Solo: doublet identification via semi-supervised deep learning
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ABSTRACT:
PROVIDER: PRJNA589061 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We developed a semi-supervised deep learning framework for the identification of doublets in scRNA-seq analysis called Solo. To validate our method, we used MULTI-seq, cholesterol modified oligos (CMOs), to experimentally identify doublets in a solid tissue with diverse cell types, mouse kidney, and showed Solo recapitulated experimentally identified doublets.
2019-11-13 | GSE140262 | GEO
Project description:Data and results for paper, "Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data," found at:
https://doi.org/10.1101/2020.11.12.380881
Deep learning software for PSM recalibration, called ProteoTorch-DNN, available at:
https://github.com/proteoTorch/proteoTorch
with documentation:
https://proteotorch.readthedocs.io/en/latest/
2021-05-11 | MSV000087402 | MassIVE
Project description:We introduce PeptideForest, a semi-supervised machine learning approach that integrates the assignment of peptides to mass spectra from multiple algorithms to train a random forest classifier, thereby combining the results from different search engines. PeptideForest increases the number of peptide-to-spectrum matches that exhibit a q-value lower than 1% by 25.2 ± 1.6% compared to MS-GF+ data on samples containing mixed HEK and Escherichia coli proteomes. However, an increase in quantity does not necessarily reflect an increase in quality and this is why we devised a novel approach to determine the quality of the assigned spectra through TMT quantification of samples with known ground truths. Thereby, we could show that the increase in PSMs below 1% q-value does not come with a decrease in quantification quality and as such PeptideForest offers a possibility to gain deeper insights into bottom-up proteomics. PeptideForest has been integrated into our pipeline framework Ursgal and can therefore be combined with a wide array of algorithms.
2025-06-02 | PXD056915 | Pride
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
2023-09-25 | GSE241834 | GEO
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
2023-09-25 | GSE241835 | GEO
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
2023-09-25 | GSE241833 | GEO
Project description: Not available
| S-EPMC6550282 | biostudies-literature
Project description:Solo amostrada fazenda Raw sequence reads
| PRJNA1098501 | ENA
Project description:Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these tools remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Thus, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesized and characterized in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperformed (R2=0.43-0.70) previous state-of-the-art thermodynamic and kinetic models (R2=0.04-0.15) and allowed for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This deep learning approach constitutes a major step forward in engineering and understanding of RNA synthetic biology.
2020-08-12 | GSE149225 | GEO