Browse
Submit Data
Databases
API
Help

Dataset Information

38 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Ensemble learning for classifying single-cell data and projection across reference atlases.

ABSTRACT:

Summary

Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms.

Availability and implementation

https://github.com/diazlab/ELSA.

Contact

aaron.diaz@ucsf.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Wang L

PROVIDER: S-EPMC7267838 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Ensemble learning for classifying single-cell data and projection across reference atlases.

Wang Lin L Catalan Francisca F Shamardani Karin K Babikir Husam H Diaz Aaron A

Bioinformatics (Oxford, England) 20200601 11

<h4>Summary</h4>Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across ...[more]

PMID: 32105316

Similar Datasets

Ensemble learning for classifying single-cell data and projection across reference atlases

Project description:Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms.

2020-03-01 | GSE141982 | GEO

Ensemble learning for classifying single-cell data and projection across reference atlases

Project description:Ensemble learning for classifying single-cell data and projection across reference atlases

| PRJNA595499 | ENA

Ensemble learning for classifying single-cell data and projection across reference atlases

| EGAS00001004283 | EGA

Mapping single-cell data to reference atlases by transfer learning.

Project description:Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.

| S-EPMC8763644 | biostudies-literature

Interpretation of T cell states from single-cell transcriptomics data using reference atlases.

Project description:Single-cell RNA sequencing (scRNA-seq) has revealed an unprecedented degree of immune cell diversity. However, consistent definition of cell subtypes and cell states across studies and diseases remains a major challenge. Here we generate reference T cell atlases for cancer and viral infection by multi-study integration, and develop ProjecTILs, an algorithm for reference atlas projection. In contrast to other methods, ProjecTILs allows not only accurate embedding of new scRNA-seq data into a reference without altering its structure, but also characterizing previously unknown cell states that "deviate" from the reference. ProjecTILs accurately predicts the effects of cell perturbations and identifies gene programs that are altered in different conditions and tissues. A meta-analysis of tumor-infiltrating T cells from several cohorts reveals a strong conservation of T cell subtypes between human and mouse, providing a consistent basis to describe T cell heterogeneity across studies, diseases, and species.

| S-EPMC8137700 | biostudies-literature

Classifying Drosophila Olfactory Projection Neuron Subtypes by Single-Cell RNA Sequencing.

Project description:The definition of neuronal type and how it relates to the transcriptome are open questions. Drosophila olfactory projection neurons (PNs) are among the best-characterized neuronal types: different PN classes target dendrites to distinct olfactory glomeruli, while PNs of the same class exhibit indistinguishable anatomical and physiological properties. Using single-cell RNA sequencing, we comprehensively characterized the transcriptomes of most PN classes and unequivocally mapped transcriptomes to specific olfactory function for six classes. Transcriptomes of closely related PN classes exhibit the largest differences during circuit assembly but become indistinguishable in adults, suggesting that neuronal subtype diversity peaks during development. Transcription factors and cell-surface molecules are the most differentially expressed genes between classes and are highly informative in encoding cell identity, enabling us to identify a new lineage-specific transcription factor that instructs PN dendrite targeting. These findings establish that neuronal transcriptomic identity corresponds with anatomical and physiological identity defined by connectivity and function.

| S-EPMC6095479 | biostudies-literature

SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection.

Project description:To process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion during dimension reduction, we present SHARP, an ensemble random projection-based algorithm that is scalable to clustering 10 million cells. Comprehensive benchmarking tests on 17 public scRNA-seq data sets show that SHARP outperforms existing methods in terms of speed and accuracy. Particularly, for large-size data sets (more than 40,000 cells), SHARP runs faster than other competitors while maintaining high clustering accuracy and robustness. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering scRNA-seq data with 10 million cells.

| S-EPMC7050522 | biostudies-literature

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.

Project description:MotivationRecent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in individual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterization of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy.ResultsWe propose SnapCCESS for clustering cells by integrating data modalities in multimodal single-cell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterization of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data.Availability and implementationSnapCCESS is implemented as a Python package and is freely available from https://github.com/PYangLab/SnapCCESS under the open-source license of GPL-3. The data used in this study are publicly available (see section 'Data availability').

| S-EPMC10287920 | biostudies-literature

OrganoidPortal: Web server and single-cell transcriptome database featuring reference atlases of organoids.

Project description: Not available

| S-EPMC11306279 | biostudies-literature

Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

Project description:MotivationSingle-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. In addition, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data's inherent characteristics.ResultsIn this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics.Availability and implementationImplementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https://github.com/polarisChen/GRMEC-SC.

| S-EPMC11015955 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data