Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

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Items 101–200 of 702

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14. Multimodal learning enables chat-based exploration of single-cell data
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15. Algorithmic advances in machine learning for single-cell expression analysis
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21. Hypergraph factorization for multi-tissue gene expression imputation
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23. The single-cell transcriptomic atlas iPain identifies senescence of nociceptors as a therapeutical target for chronic pain treatment
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31. Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling
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36. A Systematic Evaluation of Supervised Machine Learning Algorithms for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data
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37. Applications of single-cell genomics and computational strategies to study common disease and population-level variation
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39. Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research
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41. Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis
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42. Synthetic single cell RNA sequencing data from small pilot studies using deep generative models
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43. Interpretable generative deep learning: an illustration with single cell gene expression data
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44. Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2
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47. Limitations of cell embedding metrics assessed using drifting islands
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48. Uncovering plaque-glia niches in human Alzheimer’s disease brains using spatial transcriptomics
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51. Uncovering Plaque-Glia Niches in Human Alzheimer’s Disease Brains Using Spatial Transcriptomics
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55. Feature selection methods affect the performance of scRNA-seq data integration and querying
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56. A transcriptomic atlas of astrocyte heterogeneity across space and time in mouse and marmoset
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