Thursday, October 22, 2020

QSoas tips and tricks: generating smooth curves from a fit

Often, one would want to generate smooth data from a fit over a small number of data points. For an example, take the data in the following file. It contains (fake) experimental data points that obey to Michaelis-Menten kinetics: $$v = \frac{v_m}{1 + K_m/s}$$ in which \(v\) is the measured rate (the y values of the data), \(s\) the concentration of substrate (the x values of the data), \(v_m\) the maximal rate and \(K_m\) the Michaelis constant. To fit this equation to the data, just use the fit-arb fit:
QSoas> l michaelis.dat
QSoas> fit-arb vm/(1+km/x)
After running the fit, the window should look like this:
Now, with the fit, we have reasonable values for \(v_m\) (vm) and \(K_m\) (km). But, for publication, one would want to generate "smooth" curve going through the lines... Saving the curve from "Data.../Save all" doesn't help, since the data has as many points as the original data and looks very "jaggy" (like on the screenshot above)... So one needs a curve with more data points.

Maybe the most natural solution is simply to use generate-buffer together with apply-formula using the formula and the values of km and vm obtained from the fit, like:

QSoas> generate-buffer 0 20
QSoas> apply-formula y=3.51742/(1+3.69767/x)
By default, generate-buffer generate 1000 evenly spaced x values, but you can change their number using the /samples option. The two above commands can be combined to just one call to generate-buffer:
QSoas> generate-buffer 0 20 3.51742/(1+3.69767/x)
This works, but it is quite cumbersome and it is not going to work well for complex formulas or the results of differential equations or kinetic systems...

This is why to each fit- command corresponds a sim- command that computes the result of the fit using a "saved parameters" file (here, michaelis.params, but you can also save it yourself) and buffers as "models" for X values:

QSoas> generate-buffer 0 20
QSoas> sim-arb vm/(1+km/x) michaelis.params 0
This strategy works with every single fit ! As an added benefit, you even get the fit parameters as meta-data, which are displayed by the show command:
QSoas> show 0
Dataset generated_fit_arb.dat: 2 cols, 1000 rows, 1 segments, #0
Flags: 
Meta-data:	commands =	 sim-arb vm/(1+km/x) michaelis.params 0	fit =	 arb (formula: vm/(1+km/x))	km =	 3.69767
	vm =	 3.5174
They also get saved as comments if you save the data.

Important note: the sim-arb command will be available only in the 3.0 release, although you can already enjoy it if you use the github version.

About 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 2.2. You can download its source code and compile it yourself or buy precompiled versions for MacOS and Windows there.

Wednesday, October 7, 2020

QSoas quiz #1 : averaging spectra

Here is the first QSoas quiz ! I recently measured several identical spectra in a row to evaluate the noise of the setup, and so I wanted to average all the spectra and also determine the standard deviation in the absorbances. Averaging the spectra can simply be done taking advantage of the average command:
QSoas> load Spectrum*.dat /flags=spectra
QSoas> average flagged:spectra
However, average does not provide means to make standard deviations, it just takes the average of all but the X column. I wanted to add this feature, but I realized there are already at least two distinct ways to do that...

Quiz

Your task is to determine the average and standard deviations of the three spectra located there, (Spectrum-1.dat, Spectrum-2.dat and Spectrum-3.dat). There are at least two ways:
  • One that relies simply on average and on apply-formula, and which requires that you remember how to compute standard deviations.
  • One that is a little more involved, that requires more data manipulation (take a look at contract for instance) and relies on the fact that you can use statistics in apply-formula (and in particular you can use y_stddev to refer to the standard deviation of \(y\)), but which does not require you to know exactly how to compute standard deviations.
To help you, I've added the result in Average.dat. The figure below shows a zoom on the data superimposed to the average (bonus points to find how to display this light red area that corresponds to the standard deviation !).
I will post the answer later. In the meantime, feel free to post your own solutions or attempts, hacks, and so on !

About 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 2.2. You can download its source code or buy precompiled versions for MacOS and Windows there.

