18 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…
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…
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…
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…
Malte Mensching-Buhr, Thomas Sterr, Dennis Völkl, Nicole Seifert + 9 more
Gene expression profiles derived from heterogeneous bulk samples contain signals from various cell populations. Cell-type deconvolution approaches are computational tools to reverse engineer the composition of bulks in term of cell populations. Accurate estimates of cell compositions are crucial for identifying cell…
Malte Mensching-Buhr, Thomas Sterr, Nicole Seifert, Dennis Völkl + 7 more
Gene expression profiles of heterogeneous bulk samples contain signals from multiple cell populations. Studying variations in their composition can help to identify cell populations relevant for disease. Moreover, analyses, such as the identification of differentially expressed genes, can be confounded by cellular…
Anna Vathrakokoili Pournara, Zhichao Miao, Ozgur Yilimaz Beker, Nadja Nolte + 3 more
There is a growing interest in understanding the level of heterogeneity and the importance of cell-type abundances in healthy and diseased tissues. Transcriptomic heterogeneity and cell-type composition reveal critical features of tissue functionality. For example, in cancer, immune cells can either be recruited in the…
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…
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…
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…
Yingying Lu, Qin Chen, Lingling An
The advent of spatial transcriptomics technology has allowed for the acquisition of gene expression profiles with multi-cellular resolution in a spatially resolved manner, presenting a new milestone in the field of genomics. However, the aggregate gene expression from heterogeneous cell types obtained by these…
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…
Guanqun Meng, Yue Pan, Wen Tang, Lijun Zhang + 8 more
Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study…
Jiadong Mao, Jarny Choi, Kim-Anh Lê Cao
We introduce Φ-Space ST, a platform-agnostic method to identify continuous cell states in spatial transcriptomics (ST) data using multiple scRNA-seq references. For ST with supercellular resolution, Φ-Space ST achieves interpretable cell type deconvolution with significantly faster computation. For subcellular…
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…
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…