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bioRxiv · 10.1101/2020.03.13.991216

Automated curation of CNMF-E-extracted ROI spatial footprints and calcium traces using open-source AutoML tools

Tran, L. M.·Mocle, A. J.·Ramsaran, A. I.·Jacob, A. D.·Frankland, P. W.·Josselyn, S. A.
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Abstract

In vivo 1-photon calcium imaging is an increasingly prevalent method in behavioural neuroscience. Numerous analysis pipelines have been developed to improve the reliability and scalability of pre-processing and ROI extraction for these large calcium imaging datasets. Despite these advancements in pre-processing methods, manual curation of the extracted spatial footprints and calcium traces of neurons remains important for quality control. Here, we propose an additional semi-automated curation step for sorting spatial footprints and calcium traces from putative neurons extracted using the popular CNMF-E algorithm. We used the automated machine learning tools TPOT and AutoSklearn to generate classifiers to curate the extracted ROIs trained on a subset of human-labeled data. AutoSklearn produced the best performing classifier, achieving an F1 score > 92% on the ground truth test dataset. This automated approach is a useful strategy for filtering ROIs with relatively few labeled data points, and can be easily added to pre-existing pipelines currently using CNMF-E for ROI extraction.

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Tran, L. M., Mocle, A. J., Ramsaran, A. I., Jacob, A. D., Frankland, P. W., Josselyn, S. A.. 2020-03-14. Automated curation of CNMF-E-extracted ROI spatial footprints and calcium traces using open-source AutoML tools. https://doi.org/10.1101/2020.03.13.991216

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