******************
***************
Doing now trial 3 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1361   Min.   :1672   Min.   :1644   Min.   :1650  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2049  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2086   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2435   Max.   :2369   Max.   :2355   Max.   :2392  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      472       107        96        93       166        10 
Trial 3 done!
******************
***************
Doing now trial 4 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1480   Min.   :1610   Min.   :1550   Min.   :1625  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2051   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2086   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2493   Max.   :2372   Max.   :2371   Max.   :2382  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      470        85       112        92       153        28 
Trial 4 done!
******************
***************
Doing now trial 5 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1476   Min.   :1685   Min.   :1663   Min.   :1636  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2086   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2505   Max.   :2350   Max.   :2349   Max.   :2373  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      464        70       130       103       136        25 
Trial 5 done!
******************
***************
Doing now trial 6 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1486   Min.   :1659   Min.   :1614   Min.   :1655  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2560   Max.   :2374   Max.   :2455   Max.   :2399  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      430        60       133        87       136        14 
Trial 6 done!
******************
***************
Doing now trial 7 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1467   Min.   :1687   Min.   :1646   Min.   :1659  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2086   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2468   Max.   :2358   Max.   :2361   Max.   :2374  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      470       111        98        84       159        18 
Trial 7 done!
******************
***************
Doing now trial 8 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1507   Min.   :1654   Min.   :1675   Min.   :1633  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2086   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2465   Max.   :2393   Max.   :2381   Max.   :2368  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      488        66       131       105       157        29 
Trial 8 done!
******************
***************
Doing now trial 9 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1503   Min.   :1607   Min.   :1649   Min.   :1647  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2531   Max.   :2394   Max.   :2363   Max.   :2364  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      467        63       118       123       144        19 
Trial 9 done!
******************
***************
Doing now trial 10 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1460   Min.   :1687   Min.   :1649   Min.   :1607  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2500   Max.   :2374   Max.   :2379   Max.   :2401  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      398        69        76       109       125        19 
Trial 10 done!
******************
***************
Doing now trial 11 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1505   Min.   :1669   Min.   :1661   Min.   :1640  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2459   Max.   :2365   Max.   :2360   Max.   :2382  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      407        90        72       110       115        20 
Trial 11 done!
******************
***************
Doing now trial 12 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1490   Min.   :1702   Min.   :1661   Min.   :1651  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2550   Max.   :2410   Max.   :2354   Max.   :2379  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      410        72        89        97       126        26 
Trial 12 done!
******************
***************
Doing now trial 13 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1501   Min.   :1529   Min.   :1640   Min.   :1638  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2430   Max.   :2386   Max.   :2413   Max.   :2404  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      436        80        67       118       154        17 
Trial 13 done!
******************
***************
Doing now trial 14 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1496   Min.   :1642   Min.   :1623   Min.   :1623  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2472   Max.   :2360   Max.   :2347   Max.   :2425  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      410        57        95       105       128        25 
Trial 14 done!
******************
***************
Doing now trial 15 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1480   Min.   :1571   Min.   :1656   Min.   :1631  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2459   Max.   :2432   Max.   :2385   Max.   :2411  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      465        85       122       110       123        25 
Trial 15 done!
******************
***************
Doing now trial 16 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1493   Min.   :1620   Min.   :1649   Min.   :1682  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2431   Max.   :2356   Max.   :2349   Max.   :2383  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      456        80       107       110       137        22 
Trial 16 done!
******************
***************
Doing now trial 17 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1444   Min.   :1692   Min.   :1673   Min.   :1595  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2523   Max.   :2337   Max.   :2342   Max.   :2396  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      442        77       119        92       129        25 
Trial 17 done!
******************
***************
Doing now trial 18 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1460   Min.   :1616   Min.   :1585   Min.   :1596  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2439   Max.   :2346   Max.   :2394   Max.   :2381  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      447        74       108       117       115        33 
Trial 18 done!
