13 papers · ranked by Valyu relevance
Chen Qiao, Yuanhua Huang
Imputation of missing features in spatial transcriptomics is urgently demanded due to technology limitations, while most existing computational methods suffer from moderate accuracy and cannot estimate the reliability of the imputation. To fill the research gaps, we introduce a computational model, TransImp, that…
Xinyu Zhou, Pengtao Dang, Xiao Wang, Laura Xianlu Peng + 7 more
Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general statistical framework that…
Ali Punjani, Haowei Zhang, David J. Fleet
Single particle cryo-EM is a powerful method for studying proteins and other biological macromolecules. Many of these molecules comprise regions with varying structural properties including disorder, flexibility, and partial occupancy. These traits make computational 3D reconstruction from 2D images challenging.…
Xinyu Zhou, Pengtao Dang, Haixu Tang, Laura Xianlu Peng + 6 more
Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general framework that estimates…
Zhuliu Li, Tianci Song, Jeongsik Yong, Rui Kuang
High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. One major limitation of in-situ capturing is the high dropout rate of…
Jiayu Su, Jean-Baptiste Reynier, Xi Fu, Guojie Zhong + 7 more
Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses.…
Anwar O. Nunez-Elizalde, Alexander G. Huth, Jack L. Gallant
Predictive models for neural or fMRI data are often fit using regression methods that employ priors on the model parameters. One widely used method is ridge regression, which employs a spherical Gaussian prior that assumes equal and independent variance for all parameters. However, a spherical prior is not always…
Marta Karas, Damian Brzyski, Mario Dzemidzic, Joaquin Goni + 3 more
A challenging problem arising in brain imaging research is principled incorporation of information from different imaging modalities. Frequently each modality is analyzed separately using, for instance, dimensionality reduction techniques which result in a loss of mutual information. We propose a novel regularization…
Yong Bai, Xiangyu Guo, Keyin Liu, Qiuhong Luo + 6 more
Recent improvements in spatial transcriptomics technologies have enabled the characterization of complex cellular mechanisms within tissue context through unbiased profiling of genome-wide transcriptomes in conjunction with spatial coordinates. These technologies require a systematic analysis approach to deciphering…
Manik Kuchroo, Danielle F. Miyagishima, Holly R. Steach, Abhinav Godavarthi + 10 more
Biological networks operate within architectural frameworks that influence the state and function of cells through niche-specific factors such as exposure to nutrients and metabolites, soluble signaling molecules, and direct cognate cell-cell communication. Spatial omics technologies incorporate environmental…
Maximilian Woollard, Pratibha Panwar, Luke T.G. Harland, Jeremy Mo + 5 more
With the dramatic take-up of spatially resolved transcriptomics biotechnologies, performing spatially-aware analysis of the resulting data is crucial to maximise advances in biological understanding. Dimensionality reduction is a first step in almost any analysis of spatial transcriptomics data, regardless of whether…
Sikta Das Adhikari, Nina G. Steele, Brian Theisen, Jianrong Wang + 1 more
Recent advances in spatial transcriptomics have significantly deepened our understanding of biology. A primary focus has been identifying spatially variable genes (SVGs) which are crucial for downstream tasks like spatial domain detection. Traditional methods often use all or a set number of top SVGs for this purpose.…
Qi Liu, Chih-Yuan Hsu, Yu Shyr
The expeditious growth in spatial omics technologies enable profiling genome-wide molecular events at molecular and single-cell resolution, highlighting a need for fast and reliable methods to characterize spatial patterns. We developed SpaGene, a model-free method to discover any spatial patterns rapidly in large…