Wednesday, September 23, 2020

Tutorial: analyze Km data of CODHs

This is the first post of a series in which we will provide the readers with simple tutorial approaches to reproduce the data analysis of some of our published papers. All our data analysis is performed using QSoas. Today, we will show you how to analyze the experiments we used to characterize the behaviour of an enzyme, the Nickel-Iron CO dehydrogenase IV from Carboxytothermus hydrogenoformans. The experiments we analyze here are described in much more details in the original publication, Domnik et al, Angewandte Chemie, 2017. The only things you need to know for now are the following:
  • the data is the evolution of the current over time given by the absorbed enzyme;
  • at a given point, a certain amount of CO (50 µM) is injected, and its concentration decreases exponentially over time;
  • the enzyme has a Michaelis-Menten behaviour with respect to CO.
This means that we expect a response of the type: $$i(t) = \frac{i_m}{1 + \frac{K_m}{[\mathrm{CO}](t)}}$$ in which $$[\mathrm{CO}](t) = \begin{cases} 0, & \text{for } t < t_0 \\ C_0 \exp \frac{t_0 - t}{\tau}, & \text{for }t\geq t_0 %> \end{cases}$$

To begin this tutorial, first download the files from the github repository (direct links: data, parameter file and ruby script). Start QSoas, go to the directory where you saved the files, load the data file, and remove spikes in the data using the following commands:

QSoas> cd
QSoas> l Km-CODH-IV.dat
QSoas> R
First fit
Then, to fit the above equation to the data, the simplest is to take advantage of the time-dependent parameters features of QSoas. Run simply:
QSoas> fit-arb im/(1+km/s) /with=s:1,exp
This simply launches the fit interface to fit the exact equations above. The im/(1+km/s) is simply the translation of the Michaelis-Menten equation above, and the /with=s:1,exp specifies that s is the result of the sum of 1 exponential like for the definition of above. Then, load the Km-CODH-IV.params parameter files (using the "Parameters.../Load from file" action at the bottom, or the Ctrl+L keyboard shortcut). Your window should now look like this:
To fit the data, just hit the "Fit" button ! (or Ctrl+F).
Including an offset
The fit is not bad, but not perfect. In particular, it is easy to see why: the current predicted by the fit goes to 0 at large times, but the actual current is below 0. We need therefore to include an offset to take this into consideration. Close the fit window, and re-run a fit, but now with this command:
QSoas> fit-arb im/(1+km/s)+io /with=s:1,exp
Notice the +io bit that corresponds to the addition of an offset current. Load again the base parameters, run the fit again... Your fit window show now look like:
See how the offset current is now much better taken into account. Let's talk a bit more about the parameters:
  • im is \(i_m\), the maximal current, around 120 nA (which matches the magnitude of the original current).
  • io is the offset current, around -3nA.
  • km is the \(K_m\), expressed in the same units as s_1, the first "injected" value of s (we used 50 because the injection is 50 µM CO). That means the value of \(K_m\) determined by this fit is around 9 nM ! (but see below).
  • s_t_1 is the time of the injection of CO (it was in the parameter files you loaded).
  • Finally, s_tau_1 is the time constant of departure of CO, noted \(\tau\) in the equations above.
Taking into account mass-transport limitations
However, the fit is still unsatisfactory: the predicted curve fails to reproduce the curvature at the beginning and at the end of the decrease. This is due to issues linked to mass-transport limitations, which are discussed in details in Merrouch et al, Electrochimica Acta, 2017. In short, what you need to do is to close the fit window again, load the transport.rb Ruby file that contains the definition of the itrpt function, and re-launch the fit window using:
QSoas> ruby-run transport.rb
QSoas> fit-arb itrprt(s,km,nFAm,nFAmu)+io /with=s:1,exp
Load again the parameter file... but this time you'll have to play a bit more with the starting parameters for QSoas to find the right values when you fit. Here are some tips:
  • the curve predicted with the current parameters (use "Update" to update the display) should "look like" the data;
  • apart from io, no parameter should be negative;
  • there may be hints about the correct values in the papers...
A successful fit should look like this:
Here you are ! I hope you enjoyed analyzing our data, and that it will help you analyze yours ! Feel free to comment and ask for clarifications.

About 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 2.2. You can download its source code or buy precompiled versions for MacOS and Windows there.