******************
***************
Doing now trial 19 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1484   Min.   :1675   Min.   :1679   Min.   :1665  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2049  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2094  
 Max.   :2441   Max.   :2357   Max.   :2346   Max.   :2413  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      410        50       114       109       125        12 
Trial 19 done!
******************
***************
Doing now trial 20 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1483   Min.   :1687   Min.   :1692   Min.   :1629  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2051   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2449   Max.   :2380   Max.   :2370   Max.   :2393  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      437        77       121        97       118        24 
Trial 20 done!
******************
***************
Doing now trial 21 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1500   Min.   :1683   Min.   :1687   Min.   :1623  
 1st Qu.:2014   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2049  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2447   Max.   :2337   Max.   :2348   Max.   :2381  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      361        84        47        99       110        21 
Trial 21 done!
******************
***************
Doing now trial 22 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1478   Min.   :1662   Min.   :1674   Min.   :1617  
 1st Qu.:2013   1st Qu.:2015   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2051   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2086   3rd Qu.:2084   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2555   Max.   :2358   Max.   :2358   Max.   :2410  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      496        69       163       116       119        29 
Trial 22 done!
******************
***************
Doing now trial 23 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1510   Min.   :1628   Min.   :1659   Min.   :1557  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2459   Max.   :2359   Max.   :2349   Max.   :2393  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      418        67       112       110       111        18 
Trial 23 done!
******************
***************
Doing now trial 24 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1431   Min.   :1620   Min.   :1527   Min.   :1542  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2485   Max.   :2363   Max.   :2360   Max.   :2415  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      472        84       102       132       125        29 
Trial 24 done!
******************
***************
Doing now trial 25 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1515   Min.   :1553   Min.   :1651   Min.   :1585  
 1st Qu.:2014   1st Qu.:2016   1st Qu.:2013   1st Qu.:2004  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2094  
 Max.   :2439   Max.   :2356   Max.   :2346   Max.   :2390  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      387        74        58       113       117        25 
Trial 25 done!
******************
***************
Doing now trial 26 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1519   Min.   :1681   Min.   :1704   Min.   :1618  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2458   Max.   :2436   Max.   :2352   Max.   :2401  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      434        63       105       121       120        25 
Trial 26 done!
******************
***************
Doing now trial 27 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1393   Min.   :1683   Min.   :1660   Min.   :1622  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2490   Max.   :2338   Max.   :2365   Max.   :2410  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      469        86        76       121       152        34 
Trial 27 done!
******************
***************
Doing now trial 28 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1522   Min.   :1619   Min.   :1691   Min.   :1588  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2500   Max.   :2351   Max.   :2377   Max.   :2411  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      470        51       113       118       164        24 
Trial 28 done!
******************
***************
Doing now trial 29 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1413   Min.   :1605   Min.   :1656   Min.   :1556  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2514   Max.   :2397   Max.   :2415   Max.   :2418  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      450        82        92       107       150        19 
Trial 29 done!
******************
***************
Doing now trial 30 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1283   Min.   :1579   Min.   :1602   Min.   :1527  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2583   Max.   :2350   Max.   :2357   Max.   :2516  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      442        64       108       109       127        34 
Trial 30 done!
******************
***************
Doing now trial 31 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1486   Min.   :1589   Min.   :1636   Min.   :1546  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2484   Max.   :2349   Max.   :2373   Max.   :2444  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      442        48       138       105       126        25 
Trial 31 done!
******************
***************
Doing now trial 32 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1336   Min.   :1591   Min.   :1617   Min.   :1532  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2087   3rd Qu.:2095  
 Max.   :2492   Max.   :2395   Max.   :2357   Max.   :2425  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      428        74       131       101        98        24 
Trial 32 done!
******************
***************
Doing now trial 33 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1515   Min.   :1647   Min.   :1674   Min.   :1615  
 1st Qu.:2014   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2455   Max.   :2341   Max.   :2349   Max.   :2378  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      418        58       121        92       122        25 
Trial 33 done!
******************
***************
Doing now trial 34 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1445   Min.   :1558   Min.   :1598   Min.   :1567  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2581   Max.   :2428   Max.   :2365   Max.   :2408  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      433        68       122       117       106        20 
Trial 34 done!
