## Sunday, October 16, 2022

### Tutorial: analysis of multiwavelength fast kinetics data

The purpose of this post is to demonstrate a first approach to the analysis of multiwavelength kinetic data, like those obtained using stopped-flow data. To practice, we will use data that were acquired during the stopped flow practicals of the MetBio summer school from the FrenchBIC. During the practicals, the student monitored the reaction of myoglobin (in its Fe(III) state) with azide, which yields a fast and strong change in the absorbance spectrum of the protein, which was monitored using a diode array. The data is publicly available on zenodo.

#### Aims of this tutorial

The purpose of this tutorial is to teach you to use the free softwareQSoas to run a simple, multiwavelength exponential fit on the data, and to look at the results. This is not a kinetics lecture, so that it will not go in depth about the use of the exponential fit and its meaning.

First, make sure you have a working version of QSoas, you can download them (for free) there. Then download the data files from zenodo. We will work only on the data file Azide-1.25mm_001.dat, but of course, the purpose of this tutorial is to enable you to work on all of them. The data files contain the time evolution of the absorbance for all wavelengths, in a matrix format, in which each row correpond to a time point and each column to a wavelength.

Start QSoas, and launch the command:

QSoas> load /comments='"'

Then, choose the Azide-1.25mm_001.dat data file. This should bring up a horizontal red line at the bottom of the data display, with X values between about 0 and 2.5. If you zoom on the red line with the mouse wheel, you'll realize it is data. The /comments='"' part is very important since it allows the extraction of the wavelength from the data. We will look at what it means another day. At this stage, you can look at the loaded data using the command:
QSoas> edit

You should have a window looking like this:
The rows each correspond to a data point displayed on the window below. The first column correspond to the X values, the second the Y values, and all the other ones to extra Y columns (they are not displayed by default). What is especially interesting is the first row, which contains a nan as the X value and what is obviously the wavelength for all the Y values. To tell that QSoas should take this line as the wavelength (which will be the perpendicular coordinate, the coordinate of the other direction of the matrix), first close the edit window and run:
QSoas> set-perp /from-row=0


#### Splitting and fitting

Now, we have a single dataset containing a lot of Y columns. We want to fit all of them simultaneously with a (mono) exponential fit. For that, we first need to split the big matrix into a series of X,Y datasets (because fitting only works on the first Y). This is possible by running:
QSoas> expand /style=red-to-blue /flags=kinetics

Your screen should now look like this:
You're looking at the kinetics at all wavelengths at the same time (this may take some time to display on your computer, it is after all a rather large number of data points). The /style=red-to-blue is not strictly necessary, but it gives the red to blue color gradient which makes things easier to look at (and cooler !). The /flags=kinetics is there to attach a label (a flag) to the newly created datasets so we can easily manipulate all of them at the same time. Then it's time to fit, with the following command:
QSoas> mfit-exponential-decay flagged:kinetics

This should bring up a new window. After resizing it, you should have something that looks like this:
The bottom of the fit window is taken by the parameters, each with two checkboxes on the right to set them fixed (i.e. not determined by the fitting mechanism) and/or global (i.e. with a single value for all the datasets, here all the wavelengths). The top shows the current dataset along with the corresponding fit (in green), and, below, the residuals. You can change the dataset by clicking on the horizontal arrows or using Ctrl+PgUp or Ctrl+PgDown (keep holding it to scan fast). See the Z = 728.15 showing that QSoas has recognized that the currently displayed dataset corresponds to the wavelength 728.15. The equation fitted to the data is: $$y(x) = A_\infty + A_1 \times \exp -(x - x_0)/\tau_1$$ In this case, while the $$A_1$$ and $$A_\infty$$ parameters clearly depend on the wavelength, the time constant of evolution should be independent of wavelength (the process happens at a certain rate regardless of the wavelength we're analyzing), so that the $$\tau_1$$ parameter should be common for all the datasets/wavelengths. Just click on the global checkbox at the right of the tau_1 parameter, make sure it is checked, and hit the Fit button...

The fit should not take long (less than a minute), and then you end up with the results of the fits: all the parameters. The best way to look at the non global parameters like $$A_1$$ and $$A_\infty$$ is to use the Show Parameters item from the Parameters menu. Using it and clicking on A_inf too should give you a display like this one:

The A_inf parameter corresponds to the spectum at infinite time (of azide-bound heme), while the A_1 parameter corresponds to the difference spectrum between the initial (azide-free) and final (azide-bound) states.

