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09 Celltype deconvolution overview
Sano Seminars – Spatially aware approach for cell-type deconvolution in spatial transcriptomics data
570-BayesPrism: Cell Type and Gene Expression Deconvolution
9 Visium data: Identifying cell types using deconvolution
12. Segmentation-free inference of cell types from in situ transcriptomics data
Brian White - The CIBERSORTx deconvolution algorithm
Jean Fan: Computational Tools for Spatially Resolved Transcriptomic Data Analysis
Live R Coding Session - Spatial transcriptomics data analysis with STdeconvolve and SpotClean
Spatial Transcriptomics
Spatial Transcriptomics Data Deconvolution with cell2location in Python
[2020-10-22] Strategies for cellular deconvolution in human brain RNA sequencing data
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Last Updated: September 28, 2026
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I'm learning how to give + record my scientific talks from home. This video is an abbreviated version of invited scientific talks I have ... Alma Andersson, MSc Bioinformatician Department of Gene Technology, KTH SciLifeLab, Stockholm, Sweden Single Ying Ma, from University of Michigan, Ann Arbor, about her Nature Biotechnology paper, "Spatially informed I'm trying out different video styles to teach students about bioinformatics analyses for spatially resolved Alma Andersson, PhD Bioinformatician Department of Gene Technology, KTH SciLifeLab, Stockholm, Sweden Video editing: ... Marcin Wierzbiński, "Spatially aware approach for Researchers have developed a new statistical tool called BayesPrism to better understand the complex tumor microenvironment ... This lecture by Jeongbin Park (Charité-Universitätsmedizin Berlin & de.NBI, Germany) is part of the course "Single Talk 6.3 from the ERCC's April 2021 exRNA North West Seminar Series of Mathematical Biology and I record myself coding in the R programming language to perform an exploratory bioinformatics analysis of multi- Spatial Transcriptomics Data Deconvolution For more information about the materials discussed in this video, check doi.org/10.1101/2020.01.19.910976. To ...
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