Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Solo: doublet identification via semi-supervised deep learning

ABSTRACT: Solo: doublet identification via semi-supervised deep learning

PROVIDER: PRJNA589061 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Solo: doublet identification via semi-supervised deep learning

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

Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data

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

PeptideForest: Semi-supervised machine learning integrating multiple search engines for peptide identification

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

COSMOS: A Platform for real-time morphology-based, label-free cell sorting using deep learning (scRNA-Seq)

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

COSMOS: A Platform for real-time morphology-based, label-free cell sorting using deep learning (bulk RNA-Seq)

2023-09-25 | GSE241833 | GEO

COSMOS: A Platform for real-time morphology-based, label-free cell sorting using deep learning (targeted RNA-Seq)

2023-09-25 | GSE241835 | GEO

Solo specimen

Project description:Solo specimen Raw sequence reads

| PRJNA1114439 | ENA