Detection of Specific Templates in Calcium Spiking in HeLa Cells Using Hierarchical DBSCAN: Clustering and Visualization of CellDrug Interaction at Multiple Doses

Chel, Soumita; Gare, Suman; Giri, Lopamudra

doi:10.1109/embc44109.2020.9175925

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…Hence, further improvements can be proposed based on HDBSCAN (hierarchical density-based spatial clustering of applications with noise) clustering in the future, which can be used for automation in the selection of cluster numbers. 44 Furthermore, to achieve fully automated analysis, an image-processing module coupled with the proposed dimension reduction and clustering can be used as a highthroughput data analysis workflow toward toxicity mapping. Also, the proposed computational tool can be used for analysis of cytosolic Ca 2+ measured through genetic sensors.…”

Section: Discussionmentioning

confidence: 99%

Quantitative confocal microscopy for grouping of dose–response data: Deciphering calcium sequestration and subsequent cell death in presence of excess norepinephrine

Manohar

Gare

Chel

et al. 2022

Preprint

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Live imaging based testing of ligands and identification of toxic dose range during in vitro studies is a time-intensive process due to the inherent heterogeneity present in cell responses. In this context, high magnification imaging and large-scale data visualization remains challenging for analysis of toxicity during the drug screening as well as selection of dose-range. To address this challenge, we propose the measurement of cytosolic calcium ion (Ca2+) using spinning disk confocal microscopy at a higher resolution for generation of imaging data that can be visualized using uniform manifold and projection (UMAP). First, we performed large scale experiments and showed norepinephrine induced increase in Ca2+ flux in HeLa cells for a large range of doses. Secondly, the time-series dataset was mapped in 2D plane using UMAP. We also show that the proposed framework can be used to depict the relative distribution of various responses corresponding to a range of drug doses. To the best of our knowledge, this is the first attempt for UMAP visualization of time-series dose-response and identification of Ca2+ signature in the toxic dose-range. Such quantitative microscopy can be used for prediction of toxic drug dose range, and identification of drugs that lead to lytic cell death.

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Section: Discussionmentioning

confidence: 99%

Quantitative confocal microscopy for grouping of dose–response data: Deciphering calcium sequestration and subsequent cell death in presence of excess norepinephrine

Manohar

Gare

Chel

et al. 2022

Preprint

Self Cite

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show abstract

“…Hence, further improvements can be proposed based on HDBSCAN (hierarchical density-based spatial clustering of applications with noise) clustering in the future, which can be used for automation in the selection of cluster numbers. 44 Furthermore, to achieve fully automated analysis, an image-processing module coupled with the proposed dimension reduction and clustering can be used as a high-throughput data analysis workflow toward toxicity mapping. Also, the proposed computational tool can be used for analysis of cytosolic Ca 2+ measured through genetic sensors.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Confocal Microscopy for Grouping of Dose–Response Data: Deciphering Calcium Sequestration and Subsequent Cell Death in the Presence of Excess Norepinephrine

et al. 2021

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“…In this work, we approach this problem upside down. We develop methods to investigate the autoencoder's capability to conform to the manifold hypothesis in a visual and qualitative way, which integrates into the general trend of visualization methods gaining more importance over the last while [23][24][25]. Our goal is to give data scientists a non-mathematical and interpretable tool at hand to monitor and supervise the nonlinear embedding process whose result constitutes the latency space.…”

Section: Our Approachmentioning

confidence: 99%

Meta-Parameter Selection for Embedding Generation of Latency Spaces in Auto Encoder Analytics

Walch

Schichtel

Lehmann

et al. 2021

The 7th International Conference on Time Series and Forecasting

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Picking an appropriate parameter setting (meta-parameters) for visualization and embedding techniques is a tedious task. However, especially when studying the latent representation generated by an autoencoder for unsupervised data analysis, it is also an indispensable one. Here we present a procedure using a cross-correlative take on the meta-parameters. This ansatz allows us to deduce meaningful meta-parameter limits using OPTICS, DBSCAN, UMAP, t-SNE, and k-MEANS. We can perform first steps of a meaningful visual analysis in the unsupervised case using a vanilla autoencoder on the MNIST and DeepVALVE data sets.

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Detection of Specific Templates in Calcium Spiking in HeLa Cells Using Hierarchical DBSCAN: Clustering and Visualization of CellDrug Interaction at Multiple Doses

Cited by 8 publications

References 6 publications

Quantitative confocal microscopy for grouping of dose–response data: Deciphering calcium sequestration and subsequent cell death in presence of excess norepinephrine

Quantitative confocal microscopy for grouping of dose–response data: Deciphering calcium sequestration and subsequent cell death in presence of excess norepinephrine

Quantitative Confocal Microscopy for Grouping of Dose–Response Data: Deciphering Calcium Sequestration and Subsequent Cell Death in the Presence of Excess Norepinephrine

Meta-Parameter Selection for Embedding Generation of Latency Spaces in Auto Encoder Analytics

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