Wednesday, March 21, 2018

Release 2.2 of QSoas

The new release of QSoas is finally ready ! It brings in a lot of new features and improvements, notably greatly improved memory use for massive multifits, a fit for linear (in)activation processes (the one we used in Fourmond et al, Nature Chemistry 2014), a new way to transform "numbers" like peak position or stats into new datasets and even SVG output ! Following popular demand, it also finally brings back the peak area output in the find-peaks command (and the other, related commands) ! You can browse the full list of changes there.

The new release can be downloaded from the downloads page.

Freely available binary images for QSoas 1.0

In addition to the new release, we are now releasing the binary images for MacOS and Windows for the release 1.0. They are also freely available for download from the downloads page.

About 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 2.2. You can download its source code or buy precompiled versions for MacOS and Windows there.

Tuesday, October 10, 2017

Define a function with inline Ruby code in QSoas

QSoas can read and execute Ruby code directly, while reading command files, or even at the command prompt. For that, just write plain Ruby code inside a ruby...ruby end block. Probably the most useful possibility is to define elaborated functions directly from within QSoas, or, preferable, from within a script; this is an alternative to defining a function in a completely separated Ruby-only file using ruby-run. For instance, you can define a function for plain Michaelis-Menten kinetics with a file containing:

ruby
def my_func(x, vm, km)
  return vm/(1 + km/x)
end
ruby end

This defines the function my_func with three parameters, , (vm) and (km), with the formula:

You can then test that the function has been correctly defined running for instance:

QSoas> eval my_func(1.0,1.0,1.0)
 => 0.5
QSoas> eval my_func(1e4,1.0,1.0)
 => 0.999900009999

This yields the correct answer: the first command evaluates the function with x = 1.0, vm = 1.0 and km = 1.0. For , the result is (here 0.5). For , the result is almost . You can use the newly defined my_func in any place you would use any ruby code, such as in the optional argument to generate-buffer, or for arbitrary fits:

QSoas> generate-buffer 0 10 my_func(x,3.0,0.6)
QSoas> fit-arb my_func(x,vm,km)

To redefine my_func, just run the ruby code again with a new definition, such as:
ruby
def my_func(x, vm, km)
  return vm/(1 + km/x**2)
end
ruby end
The previous version is just erased, and all new uses of my_func will refer to your new definition.


See for yourself

The code for this example can be found there. Browse the qsoas-goodies github repository for more goodies !

About 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 2.1. You can download its source code or buy precompiled versions for MacOS and Windows there.

Friday, September 8, 2017

Extract many attachement from many mails in one go using ripmime

I was recently looking for a way to extract many attachments from a series of emails. I first had a look at the AttachmentExtractor thunderbird plugin, but it seems very old and not maintained anymore. So I've come up with another very simple solution that also works with any other mail client.

Just copy all the mails you want to extract attachments from to a single (temporary) mail folder, find out which file holds the mail folder and use ripmime on that file (ripmime is packaged for Debian). For my case, it looked like:

~ ripmime -i .icedove/XXXXXXX.default/Mail/pop.xxxx/tmp -d target-directory

Simple solution, but it saved me quite some time. Hope it helps !

Thursday, July 13, 2017

Screen that hurts my eyes, take 2

Six month ago, I wrote a lengthy post about my new computer hurting my eyes. I haven't made any progress with that, but I've accidentally upgraded my work computer from Kernel 4.4 to 4.8 and the nvidia drivers from 340.96-4 to 375.66-2. Well, my work computer now hurts, I've switched back to the previous kernel and drivers, I hope it'll be back to normal.

Any ideas of something specific that changed, either between 4.4 and 4.8 (kernel startup code, default framebuffer modes, etc ?), or between the 340.96 and the 375.66 drivers ? In any case, I'll try that specific combination of kernels/drivers home to see if I can get it to a useable state.

Update, July 23rd

I reverted to the earlier drivers/kernel, to no avail. But it seems in fact the problem with my work computer is linked to an allergy of some kind, since antihistamine drugs have an effect (there are construction works in my lab, perhaps they are the cause ?). No idea still for my home computer, for which the problem is definitely not solved.