******************
***************
Doing now trial 35 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1506   Min.   :1614   Min.   :1678   Min.   :1571  
 1st Qu.:2014   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2094  
 Max.   :2460   Max.   :2372   Max.   :2370   Max.   :2423  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      417        73        80       117       117        30 
Trial 35 done!
******************
***************
Doing now trial 36 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1521   Min.   :1682   Min.   :1692   Min.   :1662  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2004  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2094  
 Max.   :2495   Max.   :2332   Max.   :2355   Max.   :2453  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      391        64        71       117       114        25 
Trial 36 done!
******************
***************
Doing now trial 37 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1468   Min.   :1640   Min.   :1605   Min.   :1638  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2486   Max.   :2355   Max.   :2376   Max.   :2402  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      439        63       117       120       115        24 
Trial 37 done!
******************
***************
Doing now trial 38 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1478   Min.   :1677   Min.   :1712   Min.   :1649  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2488   Max.   :2355   Max.   :2396   Max.   :2430  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      432        66       112       111       114        29 
Trial 38 done!
******************
***************
Doing now trial 39 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1489   Min.   :1636   Min.   :1657   Min.   :1632  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2465   Max.   :2347   Max.   :2352   Max.   :2427  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      369        80        83        81       100        25 
Trial 39 done!
******************
***************
Doing now trial 40 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1500   Min.   :1635   Min.   :1679   Min.   :1604  
 1st Qu.:2014   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2433   Max.   :2348   Max.   :2352   Max.   :2395  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      432        72        93       115       131        21 
Trial 40 done!
******************
***************
Doing now trial 41 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1507   Min.   :1704   Min.   :1692   Min.   :1631  
 1st Qu.:2014   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2508   Max.   :2335   Max.   :2356   Max.   :2401  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      395        64       109       100        99        23 
Trial 41 done!
******************
***************
Doing now trial 42 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1466   Min.   :1615   Min.   :1701   Min.   :1592  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2494   Max.   :2401   Max.   :2337   Max.   :2407  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      424        89       109        92       113        21 
Trial 42 done!
******************
***************
Doing now trial 43 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1496   Min.   :1675   Min.   :1639   Min.   :1629  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2470   Max.   :2331   Max.   :2378   Max.   :2463  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      402        91        97        75       119        20 
Trial 43 done!
******************
***************
Doing now trial 44 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1400   Min.   :1617   Min.   :1646   Min.   :1505  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2507   Max.   :2367   Max.   :2337   Max.   :2405  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      421        86       116        92       108        19 
Trial 44 done!
******************
***************
Doing now trial 45 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1493   Min.   :1698   Min.   :1660   Min.   :1642  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2451   Max.   :2357   Max.   :2337   Max.   :2413  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      395        75        67       110       117        26 
Trial 45 done!
******************
***************
Doing now trial 46 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1493   Min.   :1681   Min.   :1660   Min.   :1610  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2004  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2094  
 Max.   :2449   Max.   :2369   Max.   :2345   Max.   :2452  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      368        84        78        59       121        26 
Trial 46 done!
******************
***************
Doing now trial 47 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1441   Min.   :1653   Min.   :1653   Min.   :1503  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2505   Max.   :2396   Max.   :2388   Max.   :2437  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      414        86        71       104       130        23 
Trial 47 done!
******************
***************
Doing now trial 48 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1495   Min.   :1651   Min.   :1683   Min.   :1638  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2004  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2094  
 Max.   :2457   Max.   :2339   Max.   :2346   Max.   :2399  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      331        71        59        92        93        16 
Trial 48 done!
******************
***************
Doing now trial 49 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1464   Min.   :1692   Min.   :1679   Min.   :1606  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2004  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2094  
 Max.   :2459   Max.   :2366   Max.   :2357   Max.   :2397  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      371        65        72       103       103        28 
Trial 49 done!
******************
***************
Doing now trial 50 of Cis-3-Hexen-1-ol (10^-0)
The five number summary is:
      ch09           ch11           ch13           ch16     
 Min.   :1504   Min.   :1677   Min.   :1658   Min.   :1609  
 1st Qu.:2013   1st Qu.:2016   1st Qu.:2013   1st Qu.:2003  
 Median :2050   Median :2050   Median :2050   Median :2050  
 Mean   :2049   Mean   :2049   Mean   :2049   Mean   :2049  
 3rd Qu.:2085   3rd Qu.:2083   3rd Qu.:2086   3rd Qu.:2095  
 Max.   :2463   Max.   :2358   Max.   :2358   Max.   :2398  