Now, the fit is finished, you can save the parameters if you want to reload them in a later fit by using the Parameters/Save menu item or export them in a form more suitable for plotting using Parameters/Export (although QSoas can also display and the parameters saved using Save). This concludes this first approach to fitting the data. What you can do is

• look at the depence of the tau_1 parameter as a function of the azide concentration;
• try fitting more than one exponential, using for instance:
QSoas> mfit-exponential-decay /exponentials=2 flagged:kinetics


#### How to read the code above

All the lines starting by QSoas> in the code areas above are meant to be typed into the QSoas command line (at the bottom of the window), and started by pressing enter at the end. You must remove the QSoas> bit. The other lines (when applicable) show you the response of QSoas, in the terminal just above the command-line. You may want to play with the QSoas tutorial to learn more about how to interact with QSoas.

QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050–5052. Current version is 3.1. You can freely (and at no cost) download its source code or precompiled versions for MacOS and Windows there. Alternatively, you can clone from the GitHub repository.

## Wednesday, September 28, 2022

### Version 3.1 of QSoas is out

The new version of QSoas has just been released ! It brings in a host of new features, as the releases before, but maybe the most important change is the following...

#### Binary images now freely available !

Starting from now, all the binary images for the new versions of QSoas will freely available from the download page. You can download the precompiled versions of QSoas for MacOS or windows. So now, you have no reason anymore not to try !
My aim with making the binaries freely available is also to simplify the release process for me and therefore increase the rate at which new versions are released.

#### Improvements to the fit interface

Some work went into improving the fit interface, in particular for the handling of fit trajectories when doing parameter space exploration, for difficult fits with many parameters and many local minima. The fit window now features real menus, along with tab a way to display the terminal (see the menus and the tabs selection on the image).
Individual fits have also been improved, with, among others, the possibility to easily simulate voltammograms with the kinetic-system fits, and the handling of Marcus-Hush-Chidsey (or Marcus "distribution of states") kinetics for electron transfers.

#### Column and row names

This release greatly improves the handling of column and row names, including commands to easily modify them, the possibility to use Ruby formulas to change them, and a much better way read and write them to data files. Mastering the use of column names (and to a lesser extent, row names) can greatly simplify data handling, especially when dealing with files with a large number of columns.

#### Complex numbers

Version 3.1 brings in support for formulas handling complex numbers. Although it is not possible to store complex numbers directly into datasets, it is easy to separate them in real and imaginary parts to your liking.

#### Scripting improvement

Two important improvements for scripting are included in version 3.1. The first is the possibility to define virtual files inside a script file, which makes it easy to define subfunctions to run using commands like run-for-each. The second is the possibility to define variables to be reused later (like the script arguments) using the new command let.

There are a lot of other new features, improvements and so on, look for the full list there.

QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050–5052. Current version is 3.1. You can download its source code or precompiled versions for MacOS and Windows there. Alternatively, you can clone from the GitHub repository.

## Wednesday, July 7, 2021

### Upcoming features of QSoas and github repository

For the past years, most of the development has happened behind the scene in a private repository, and the code has appeared in the public repository only a couple of months before the release, in the release branch. I have now decided to publish the current code of QSoas in the github repository (in the public branch). This way, you can follow and use all the good things that were developed since the last release, and also verify whether any bug you have is still present in the currently developed version !

#### Upcoming features

This is the occasion to write a bit about the some of the features that have been added since the publication of the 3.0 release. Not all of them are polished nor documented yet, but here are a few teasers. The current version in github has:
• a comprehensive handling of column/row names, which makes it much easier to work with files with named columns (like the output files QSoas produces !);
• better handling of lists of meta-data, when there is one value of the meta for each segment or each Y column;
• handling of complex numbers in apply-formula;
• defining fits using external python code;
• a command for linear least squares (which has the huge advantage of being exact and not needing any initial parameters);
• commands to pause in a script or sort datasets in the stack;
• improvements over previous commands, in particular with eval;
• ... and more...
Check out the github repository if you want to know more about the new features !

As of now, no official date is planned for the 3.1 release, but this could happen during fall.

QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050–5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

## Sunday, June 13, 2021

### Solution for QSoas quiz #2: averaging several Y values for the same X value

This post describes two similar solutions to the Quiz #2, using the data files found there. The two solutions described here rely on split-on-values. The first solution is the one that came naturally to me, and is by far the most general and extensible, but the second one is shorter, and doesn't require external script files.

