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A metric for odorant comparison

Abstract

In studies of vision and audition, stimuli can be systematically varied by wavelength and frequency, respectively, but there is no equivalent metric for olfaction. Restricted odorant-feature metrics such as number of carbons and functional group do not account for response patterns to odorants varying along other structural dimensions. We generated a multidimensional odor metric, in which each odorant molecule was represented as a vector of 1,664 molecular descriptor values. Revisiting many studies, we found that this metric and a second optimized metric were always better at accounting for neural responses than the specific metric used in each study. These metrics were applicable across studies that differed in the animals studied, the type of olfactory neurons tested, the odorants applied and the recording methods used. We use this new metric to recommend sets of odorants that span the physicochemical space for use in olfaction experiments.

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Figure 1: Evaluating the multidimensional metric.
Figure 2: Correlation between neuronal response pattern similarity and odorant distances calculated using three different metrics across 7 datasets.
Figure 3: Testing the predictive power of the multidimensional metric using new data.

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Acknowledgements

We thank A. Elite.

Author information

Authors and Affiliations

Authors

Contributions

R.H., R.K., D.H. and N.S. are authors of the concept. R.H. performed the analysis; R.H., N.S. and D.H. wrote the manuscript; Y.K.T. and K.M. provided data post-hoc for the blind test.

Corresponding author

Correspondence to Rafi Haddad.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4, Supplementary Table 1–3, Supplementary Methods (PDF 312 kb)

Supplementary Data 1

The predicted and observed values used for the blind test. The distances were calculated by using the multidimensional optimized metric. (XLS 440 kb)

Supplementary Data 2

Interactive tool to calculate odorant distances between any two odorants out of more than 400 commonly used odorants. The distance is calculated by using the multidimensional optimized metric. (XLS 323 kb)

Supplementary Data 3

A suggested 8 clusters with different granularity of more than 400 odorants using the optimized multidimensional metric. Each clustering can be used to select odorant that span the odor space in a more systematic way. (XLS 124 kb)

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Haddad, R., Khan, R., Takahashi, Y. et al. A metric for odorant comparison. Nat Methods 5, 425–429 (2008). https://doi.org/10.1038/nmeth.1197

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