Global counts at classification's end:
    Total Cluster 1 Cluster 2 Cluster 3 Cluster 4         ? 
      412        64       104        94       128        22 
Trial 50 done!
******************

4.2 Diagnostic plots

The counts evolution is:

counts_evolution(a_Cis_3_Hexen_1_ol_0_tetC)

Figure 10: Evolution of the number of events attributed to each unit (1 to 4) or unclassified ("?") during the 50 trials with "pure" Cis_3_Hexen_1_ol for tetrode C.

The waveform evolution is:

waveform_evolution(a_Cis_3_Hexen_1_ol_0_tetC,threshold_factor)

Figure 11: Evolution of the templates of each unit during the 50 trials with "pure" Cis_3_Hexen_1_ol for tetrode C.

The observed counting processes, inter spike intervals densities and raster plots are:

cp_isi_raster(a_Cis_3_Hexen_1_ol_0_tetC)

Figure 12: Observed counting processes, empirical inter spike interval distributions and raster plots for "pure" Cis_3_Hexen_1_ol.

4.3 Save results

Before analyzing the next set of trials we save the output of sort_many_trials to disk with:

save(a_Cis_3_Hexen_1_ol_0_tetC,
     file=paste0("tetC_analysis/tetC_","Cis_3_Hexen_1_ol_0","_summary_obj.rda"))

We write to disk the spike trains in text mode:

for (c_idx in 1:length(a_Cis_3_Hexen_1_ol_0_tetC$spike_trains))
    cat(a_Cis_3_Hexen_1_ol_0_tetC$spike_trains[[c_idx]],
        file=paste0("locust20000616_spike_trains/locust20000616_Cis_3_Hexen_1_ol_0_tetC_u",c_idx,".txt"),sep="\n")

5 The second minute of spontaneous activity recording

5.1 Another taylored `sort_many_trials`

smt2 = function(stim_name,
                centers,
                counts) {
    sort_many_trials(inter_trial_time=60*15000,
                     get_data_fct=function(i,s) {
                         prefix=paste0("/",s, "/")
                         rbind(cbind(h5read("locust20000616.hdf5",paste0(prefix,"ch09")),
                                     h5read("locust20000616.hdf5",paste0(prefix,"ch11")),
                                     h5read("locust20000616.hdf5",paste0(prefix,"ch13")),
                                     h5read("locust20000616.hdf5",paste0(prefix,"ch16"))))
                     },
                     stim_name=stim_name,
                     trial_nbs=1,
                     centers=centers,
                     counts=counts,
                     all_at_once_call_list=list(thres=threshold_factor*c(1,1,1,1), 
                                                filter_length_1=filter_length, filter_length=filter_length, 
                                                minimalDist_1=15, minimalDist=15, 
                                                before=c_before, after=c_after, 
                                                detection_cycle=c(0,0), verbose=1),
                     layout_matrix=matrix(c(1,1:5),nr=3),new_weight_in_update=0.01
                     )
}

5.2 Do the job

a_Spontaenous_2_tetC=smt2(stim_name="Spontaneous second",
                          centers=a_Cis_3_Hexen_1_ol_0_tetC$centers,
                          counts=a_Cis_3_Hexen_1_ol_0_tetC$counts)

