Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data.
<p><b>(A)</b> In a simulated dataset containing three different neurons, UMAP-based sorting forms three well-separated clusters with distinct shapes and colors, indicating strong accuracy and minimal overlap. <b>(B)</b> Applying wavelet sorting to the same dataset merge...
Bewaard in:
| Hoofdauteur: | |
|---|---|
| Andere auteurs: | , , , , , , , , , , |
| Gepubliceerd in: |
2025
|
| Onderwerpen: | |
| Tags: |
Voeg label toe
Geen labels, Wees de eerste die dit record labelt!
|
| _version_ | 1849927641759481856 |
|---|---|
| author | Daniel Suárez-Barrera (22676654) |
| author2 | Lucas Bayones (22676657) Norberto Encinas-Rodríguez (22676660) Sergio Parra (22676663) Viktor Monroy (22676666) Sebastián Pujalte (22676669) Bernardo Andrade-Ortega (22676672) Héctor Díaz (22676675) Manuel Alvarez (3468647) Antonio Zainos (22676678) Alessio Franci (143351) Román Rossi-Pool (22676681) |
| author2_role | author author author author author author author author author author author |
| author_facet | Daniel Suárez-Barrera (22676654) Lucas Bayones (22676657) Norberto Encinas-Rodríguez (22676660) Sergio Parra (22676663) Viktor Monroy (22676666) Sebastián Pujalte (22676669) Bernardo Andrade-Ortega (22676672) Héctor Díaz (22676675) Manuel Alvarez (3468647) Antonio Zainos (22676678) Alessio Franci (143351) Román Rossi-Pool (22676681) |
| author_role | author |
| dc.creator.none.fl_str_mv | Daniel Suárez-Barrera (22676654) Lucas Bayones (22676657) Norberto Encinas-Rodríguez (22676660) Sergio Parra (22676663) Viktor Monroy (22676666) Sebastián Pujalte (22676669) Bernardo Andrade-Ortega (22676672) Héctor Díaz (22676675) Manuel Alvarez (3468647) Antonio Zainos (22676678) Alessio Franci (143351) Román Rossi-Pool (22676681) |
| dc.date.none.fl_str_mv | 2025-11-24T18:33:33Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pbio.3003527.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Comparison_of_Uniform_Manifold_Approximation_and_Projection_UMAP_-based_and_feature-based_spike_sorting_on_synthetic_data_/30697580 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Biology Molecular Biology Neuroscience Physiology Biotechnology Developmental Biology Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified spike sorting pipelines spike sorting pipeline seldom spiking neurons recorded putative neurons correctly sorted neurons clustering algorithms responsible spike sorting makes reliable spike sorting umap drastically increases data analysis techniques drastically improve processed data experimental data universal practice precise explorations neural recordings neural code mathematically grounded fundamentally motivated extracellular electrophysiology enables deeper crucial step computational cost ad hoc |
| dc.title.none.fl_str_mv | Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p><b>(A)</b> In a simulated dataset containing three different neurons, UMAP-based sorting forms three well-separated clusters with distinct shapes and colors, indicating strong accuracy and minimal overlap. <b>(B)</b> Applying wavelet sorting to the same dataset merges the spikes into just two clusters that exhibit noticeable overlap and weak separation, as illustrated in both 3D (top) and 2D (bottom) views. <b>(C)</b> Principal component analysis (PCA)-based sorting isolates only a single cluster with poor separation, underscoring PCA’s difficulty with complex spike distributions. <b>(D)</b> By contrast, UMAP spike sorting consistently surpasses both wavelet and PCA methods, particularly in higher-dimensional spaces, as measured by the F1 score. <b>(E)</b> UMAP-based sorting also proves robust against noise perturbations at various spike dilution levels (dark trace; firing rate [fr] at 100% vs. light blue trace; fr diluted at 40%). Even under increased noise and cluster dilution, it preserves high F1 scores. <b>(F)</b> Moreover, UMAP-based sorting (dark blue) remains accurate despite data loss, achieving high F1 scores even when a large fraction of spikes (x-axis) is removed from one cluster, whereas wavelet (green) and PCA (orange) methods experience a significant decline in performance. The synthetic data used to generate this figure are publicly available at [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003527#pbio.3003527.ref037" target="_blank">37</a>], and the code for performing the analyses is available at [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003527#pbio.3003527.ref049" target="_blank">49</a>].</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_1ec51fdac1d5c6519914f623f3df47d4 |
