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An online peak extraction algorithm for ion mobility spectrometry data.

Kopczynski D, Rahmann S - Algorithms Mol Biol (2015)

Bottom Line: Each individual spectrum is processed as it arrives, removing the need to store the measurement before starting the analysis, as is currently the state of the art.Thus the analysis device can be an inexpensive low-power system such as the Raspberry Pi.The key idea is to extract one-dimensional peak models (with four parameters) from each spectrum and then merge these into peak chains and finally two-dimensional peak models.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics for High-Throughput Technologies, Computer Science XI, and Collaborative Research Center SFB 876, TU Dortmund, Dortmund, Germany.

ABSTRACT
Ion mobility (IM) spectrometry (IMS), coupled with multi-capillary columns (MCCs), has been gaining importance for biotechnological and medical applications because of its ability to detect and quantify volatile organic compounds (VOC) at low concentrations in the air or in exhaled breath at ambient pressure and temperature. Ongoing miniaturization of spectrometers creates the need for reliable data analysis on-the-fly in small embedded low-power devices. We present the first fully automated online peak extraction method for MCC/IMS measurements consisting of several thousand individual spectra. Each individual spectrum is processed as it arrives, removing the need to store the measurement before starting the analysis, as is currently the state of the art. Thus the analysis device can be an inexpensive low-power system such as the Raspberry Pi. The key idea is to extract one-dimensional peak models (with four parameters) from each spectrum and then merge these into peak chains and finally two-dimensional peak models. We describe the different algorithmic steps in detail and evaluate the online method against state-of-the-art peak extraction methods.

No MeSH data available.


Histograms of Fowlkes-Mallows index (FMI; higher is better) and normalized variation of information (NVI; lower is better) comparing 100 simulated measurements containing partitioned peak locations with their clusters produced by the different methods.
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Fig4: Histograms of Fowlkes-Mallows index (FMI; higher is better) and normalized variation of information (NVI; lower is better) comparing 100 simulated measurements containing partitioned peak locations with their clusters produced by the different methods.

Mentions: For the first test, we evaluated 100 sets of data points distributed as described above. The cluster model (normal, exponential or uniform) was drawn randomly. The results show that even with the advantage that k-means knows the true k, our adaptive EM clustering performs best on average in terms of FMI and NVI score (Figure 4).Figure 4


An online peak extraction algorithm for ion mobility spectrometry data.

Kopczynski D, Rahmann S - Algorithms Mol Biol (2015)

Histograms of Fowlkes-Mallows index (FMI; higher is better) and normalized variation of information (NVI; lower is better) comparing 100 simulated measurements containing partitioned peak locations with their clusters produced by the different methods.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4495807&req=5

Fig4: Histograms of Fowlkes-Mallows index (FMI; higher is better) and normalized variation of information (NVI; lower is better) comparing 100 simulated measurements containing partitioned peak locations with their clusters produced by the different methods.
Mentions: For the first test, we evaluated 100 sets of data points distributed as described above. The cluster model (normal, exponential or uniform) was drawn randomly. The results show that even with the advantage that k-means knows the true k, our adaptive EM clustering performs best on average in terms of FMI and NVI score (Figure 4).Figure 4

Bottom Line: Each individual spectrum is processed as it arrives, removing the need to store the measurement before starting the analysis, as is currently the state of the art.Thus the analysis device can be an inexpensive low-power system such as the Raspberry Pi.The key idea is to extract one-dimensional peak models (with four parameters) from each spectrum and then merge these into peak chains and finally two-dimensional peak models.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics for High-Throughput Technologies, Computer Science XI, and Collaborative Research Center SFB 876, TU Dortmund, Dortmund, Germany.

ABSTRACT
Ion mobility (IM) spectrometry (IMS), coupled with multi-capillary columns (MCCs), has been gaining importance for biotechnological and medical applications because of its ability to detect and quantify volatile organic compounds (VOC) at low concentrations in the air or in exhaled breath at ambient pressure and temperature. Ongoing miniaturization of spectrometers creates the need for reliable data analysis on-the-fly in small embedded low-power devices. We present the first fully automated online peak extraction method for MCC/IMS measurements consisting of several thousand individual spectra. Each individual spectrum is processed as it arrives, removing the need to store the measurement before starting the analysis, as is currently the state of the art. Thus the analysis device can be an inexpensive low-power system such as the Raspberry Pi. The key idea is to extract one-dimensional peak models (with four parameters) from each spectrum and then merge these into peak chains and finally two-dimensional peak models. We describe the different algorithmic steps in detail and evaluate the online method against state-of-the-art peak extraction methods.

No MeSH data available.