#### Solution #1

The key to both solution is to separate the original data into a series of datasets that only contain data at a fixed value of x (which corresponds here to a fixed pH), and then process each dataset one by one to extract the average and standard deviation. This first step is done thus:
QSoas> load kcat-vs-ph.dat
QSoas> split-on-values pH x /flags=data

After these commands, the stacks contains a series of datasets bearing the data flag, that each contain a single column of data, as can be seen from the beginnings of a show-stack command:
QSoas> k
Normal stack:
F  C	Rows	Segs	Name
#0	(*) 1	43	1	'kcat-vs-ph_subset_22.dat'
#1	(*) 1	44	1	'kcat-vs-ph_subset_21.dat'
#2	(*) 1	43	1	'kcat-vs-ph_subset_20.dat'
...

Each of these datasets have a meta-data named pH whose value is the original x value from kcat-vs-ph.dat. Now, the idea is to run a stats command on the resulting datasets, extracting the average value of x and its standard deviation, together with the value of the meta pH. The most natural and general way to do this is to use run-for-datasets, using the following script file (named process-one.cmds):
stats /meta=pH /output=true /stats=x_average,x_stddev

So the command looks like:
QSoas> run-for-datasets process-one.cmds flagged:data

This command produces an output file containing, for each flagged dataset, a line containing x_average, x_stddev, and pH. Then, it is just a matter of loading the output file and shuffling the columns in the right order to get the data in the form asked. Overall, this looks like this:
l kcat-vs-ph.dat
split-on-values pH x /flags=data
output result.dat /overwrite=true
run-for-datasets process-one.cmds flagged:data
l result.dat
apply-formula tmp=y2;y2=y;y=x;x=tmp
dataset-options /yerrors=y2

The slight improvement over what is described above is the use of the output command to write the output to a dedicated file (here result.dat), instead of out.dat and ensuring it is overwritten, so that no data remains from previous runs.

#### Solution #2

The second solution is almost the same as the first one, with two improvements:
This yields the following, smaller, solution:
l kcat-vs-ph.dat
split-on-values pH x /flags=data
stats /meta=pH /accumulate=* /stats=x_average,x_stddev /buffers=flagged:data
pop
apply-formula tmp=y2;y2=y;y=x;x=tmp
dataset-options /yerrors=y2


QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050–5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

## Sunday, May 30, 2021

### QSoas quiz #2: averaging several Y values for the same X value

This second quiz may sound like the first one, but in fact, the approach used is completely different. The point is to gather some elementary statistics from a series of experiments performed under different conditions, but with several repeats at the same conditions.

#### Quiz

You are given a file (which you can download there) that contains a series of pH value data: the X column is the pH, the Y column the result of the experiment at the given pH (let's say the measure of the catalytic rate of an enzyme). Your task is to take this data and produce a single dataset which contains, for each pH value, the pH, the average of the results at that pH and the standard deviation. The result should be identical to the following file, and should look like that:
There are several ways to do this, but all ways must rely on stats, and the more natural way in QSoas is to take advantage of split-on-values, which is a very powerful command but somehow hard to master, which is the point of this Quiz.
By the way, the data file is purely synthetic, if you look in the GitHub repository, you'll see how it was generated.

QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050–5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

## Sunday, May 16, 2021

### Tutorial: analyze redox inactivations/reactivations

Redox-dependent inactivations are actually rather common in the field of metalloenzymes, and electrochemistry can be an extremely powerful tool to study them, providing one can analyze the data quantitatively. The point of this point is to teach the reader how to do so using QSoas. For more general information about redox inactivations and how to study them using electrochemical techniques, the reader is invited to read the review del Barrio and Fourmond, ChemElectroChem 2019.

This post is a tutorial to learn the analysis of data coming from the study of the redox-dependent substrate inhibition of periplasmic nitrate reductase NapAB, which has the advantage of being relatively simple. The whole processed is discussed in Jacques et al, BBA, 2014. What you need to know in order to follow this tutorial is the following:

• the whole inactivation/reactivation process can be modelled by a simple reversible reaction: $$\mathrm{A} \rightleftharpoons \mathrm{I}$$ A is the active form, I the inactive form;
• the forward rate constant is $$k_i(E)$$ (dependent on potential) and the backward rate constant is $$k_a(E)$$, also dependent on potential;
• the experiment is done in a series of 5 steps at 3 different potentials: $$E_0$$ then $$E_1$$ then $$E_2$$ then $$E_1$$ then, finally, $$E_0$$;
• the enzyme is assumed to be fully active at the beginning of the first step;
• a single experiment is used to obtain the values of $$k_i$$ and $$k_a$$ for the three potentials (although not reliably for the value at $$E_0$$
• the current given by the active species depends on potential (and it is negative because the enzyme catalyzes a reduction), and the inactive species gives no current;
• in addition to the reversible reaction above, there is an irreversible, potential-dependent loss.
You can download the data files from the GitHub repository. Before fitting the data to determine the values of the rate constants at the potentials of the experiment, we will first subtract the background current, assuming that the respective contributions of faradaic and non-faradaic currents is additive. Start QSoas, go to the directory where you saved the files, and load both the data file and the blank file thus:
QSoas> cd
QSoas> S 1 0

(after the first command, you have to manually select the directory in which you downloaded the data files). The S 1 0 command just subtracts the dataset 1 (the first loaded) from the dataset 0 (the last loaded), see more there. blanc is the French for blank...