| identifier_str_mv | 10.1371/journal.pbio.3003527.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30697580 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data.Daniel Suárez-Barrera (22676654)Lucas Bayones (22676657)Norberto Encinas-Rodríguez (22676660)Sergio Parra (22676663)Viktor Monroy (22676666)Sebastián Pujalte (22676669)Bernardo Andrade-Ortega (22676672)Héctor Díaz (22676675)Manuel Alvarez (3468647)Antonio Zainos (22676678)Alessio Franci (143351)Román Rossi-Pool (22676681)Cell BiologyMolecular BiologyNeurosciencePhysiologyBiotechnologyDevelopmental BiologyScience PolicySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedspike sorting pipelinesspike sorting pipelineseldom spiking neuronsrecorded putative neuronscorrectly sorted neuronsclustering algorithms responsiblespike sorting makesreliable spike sortingumap drastically increasesdata analysis techniquesdrastically improveprocessed dataexperimental datauniversal practiceprecise explorationsneural recordingsneural codemathematically groundedfundamentally motivatedextracellular electrophysiologyenables deepercrucial stepcomputational costad hoc<p><b>(A)</b> In a simulated dataset containing three different neurons, UMAP-based sorting forms three well-separated clusters with distinct shapes and colors, indicating strong accuracy and minimal overlap. <b>(B)</b> Applying wavelet sorting to the same dataset merges the spikes into just two clusters that exhibit noticeable overlap and weak separation, as illustrated in both 3D (top) and 2D (bottom) views. <b>(C)</b> Principal component analysis (PCA)-based sorting isolates only a single cluster with poor separation, underscoring PCA’s difficulty with complex spike distributions. <b>(D)</b> By contrast, UMAP spike sorting consistently surpasses both wavelet and PCA methods, particularly in higher-dimensional spaces, as measured by the F1 score. <b>(E)</b> UMAP-based sorting also proves robust against noise perturbations at various spike dilution levels (dark trace; firing rate [fr] at 100% vs. light blue trace; fr diluted at 40%). Even under increased noise and cluster dilution, it preserves high F1 scores. <b>(F)</b> Moreover, UMAP-based sorting (dark blue) remains accurate despite data loss, achieving high F1 scores even when a large fraction of spikes (x-axis) is removed from one cluster, whereas wavelet (green) and PCA (orange) methods experience a significant decline in performance. The synthetic data used to generate this figure are publicly available at [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003527#pbio.3003527.ref037" target="_blank">37</a>], and the code for performing the analyses is available at [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003527#pbio.3003527.ref049" target="_blank">49</a>].</p>2025-11-24T18:33:33ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pbio.3003527.g002https://figshare.com/articles/figure/Comparison_of_Uniform_Manifold_Approximation_and_Projection_UMAP_-based_and_feature-based_spike_sorting_on_synthetic_data_/30697580CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306975802025-11-24T18:33:33Z |
| spellingShingle | Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. Daniel Suárez-Barrera (22676654) Cell Biology Molecular Biology Neuroscience Physiology Biotechnology Developmental Biology Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified spike sorting pipelines spike sorting pipeline seldom spiking neurons recorded putative neurons correctly sorted neurons clustering algorithms responsible spike sorting makes reliable spike sorting umap drastically increases data analysis techniques drastically improve processed data experimental data universal practice precise explorations neural recordings neural code mathematically grounded fundamentally motivated extracellular electrophysiology enables deeper crucial step computational cost ad hoc |
| status_str | publishedVersion |
| title | Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. |
| title_full | Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. |
| title_fullStr | Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. |
| title_full_unstemmed | Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. |
| title_short | Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. |
| title_sort | Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. |
| topic | Cell Biology Molecular Biology Neuroscience Physiology Biotechnology Developmental Biology Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified spike sorting pipelines spike sorting pipeline seldom spiking neurons recorded putative neurons correctly sorted neurons clustering algorithms responsible spike sorting makes reliable spike sorting umap drastically increases data analysis techniques drastically improve processed data experimental data universal practice precise explorations neural recordings neural code mathematically grounded fundamentally motivated extracellular electrophysiology enables deeper crucial step computational cost ad hoc |