14 papers · ranked by Valyu relevance
Gabriel Torregrosa, David Oriola, Vikas Trivedi, Jordi Garcia-Ojalvo
Flow cytometry enables monitoring protein abundance and activity at the single-cell level in a high-throughput manner, through the use of fluorescent labeling. Given the significant levels of autofluorescence emitted by cells at the spectral ranges used by this technique, removing the corresponding background signal is…
Francesca Petralia, Anna P. Calinawan, Song Feng, Sara Gosline + 3 more
Characterizing the tumor microenvironment is crucial in order to improve responsiveness to immunotherapy and develop new therapeutic strategies. The fraction of different cell-types in the tumor microenvironment can be estimated based on transcriptomic profiling of bulk tumor data via deconvolution algorithms. One…
An-Shun Tai, George C. Tseng, Wen-Ping Hsieh
Gene expression deconvolution is a powerful tool for exploring the microenvironment of complex tissues comprised of multiple cell groups using transcriptomic data. Characterizing cell activities for a particular condition has been regarded as a primary mission against diseases. For example, cancer immunology aims to…
Tinyi Chu, Zhong Wang, Dana Pe’er, Charles G. Danko
Understanding the interactions between cells in their environment is a major challenge in genomics. Here we developed BayesPrism, a Bayesian method to jointly predict cellular composition and gene expression in each cell type, including heterogeneous malignant cells, from bulk RNA-seq using scRNA-seq as prior…
Parker S. Ruth, Tommy DeBenedetti, Lily O’Brien, James A. Landay + 2 more
Vascular waveforms, which measure bulk flow in blood vessels, are widely used to measure vital signs, diagnose conditions, and predict long-term health outcomes. Analyzing vascular waveforms depends on three fundamentally interdependent tasks: signal filtering, pulse timing detection, and pulse shape extraction. We…
Mengying Hu, Maria Chikina
Computational cell-type deconvolution is an important analytic technique for modeling the compositional heterogeneity of bulk gene expression data. A conceptually new Bayesian approach to this problem, BayesPrism, has recently been proposed and has subsequently been shown to be superior in accuracy and robustness…
Vimalathithan Devaraj, Biplab Bose
The expression of a gene is commonly estimated by quantitative PCR (qPCR) using RNA isolated from a large number of pooled cells. Such pooled samples often have subpopulations of cells with different levels of expression of the target gene. Estimation of gene expression from an ensemble of cells obscures the pattern of…
Saba Ghaffari, Kelly J. Bouchonville, Ehsan Saleh, Remington E. Schmidt + 2 more
Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportion of cell types within the sample. Expression deconvolution methods can help differentiate these scenarios and enable more accurate inference of gene regulation by…
Maxim Zaslavsky, Jacqueline Buros Novik, Eliza Chang, Jeffrey Hammerbacher
Robust quantification of immune cell infiltration into the tumor microenvironment may shed light on why only a small proportion of patients benefit from checkpoint therapy. The immune cells surrounding a tumor have been suggested to mediate an effective response to immunotherapy. However, traditional measurement of…
Ruoxi Sun, Evan Archer, Liam Paninski
Super-resolution microscopy methods (e.g. STORM or PALM imaging) have become essential tools in biology, opening up a variety of new questions that were previously inaccessible with standard light microscopy methods. In this paper we develop new Bayesian image processing methods that extend the reach of…
César Caballero-Gaudes, Stefano Moia, Puja Panwar, Peter A. Bandettini + 1 more
This work introduces a novel algorithm for deconvolution of the BOLD signal in multiecho fMRI data: Multiecho Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields…
Abbas Kazemipour, Behtash Babadi, Min Wu, Kaspar Podgorski + 1 more
We consider the problem of optimizing general convex objective functions with nonnegativity constraints. Using the Karush-Kuhn-Tucker (KKT) conditions for the nonnegativity constraints we will derive fast multiplicative update rules for several problems of interest in signal processing, including non-negative…
Min Guo, Yue Li, Yijun Su, Talley Lambert + 23 more
We describe theoretical and practical advances in algorithm and software design, resulting in ten to several thousand-fold faster deconvolution and multiview fusion than previous methods. First, we adapt methods from medical imaging, showing that an unmatched back projector accelerates Richardson-Lucy deconvolution by…
Lena-Marie Woelk, Sukanya A. Kannabiran, Valerie Brock, Christine E. Gee + 4 more
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 a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes subsequent image analysis.…