Then, we remove a bit of the beginning and the end of the data, corresponding to one half of the steps at $$E_0$$, which we don't exploit much here (they are essentially only used to make sure that the irreversible loss is taken care of properly). This is done using strip-if:

QSoas> strip-if x<30||x>300

Then, we can fit ! The fit used is called fit-linear-kinetic-system, which is used to fit kinetic models with only linear reactions (like here) and steps which change the values of the rate constants but do not instantly change the concentrations. The specific command to fit the data is:
QSoas> fit-linear-kinetic-system /species=2 /steps=0,1,2,1,0

The /species=2 indicates that there are two species (A and I). The /steps=0,1,2,1,0 indicates that there are 5 steps, with three different conditions (0 to 2) in order 0,1,2,1,0. This fits needs a bit of setup before getting started. The species are numbered, 1 and 2, and the conditions (potentials) are indicated by #0, #1 and #2 suffixes.
• The I_1 and I_2 are the currents for the species 1 and 2, so something for 1 (active form) and 0 for 2 (inactive form). Moreover, the parameters I_2_#0 (and _#1, _#2) should be fixed and not free (since we don't need to adjust a current for the inactive form).
• The k_11 and k_22 correspond to species-specific irreversible loss. It is generally best to leave them fixed to 0.
• k_12 is the formation of 2 (I) from 1 (A), and k_21 is the formation of A from I. Their values will be determined for the three conditions. The default values should work here.
• The k_loss parameters are the rates of irreversible loss that apply indiscriminately on all species (unlike k_11 and k_22). They are adjusted and ther default values should work too.
• alpha_1_0 and alpha_2_0 are the initial concentrations of species 1 and 2, so they should be fixed to 1 and 0.
• Last, the xstart_a and (_b, _c, _d and _e) correspond to the starting times for the steps, here, 0, 60, 120, 210 and 270.
For the sake of simplicity, you can also simply load the starting-parameters.params parameters to have all setup the correct way. Then, just hit Fit, enjoy this moment when QSoas works and you don't have to... The screen should now look like this:
Now, it's done ! The fit is actually pretty good, and you can read the values of the inactivation and reactivation rate constants from the fit parameters.

You can train also on the 21.oxw and 21-blanc.oxw files. Usually, re-loading the best fit parameters from other potentials as starting parameters work really well. Gathering the results of several fits into a real curve of rate constants as a function of potentials is left as an exercise for the reader (or maybe a later post), although you may find these series of posts useful in this context !

QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050–5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

## Thursday, March 11, 2021

### All tips and tricks about QSoas

I've decided to post regular summaries of all the articles written here about QSoas; this is the first post of this kind. All the articles related to QSoas can be found here also.

The articles written here can be separated into several categories.

#### Tutorials to analyze real data

These are posts about how to reproduce the data analysis of published articles, including links to the original data so you can fully reproduce our results.
These posts all have the label tutorial.

QSoas has a particularly powerful interface for non-linear least square minimisations (fits):
• See here to learn how you can take advantage of the fit interface to explore easily how a parameter influences the shape of a function.
• See here how you can produce smooth curves for a fit on jaggy data.

#### Meta-data

Meta data describe the conditions in which experiments were performed.
• You can see here how to use them in data analysis, to plot for instance peak position as a function of meta-data;
• You can see here how to permanently store meta-data for files.

#### Quiz and their solutions

Quiz are small problems that take some skill to solve; they can teach you a lot about how to work with QSoas.
• Quiz #1: computing the standard deviation of spectra, along with the solution. This quiz can teach you a lot about combining data from different datasets and manipulating data row-by-row.

#### Other tips and tricks

• See here how to find the 0s of experimental data.
• See here how one can save just the selected points in a baseline, and reuse them.
• See here to learn how to generate many datasets in one go from a mathematical formula.
• See here how to define a custom mathematical function using Ruby.
• See here how one can take advantage of Ruby, the underlying programming language to sum columns, extend the values of columns with lots of missing values and rename datasets using a pattern.
• See here how you can use QSoas without starting the graphical interface !

#### Release annoucements

These have generally lot of general information about the possibilities in QSoas: