SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning

SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning

SpaDecon is a semi-supervised learning-based method for cell-type deconvolution of spatially resolved transcriptomics (SRT) data that is also computationally fast and memory efficient for large-scale SRT studies.

Bibliographic Details
Main Authors:	Kyle Coleman, Jian Hu, Amelia Schroeder, Edward B. Lee, Mingyao Li
Format:	Article
Language:	English
Published:	Nature Portfolio 2023-04-01
Series:	Communications Biology
Online Access:	https://doi.org/10.1038/s42003-023-04761-x

Similar Items

DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data
by: Erica A.K. DePasquale, et al.
Published: (2019-11-01)

GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes
by: Lakshmipuram Seshadri Swapna, et al.
Published: (2023-08-01)

spa: Semi-Supervised Semi-Parametric Graph-Based Estimation in R
by: Mark Culp
Published: (2011-04-01)

An <i>R</i>-Package for the Deconvolution and Integration of 1D NMR Data: MetaboDecon1D
by: Martina Häckl, et al.
Published: (2021-07-01)

About Decon'21
by: Kadriye Funda Akaltan
Published: (2021-12-01)

SpatialDWLS: accurate deconvolution of spatial transcriptomic data
by: Rui Dong, et al.
Published: (2021-05-01)

Deconvolution Tactics and Normalization in Renal Spatial Transcriptomics
by: Ricardo Melo Ferreira, et al.
Published: (2022-01-01)

DeconPeaker, a Deconvolution Model to Identify Cell Types Based on Chromatin Accessibility in ATAC-Seq Data of Mixture Samples
by: Huamei Li, et al.
Published: (2020-06-01)

Erratum: DeconPeaker, a Deconvolution Model to Identify Cell Types Based on Chromatin Accessibility in ATAC-Seq Data of Mixture Samples
by: Frontiers Production Office
Published: (2020-06-01)

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST
by: Yahui Long, et al.
Published: (2023-03-01)

Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome
by: Yingkun Zhang, et al.
Published: (2023-01-01)

Spatial transcriptomics deconvolution at single-cell resolution using Redeconve
by: Zixiang Zhou, et al.
Published: (2023-12-01)

DECON 2006 : design excellence for competitive manufacturing /
by: National Conference on Design and Concurrent Engineering (2006 : Kuala Lumpur), et al.
Published: (2006)

TumorDecon: A digital cytometry software
by: Rachel A. Aronow, et al.
Published: (2022-06-01)

Protocol for Identification and Removal of Doublets with DoubletDecon
by: Erica A.K. DePasquale, et al.
Published: (2020-09-01)

A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics
by: Haoyang Li, et al.
Published: (2023-03-01)

Multi-slice spatial transcriptome domain analysis with SpaDo
by: Bin Duan, et al.
Published: (2024-03-01)

SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics
by: Zhiyuan Liu, et al.
Published: (2023-08-01)

Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry
by: Qihuang Zhang, et al.
Published: (2023-07-01)

Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data
by: Patrick Danaher, et al.
Published: (2022-01-01)

Semi-supervised integration of single-cell transcriptomics data
by: Massimo Andreatta, et al.
Published: (2024-01-01)

Geostatistical semi-supervised learning for spatial prediction
by: Francky Fouedjio, et al.
Published: (2022-12-01)

Acoustical Semi-blind Deconvolution for Bearing Defect Detection
by: Wang Yu, et al.
Published: (2016-01-01)

Regularized supervised Bayesian approach for image deconvolution with regularization parameter estimation
by: Bouchra Laaziri, et al.
Published: (2020-04-01)

The DECON pilot project investigates predictive markers for successful bariatric surgery
by: Gabriel Seifert, et al.
Published: (2023-08-01)

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration
by: Wang, Z, et al.
Published: (2018)

Benchmarking of cell type deconvolution pipelines for transcriptomics data
by: Francisco Avila Cobos, et al.
Published: (2020-11-01)

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration
by: Zeya Wang, et al.
Published: (2018-11-01)

Multivariate semi-blind deconvolution of fMRI time series
by: Hamza Cherkaoui, et al.
Published: (2021-11-01)

Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data
by: Agnieszka Geras, et al.
Published: (2023-05-01)

Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data
by: Brendan F. Miller, et al.
Published: (2022-04-01)

scSemiAE: a deep model with semi-supervised learning for single-cell transcriptomics
by: Jiayi Dong, et al.
Published: (2022-05-01)

Sol-Gel preperation of 58S and 77S bioactive glass based on decon surfactant
by: Tan, Shu Er
Published: (2015)

Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics
by: Raik Otto, et al.
Published: (2023-02-01)

Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data
by: Anderson Gordon A, et al.
Published: (2009-03-01)

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk
by: Xin Shao, et al.
Published: (2022-07-01)

Preface for the special issue of International Conference on Design and Concurrent Engineering (iDECON2015)
by: Teruaki ITO
Published: (2016-10-01)

Pretreatment method for hypochlorite decon water before GC analysis of HD, VX, and GD
by: Mengxue Xu, et al.
Published: (2021-03-01)

Deconvolution /
by: Ziolkowski, Anton, author 354615
Published: (1984)

Deconvolution reveals cell-type-specific transcriptomic changes in the aging mouse brain
by: Yingxue Ren, et al.
Published: (2023-10-01)