Introduction

Welcome to qAlgorithms, a comprehensive collection of C++ libraries designed specifically for processing analytical data. Our focus is on the non-target screening data domain, ensuring precise and reliable data processing for this complex field.

The qBinning Beta aims to expand the existing program by finding and eliminating edge cases during the binning that lead to less accurate results and experiments with additional control prameters throughout the entire program.

We are always open for suggestions and feedback regarding your useage of our algorithms, so do not hesitate to open an issue on our github page.

Installation and Usage

Please note that qAlgorithms is still in active development and result accuracy cannot be guaranteed at this stage.

Windows

The entire qAlgorithms workflow is provided as an executable under "Releases" on our github repository. Note that qAlgorithms requires the libraries libgcc_s_seh-1.dll, libgomp-1.dll and libwinpthread-1.dll. If they are not present on your system already, you can also download them under "Releases" or by clicking on the filenames above. There is no need to download the source code.

Linux

Currently, no Linux releases are provided. We recommend you to clone the repository and compile from source using cmake and GCC.

Usage

On windows, start qAlgorithms.exe using powershell. Avoid non-ASCII characters in filenames. If a folder or filename has a space in it, you need to enter the absolute path with quotes to read in everything correctly. To use qAlgorithms for processing mass spectrometry data, you need to convert your measurements into .mzML files, for example with msconvert. Currently, only MS1 data can be used, so you save some disk space if you filter them out at this stage.

qAlgorithms is a command line utility which reads mzML files and outputs them as csv. You can select individual files or an entire directory to search for mzML files recursively. All output is written into one folder, which you also must specify. Below are some commands you will likely use:

./qAlgorithms.exe -h - Display the help menu, listing all availvable options.

./qAlgorithms.exe -i C:/example/path/measurement.mzML -o ../my/results -printpeaks - Process the file measurement.mzML and write a file with every detected peak into the directory "results".

./qAlgorithms.exe -i ./allMeasurements -o ./results -printall - searches the directory "allMeasurements" and all subdirectories for files ending in .mzML and process them. All intermediate results, those being centroids, bins and peaks, are written to a .csv file and saved to the "results" directory.

Some things to keep in mind:

• qAlgorithms can process both profile data and centroided data. When using centroided data, it is not possible to estimate an individual uncertainty for centroids, which leads to less accurate results. Where possible, process profile mode spectra instead.
• If you do not specify which results you want, no output will be written
• If multiple copies of the same file are found during recursive search, only one of them will be processed
• The different quality scores do not serve as a way to remove peaks from your results. They only indicate how well the data at every step fit our model assumptions regarding the mathematical properties of real peaks. All peaks which are provided in the peak table are statistically significant. The best current usage for quality scores is priorisation of peaks during further analysis.
• A negative DQSpeak means that the regression is more uncertain than the DQSpeak implies.
• A DQSpeak of -10 means that the peak is (probably) incorrect. This is a temporary solution.

Documentation

Full documentation can be found here.

Philosophy

Unlike many traditional data processing tools, our algorithms do not rely on manual user input parameters such as thresholds. Instead, they intelligently leverage the inherent properties of the measurement data itself. This approach allows the algorithms to dynamically assess and utilize the quality of the data, ensuring robust and reproducible results every time.

qAlgorithms aims to make your processing as fast as possible, so you don't have to spend more time waiting for your computer to finish than evaluating results.

Scientific Rigor

Our commitment to scientific validity is unwavering. The algorithms within qAlgorithms are rooted in well-established statistical tests. Our primary goal is to deliver results that aren't just accurate but also statistically significant, providing confidence in every analysis.

Current Offerings

qCentroids Algorithm

Our current flagship algorithm, qCentroids by Reuschenbach, Renner et al. [https://doi.org/10.1007/s00216-022-04224-y], is tailored for centroiding HRMS spectra. It excels in handling data from instruments like Orbitrap and TOF, converting their profile spectra into centroided data. Whether you're dealing with high-resolution mass spectrometry data or other analytical measurements, qCentroids offers a reliable solution without the usual hassles of parameter tweaks.

qBinning Algorithm

The qBinning algorithm utilises the centroids generated by qCentroids to produce extracted ion chromatograms. Like qCentroids, it requires no user parameters. Binning allows you to reduce the amount of centroids considered in future analysis by roughly 30%. The current qBinning program is based on the algorithm presented by Reuschenbach, Renner et al. [https://doi.org/10.1021/acs.analchem.3c01079], but implements additional steps for finding the highest amount of statistically sound bins.

qPeaks Algorithm

As the current end point of qAlgorithms, qPeaks uses a comprehensive peak model developed by Renner et al. [https://doi.org/10.1021/acs.analchem.4c00494] to identify peaks within the bins generated by qBinning. Every peak is statistically significant, sidestepping the need for further filtering steps like a minimum intensity requirement. The scores generated provide you with information about how well every step of the process to your peak worked, and allow you to make a statement about the confidence of your results. Like all parts of the qAlgorithms project, qPeaks requires no user parameters.

Future Roadmap

Our team is continuously researching and developing new algorithms to expand the capabilities of qAlgorithms. Stay tuned for more innovative solutions for analytical data processing!

qAlgorithms - Transforming data into insights.