Kernel-Based Testing for Single-Cell Differential Analysis
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914749856350208 |
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| author | Ozier-Lafontaine, Anthony Fourneaux, Camille Durif, Ghislain Arsenteva, Polina Vallot, Céline Gandrillon, Olivier Giraud, Sandrine Michel, Bertrand Picard, Franck |
| author_facet | Ozier-Lafontaine, Anthony Fourneaux, Camille Durif, Ghislain Arsenteva, Polina Vallot, Céline Gandrillon, Olivier Giraud, Sandrine Michel, Bertrand Picard, Franck |
| contents | Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_08509 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Kernel-Based Testing for Single-Cell Differential Analysis Ozier-Lafontaine, Anthony Fourneaux, Camille Durif, Ghislain Arsenteva, Polina Vallot, Céline Gandrillon, Olivier Giraud, Sandrine Michel, Bertrand Picard, Franck Machine Learning Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods. |
| title | Kernel-Based Testing for Single-Cell Differential Analysis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2307.08509 |