19 papers · ranked by Valyu relevance
Dylan Molho, Jiayuan Ding, Zhaoheng Li, Hongzhi Wen + 11 more
'Yixin Wang' 'Julian Venegas' 'Wei Jin' 'Renming Liu' 'Runze Su' 'Patrick Danaher' 'Robert Yang' 'Yu L. Lei' 'Yuying Xie' 'Jiliang Tang'] Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous…
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…
Anna Vathrakokoili Pournara, Zhichao Miao, Ozgur Yilimaz Beker, Nadja Nolte + 2 more
Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods, coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of…
Brendan F. Miller, Lyla Atta, Arpan Sahoo, Feiyang Huang + 1 more
Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type spatial co-localization patterns. We developed STdeconvolve as an unsupervised approach to deconvolve underlying cell-types comprising such…
Huang, Lin, Liu, Xiaofei + 4 more
—Accurately determining cell type composition in disease-relevant tissues is crucial for identifying disease targets. Most existing spatial transcriptomics (ST) technologies cannot achieve single-cell resolution, making it challenging to accurately determine cell types. To address this issue, various deconvolution…
Brian Nadel, David Lopez, Dennis J. Montoya, Hannah Waddel + 2 more
The cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g. cell flow cytometry) are costly, time consuming and can introduce bias. Computational approaches that infer cell type…
Brian B. Nadel, Meritxell Oliva, Benjamin L. Shou, Keith Mitchell + 7 more
Estimating cell type composition of blood and tissue samples is a biological challenge relevant in both laboratory studies and clinical care. In recent years, a number of computational tools have been developed to estimate cell type abundance using gene expression data. While these tools use a variety of approaches…
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…
Dongyue Xie, Jingshu Wang
Cell type deconvolution is a computational method that estimates the proportions of different cell types within bulk transcriptomics data by leveraging information from reference single-cell RNA sequencing data. Despite its origin as a simple linear regression model, this approach faces challenges due to technical and…
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…
Qianhui Huang, Yijun Li, Chuan Xu, Sarah A. Teichmann + 5 more
'Naftali Kaminski' 'Matteo Pellegrini' 'Quan Dong Nguyen' 'Andrew E. Teschendorff' 'Lana X. Garmire'] Deciphering cell type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach estimating cell type abundances…
Utkarsh M. Mahamune, Aldo Jongejan, Antoine H. C. van Kampen, Lisa G. M. van Baarsen + 1 more
Sequencing-based spatial transcriptomics (ST) approaches preserve spatial information but with limited cellular resolution, whereas single-cell RNA-sequencing (scRNA-seq) techniques provide single-cell resolution but lose spatial context during tissue dissociation. Given these complementary strengths, computational…
Sean K. Maden, Sang Ho Kwon, Louise A. Huuki-Myers, Leonardo Collado‐Torres + 2 more
'Leonardo Collado‐Torres' 'Stephanie C. Hicks' 'Kristen R. Maynard'] - 1. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA - 2. Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA - 3. The Solomon H. Snyder Department of Neuroscience…
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…
Wilson Kuswanto, Garry Nolan, Guolan Lu
Multiplexed imaging, which enables spatial localization of proteins and RNA to cells within tissues, complements existing multi-omic technologies and has deepened our understanding of health and disease. CODEX, a multiplexed single-cell imaging technology, utilizes a microfluidics system that incorporates DNA barcoded…
Alexander Wong, Xiaoyu 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…
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
Biomedical research often 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 cells’ properties and should be avoided, especially when purifying cells for clinical…
Juerg Straubhaar, Alexandria D’Souza, Zachary Niziolek, Bogdan Budnik
Single-cell analysis has clearly established itself in biology and biomedical fields as an invaluable tool that allows one to comprehensively understand the relationship between cells, including their types, states, transitions, trajectories, and spatial position. Scientific methods such as fluorescence labeling…