16 papers · ranked by Valyu relevance
Francisco Avila Cobos, José Alquicira-Hernandez, Joseph E. Powell, Pieter Mestdagh + 1 more
'Pieter Mestdagh' 'Katleen De Preter'] Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results…
Yunlu Chen, Feng Ruan, Ji-Ping Wang, Christina Kendziorski
Spatial transcriptomics (ST), crowned the Method of the Year in 2020 (), has revolutionized biomedical research by allowing quantification of the mRNA expression of a large number of genes simultaneously in the spatial context of the tissue. The sequencing-based ST technology, while achieving its popularity for spatial…
Jahanzeb Saqib, Junil Kim
Spatial transcriptomics technologies have significantly enhanced the analysis of gene expression profiles by retaining the spatial information of intact tissue sections and enabling the possibility of a more profound comprehension of tissue structures and cellular relationships. Despite this, most platforms have…
Maria K Jaakkola, Laura L Elo
Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses…
Nicholas K. O’Neill, Thor D. Stein, Junming Hu, Habbiburr Rehman + 4 more
'Joshua D. Campbell' 'Masanao Yajima' 'Xiaoling Zhang' 'Lindsay A. Farrer'] Background Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell…
Robin Khatri, Pierre Machart, Stefan Bonn
Cell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data. An unmet challenge in cell deconvolution is the scarcity of realistic training data and the domain shift often observed in synthetic training data. Here, we show that two novel deep neural networks with…
Wei Zhang, Xianglin Zhang, Qiao Liu, Lei Wei + 5 more
'Zhiping Liu' 'Xiaowo Wang' 'Yuchao Jiang'] Title: Abstract In recent years, computational methods for quantifying cell-type proportions from transcription data have gained significant attention, particularly those reference-based methods which have demonstrated high accuracy. However, there is currently a lack of…
Songjian Lu, Jiyuan Yang, Lei Yan, Jingjing Liu + 3 more
'Rhea Jain' 'Jiyang Yu'] The variation of transcriptome size across cell types significantly impacts single-cell RNA sequencing (scRNA-seq) data normalization and bulk RNA-seq cellular deconvolution, yet this intrinsic feature is often overlooked. Here we introduce ReDeconv, a computational algorithm that incorporates…
Li Dong, Avinash Kollipara, Toni Darville, Fei Zou + 1 more
Deconvolution of bulk transcriptomics data from mixed cell populations is vital to identify the cellular mechanism of complex diseases. Existing deconvolution approaches can be divided into two major groups: supervised and unsupervised methods. Supervised deconvolution methods use cell type-specific prior information…
Lulu Yan, Xiaoqiang Sun, Janet Kelso
Accuracy. We compared the accuracy of different deconvolution methods across metrics and datasets. We found that the accuracy of each of the 14 methods was generally quite stable across the three metrics, but the accuracy of some methods (i.e. stereoscope, STdeconvolve and Giotto/rank) varied depending on datasets…
Alexander Wong, Xiao Yu Wang, Maud Gorbet
Fluorescence microscopy is widely used for the study of biological specimens. Deconvolution can significantly improve the resolution and contrast of images produced using fluorescence microscopy; in particular, Bayesian-based methods have become very popular in deconvolution fluorescence microscopy. An ongoing…
Ana Cayuela López, José A. Gómez-Pedrero, Ana M. O. Blanco, Carlos Oscar S. Sorzano
Fluorescence microscopy techniques have experienced a substantial increase in the visualization and analysis of many biological processes in life science. We describe a semiautomated and versatile tool called Cell-TypeAnalyzer to avoid the time-consuming and biased manual classification of cells according to cell…
M Laasmaa, M Vendelin, P Peterson
Although confocal microscopes have considerably smaller contribution of out-of-focus light than widefield microscopes, the confocal images can still be enhanced mathematically if the optical and data acquisition effects are accounted for. For that, several deconvolution algorithms have been proposed. As a practical…
Lena-Marie Woelk, Sukanya A. Kannabiran, Valerie J. Brock, Christine E. Gee + 5 more
'Christine E. Gee' 'Christian Lohr' 'Andreas H. Guse' 'Björn-Philipp Diercks' 'René Werner' 'Demetrios A. Arvanitis'] Live-cell Ca $(2+)$ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to…
Sarah De Beuckeleer, Tim Van De Looverbosch, Johanna Van Den Daele, Peter Ponsaerts + 3 more
'Peter Ponsaerts' 'Winnok H De Vos' 'Assaf Zaritsky' 'Felix Campelo'] Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption…
Maik Herbig, Karen Tessmer, Martin Nötzel, Ahsan Ahmad Nawaz + 5 more
'Tiago Santos-Ferreira' 'Oliver Borsch' 'Sylvia J. Gasparini' 'Jochen Guck' 'Marius Ader'] Biomedical research relies on identification and isolation of specific cell types using molecular biomarkers and sorting methods such as fluorescence or magnetic activated cell sorting. Labelling processes potentially alter the…