5.3 Save results

Before analyzing the next set of trials we save the output of sort_many_trials to disk with:

save(a_Spontaenous_2_tetC,
     file=paste0("tetC_analysis/tetC_","Spontaneous_2","_summary_obj.rda"))

We write to disk the spike trains in text mode:

for (c_idx in 1:length(a_Spontaenous_2_tetC$spike_trains))
    cat(a_Spontaenous_2_tetC$spike_trains[[c_idx]],
        file=paste0("locust20000616_spike_trains/locust20000616_Spontaneous_2_tetC_u",c_idx,".txt"),sep="\n")

6 The third minute of spontaneous activity recording

6.1 Do the job

a_Spontaneous_3_tetC=smt2(stim_name="Spontaneous third",
                          centers=a_Spontaenous_2_tetC$centers,
                          counts=a_Spontaenous_2_tetC$counts)

6.2 Save results

Before analyzing the next set of trials we save the output of sort_many_trials to disk with:

save(a_Spontaneous_3_tetC,
     file=paste0("tetC_analysis/tetC_","Spontaneous_3","_summary_obj.rda"))

We write to disk the spike trains in text mode:

for (c_idx in 1:length(a_Spontaneous_3_tetC$spike_trains))
    cat(a_Spontaneous_3_tetC$spike_trains[[c_idx]],
        file=paste0("locust20000616_spike_trains/locust20000616_Spontaneous_3_tetC_u",c_idx,".txt"),sep="\n")

7 30 remaining trials with `Cis-3-Hexen-1-ol` diluted 100 times

As mentioned in the LabBook, there are only 30 remaining trials among 50.

7.1 Do the job

We only write the command in the html file, not its output, the diagnostic plots in the next subsection should be enough to judge if everything went fine or not.

a_Cis_3_Hexen_1_ol_2_tetC=smt(stim_name="Cis-3-Hexen-1-ol (10^-2)",
                              trial_nbs=1:30,
                              centers=a_Spontaneous_3_tetC$centers,
                              counts=a_Spontaneous_3_tetC$counts)

7.2 Diagnostic plots

The counts evolution is:

counts_evolution(a_Cis_3_Hexen_1_ol_2_tetC)

Figure 13: Evolution of the number of events attributed to each unit (1 to 4) or unclassified ("?") during the 30 remaining trials Cis_3_Hexen_1_ol diluted 100 times for tetrode C.

The waveform evolution is:

waveform_evolution(a_Cis_3_Hexen_1_ol_2_tetC,threshold_factor)

Figure 14: Evolution of the templates of each unit during the 30 remaining trials Cis_3_Hexen_1_ol diluted 100 times for tetrode C.

The observed counting processes, inter spike intervals densities and raster plots are:

cp_isi_raster(a_Cis_3_Hexen_1_ol_2_tetC)

Figure 15: Observed counting processes, empirical inter spike interval distributions and raster plots for the 30 remaining trials Cis_3_Hexen_1_ol diluted 100 times for tetrode C.

7.3 Save results

Before analyzing the next set of trials we save the output of sort_many_trials to disk with:

save(a_Cis_3_Hexen_1_ol_2_tetC,
     file=paste0("tetC_analysis/tetC_","Cis_3_Hexen_1_ol_2","_summary_obj.rda"))

We write to disk the spike trains in text mode:

for (c_idx in 1:length(a_Cis_3_Hexen_1_ol_2_tetC$spike_trains))
    cat(a_Cis_3_Hexen_1_ol_2_tetC$spike_trains[[c_idx]],
        file=paste0("locust20000616_spike_trains/locust20000616_Cis_3_Hexen_1_ol_2_tetC_u",c_idx,".txt"),sep="\n")

