Cargando…

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumo...

Descripción completa

Detalles Bibliográficos
Autores principales:	Tran, Khoa A., Addala, Venkateswar, Johnston, Rebecca L., Lovell, David, Bradley, Andrew, Koufariotis, Lambros T., Wood, Scott, Wu, Sunny Z., Roden, Daniel, Al-Eryani, Ghamdan, Swarbrick, Alexander, Williams, Elizabeth D., Pearson, John V., Kondrashova, Olga, Waddell, Nicola
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2023
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505141/ https://www.ncbi.nlm.nih.gov/pubmed/37717006 http://dx.doi.org/10.1038/s41467-023-41385-5

Ejemplares similares

Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions
por: Andersson, Alma, et al.
Publicado: (2021)

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology
por: MacDonald, Samual, et al.
Publicado: (2023)

Efficient detection and monitoring of pediatric brain malignancies with liquid biopsy based on patient-specific somatic mutation screening
por: Kojic, Marija, et al.
Publicado: (2023)

The oesophageal adenocarcinoma tumour immune microenvironment dictates outcomes with different modalities of neoadjuvant therapy – results from the AGITG DOCTOR trial and the cancer evolution biobank
por: Lonie, James M., et al.
Publicado: (2023)

Verifying explainability of a deep learning tissue classifier trained on RNA-seq data
por: Yap, Melvyn, et al.
Publicado: (2021)

DNA barcoding reveals ongoing immunoediting of clonal cancer populations during metastatic progression and immunotherapy response
por: Baldwin, Louise A., et al.
Publicado: (2022)

Deconvolution of metal apportionment in bulk metal-organic frameworks
por: Xu, Jun, et al.
Publicado: (2022)

Deciphering Tumour Microenvironment of Liver Cancer through Deconvolution of Bulk RNA-Seq Data with Single-Cell Atlas
por: Zhang, Shaoshi, et al.
Publicado: (2022)

Criteria for selecting microhaplotypes: mixture detection and deconvolution
por: Kidd, Kenneth K, et al.
Publicado: (2015)

Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data
por: Tanner, Georgette, et al.
Publicado: (2021)

High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes
por: Singh, Mandeep, et al.
Publicado: (2019)

BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk
por: Davidson, Natalie R., et al.
Publicado: (2023)

Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors
por: Qi, Yuanyuan, et al.
Publicado: (2019)

Deconvolution of bulk blood eQTL effects into immune cell subpopulations
por: Aguirre-Gamboa, Raúl, et al.
Publicado: (2020)

Deep learning in cancer diagnosis, prognosis and treatment selection
por: Tran, Khoa A., et al.
Publicado: (2021)

Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures
por: Zaitsev, Konstantin, et al.
Publicado: (2019)

Using ICLite for deconvolution of bulk transcriptional data from mixed cell populations
por: Camiolo, Matthew J., et al.
Publicado: (2021)

Deconvolution of bulk RNA sequencing in activated phosphoinositide 3‐kinase δ syndrome
por: Huang, Xia, et al.
Publicado: (2023)

Brane singularities with mixtures in the bulk
por: Antoniadis, Ignatios, et al.
Publicado: (2012)

Comprehensive genomic and tumour immune profiling reveals potential therapeutic targets in malignant pleural mesothelioma
por: Creaney, Jenette, et al.
Publicado: (2022)

Automated deconvolution of structured mixtures from heterogeneous tumor genomic data
por: Roman, Theodore, et al.
Publicado: (2017)

RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics
por: Singh, Roopali, et al.
Publicado: (2023)

Bulk tissue cell type deconvolution with multi-subject single-cell expression reference
por: Wang, Xuran, et al.
Publicado: (2019)

FastClone is a probabilistic tool for deconvoluting tumor heterogeneity in bulk-sequencing samples
por: Xiao, Yao, et al.
Publicado: (2020)

SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references
por: Dong, Meichen, et al.
Publicado: (2020)

Computational deconvolution to estimate cell type-specific gene expression from bulk data
por: Jaakkola, Maria K, et al.
Publicado: (2021)

Likelihood-based deconvolution of bulk gene expression data using single-cell references
por: Erdmann-Pham, Dan D., et al.
Publicado: (2021)

Bulk Gene Expression Deconvolution Reveals Infiltration of M2 Macrophages in Retinal Neovascularization
por: Wang, Jiang-Hui, et al.
Publicado: (2021)

Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data
por: Andrade Barbosa, Bárbara, et al.
Publicado: (2021)

CDSeqR: fast complete deconvolution for gene expression data from bulk tissues
por: Kang, Kai, et al.
Publicado: (2021)

Simulation Investigation of Bulk and Surface Properties of Liquid Benzonitrile: Ring Stacking—Assessment and Deconvolution
por: Sakhtemanian, Leila, et al.
Publicado: (2022)

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes
por: Jeong, Yunhee, et al.
Publicado: (2022)

Deconvolution of bulk tumors into distinct immune cell states predicts colorectal cancer recurrence
por: Kim, Donghyo, et al.
Publicado: (2022)

Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes
por: Cobos, Francisco Avila, et al.
Publicado: (2023)

A mixture model for expression deconvolution from RNA-seq in heterogeneous tissues
por: Li, Yi, et al.
Publicado: (2013)

Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL)
por: Koestler, Devin C., et al.
Publicado: (2016)

NMF-Based Spectral Deconvolution with a Web Platform GC Mixture Touch
por: Zushi, Yasuyuki
Publicado: (2021)

DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference
por: Liu, Gan, et al.
Publicado: (2022)

Cell-type deconvolution of bulk RNA-Seq from kidney using opensource bioinformatic tools
por: Riojas, Angelica M., et al.
Publicado: (2023)

Cell type deconvolution of bulk blood RNA-Seq to reveal biological insights of neuropsychiatric disorders
por: Boltz, Toni, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_4i8lb2tshm9toe0deirvitvc0a