8 50 trials with `Cis-3-hexen-1-ol` diluted 10 times

8.1 Do the job

stim_name = "Cis-3-Hexen-1-ol (10^-1)"
a_Cis_3_Hexen_1_ol_1_tetC=smt(stim_name=stim_name,
                              trial_nbs=1:50,
                              centers=a_Cis_3_Hexen_1_ol_2_tetC$centers,
                              counts=a_Cis_3_Hexen_1_ol_2_tetC$counts)

8.2 Diagnostic plots

The counts evolution is:

Figure 16: Evolution of the number of events attributed to each unit (1 to 3) or unclassified ("?") during the 50 trials of Cis-3-Hexen-1-ol diluted 10 times for tetrode C.

The waveform evolution is:

Figure 17: Evolution of the templates of each unit during the 50 trials of Cis-3-Hexen-1-ol diluted 10 times for tetrode C.

The observed counting processes, inter spike intervals densities and raster plots are:

Figure 18: Observed counting processes, empirical inter spike interval distributions and raster plots during the 60 trials of Cis_3_Hexen_1_ol_1 for tetrodeD.

8.3 Save results

Before analyzing the next set of trials we save the output of sort_many_trials to disk with:

save(a_Cis_3_Hexen_1_ol_1_tetC,
     file=paste0("tetC_analysis/tetC_",stim_name,"_summary_obj.rda"))

We write to disk the spike trains in text mode:

for (c_idx in 1:length(a_Cis_3_Hexen_1_ol_1_tetC$spike_trains))
    if (!is.null(a_Cis_3_Hexen_1_ol_1_tetC$spike_trains[[c_idx]]))
        cat(a_Cis_3_Hexen_1_ol_1_tetC$spike_trains[[c_idx]],
            file=paste0("locust20000616_spike_trains/locust20000616_Cis_3_Hexen_1_ol_1_tetC_u",c_idx,".txt"),
            sep="\n")

Sorting data set 20000616 tetrode C (channels 9, 11, 13, 16)

Table of Contents

1 Introduction

1.1 Getting the data

1.2 Getting the code

2 Tetrode D (channels 9, 11, 13, 16) analysis

2.1 Loading the data

2.2 Five number summary

2.3 Plot the data

2.4 Data normalization

2.5 Spike detection

2.6 Cuts

2.7 Events

2.8 Removing obvious superposition

2.9 Dimension reduction

2.10 Exporting for GGobi

2.11 kmeans clustering with 5

2.12 Long cuts creation

2.13 Peeling

2.13.1 Round 0

2.13.2 Round 1

2.14 Getting the spike trains

2.15 Getting the inter spike intervals and the forward and backward recurrence times

2.15.1 ISI distributions

2.15.2 Forward and Backward Recurrence Times

2.16 Testing all_at_once

3 Analyzing a sequence of trials

3.1 Create directories were results get saved

3.2 Define a "taylored" version of sort_many_trials

4 Systematic analysis of the 50 trials with "pure" Cis-3-Hexen-1-ol

4.1 Doing the job

4.2 Diagnostic plots

4.3 Save results

5 The second minute of spontaneous activity recording

5.1 Another taylored sort_many_trials

5.2 Do the job

5.3 Save results

6 The third minute of spontaneous activity recording

6.1 Do the job

6.2 Save results

7 30 remaining trials with Cis-3-Hexen-1-ol diluted 100 times

7.1 Do the job

7.2 Diagnostic plots

7.3 Save results

8 50 trials with Cis-3-hexen-1-ol diluted 10 times

8.1 Do the job

8.2 Diagnostic plots

8.3 Save results

2.10 Exporting for `GGobi`

2.16 Testing `all_at_once`

3.2 Define a "taylored" version of `sort_many_trials`

4 Systematic analysis of the 50 trials with "pure" `Cis-3-Hexen-1-ol`

5.1 Another taylored `sort_many_trials`

7 30 remaining trials with `Cis-3-Hexen-1-ol` diluted 100 times

8 50 trials with `Cis-3-hexen-1-ol` diluted 10 times