16 papers · ranked by Valyu relevance
Pratibha Panwar, Boyi Guo, Haowen Zhou, Stephanie C. Hicks + 1 more
The increased uptake of high-resolution spatially-resolved transcriptomics (SRT) technologies demands the development of unsupervised methods to extract cell types and their spatial distribution from biological tissues. However, unsupervised clustering is challenging due to the sparsity of the data and the differences…
Yinqiao Yan, Xiangnan Feng, Xiangyu Luo
The spatial transcriptomics (ST) clustering plays a crucial role in elucidating the tissue spatial heterogeneity, which is referred to as region segmentation for spot level ST data and spatial cell typing for single-cell ST data. An accurate ST clustering result can greatly benefit downstream biological analyses. As…
Axel Andersson, Andrea Behanova, Christophe Avenel, Jonas Windhager + 2 more
Imaging-based spatial transcriptomics techniques generate image data that, once processed, results in a set of spatial points with categorical labels for different mRNA species. A crucial part of analyzing downstream data involves the analysis of these point patterns. Here, biologically interesting patterns can be…
Hongyan Cao, Gaiqin Liu, Jingyi Xia, Runle Chen + 8 more
Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we…
Vipul Singhal, Nigel Chou, Joseph Lee, Jinyue Liu + 7 more
Each cell type in a solid tissue has a characteristic transcriptome and spatial arrangement, both of which are observable using modern spatial omics assays. However, the common practice is still to ignore spatial information when clustering cells to identify cell types. In fact, spatial location is typically considered…
Edward Zhao, Matthew R. Stone, Xing Ren, Thomas Pulliam + 3 more
Recently developed spatial gene expression technologies such as the Spatial Transcriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the…
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.…
Huimin Li, Xi Jiang, Lei Guo, Yang Xie + 2 more
Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many…
Joao C. Marques, Michael B. Orger
How to partition a data set into a set of distinct clusters is a ubiquitous and challenging problem. The fact that data sets vary widely in features such as cluster shape, cluster number, density distribution, background noise, outliers and degree of overlap, makes it difficult to find a single algorithm that can be…
Carter Allen, Yuzhou Chang, Brian Neelon, Won Chang + 4 more
High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek…
Niklas Kleinenkuhnen, David Köhler, Till Baar, Chrysa Nikopoulou + 4 more
We introduce a multivariate statistical approach for pattern recognition in spatial transcriptomics data. Our algorithm (SPACO) constructs a low-dimensional projection of the data maximising Moran’s I, which mitigates non-spatial variation and outperforms PCA for pre-processing. Our method also provides a calibrated…
Adnane Nemri, Ovidiu Radulescu, Antoine Claessens, Thomas D. Otto
Despite the advent of spatial data science, including spatial biology, there exist few methods that study the distribution of points e.g. cells or individuals, accounting for both their own characteristics and environmental factors. We propose a new spatial entropy measure, termed the Regional Co-occurrence Entropy…
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…
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…
Evangelos Karatzas, Maria Gkonta, Joana Hotova, Fotis A. Baltoumas + 4 more
Clustering is the process of grouping together different data objects based on similar properties. Clustering has applications in various case studies from several fields such as graph theory, image analysis, pattern recognition, statistics and others. Nowadays, there are numerous algorithms and tools able to generate…
Polina Bombina, Dwayne Tally, Zachary B. Abrams, Kevin R. Coombes
Unsupervised clustering is an important task in biomedical science. We developed a new clustering method, called SillyPutty, for unsupervised clustering. As test data, we generated a series of datasets using the Umpire R package. Using these datasets, we compared SillyPutty to several existing algorithms using multiple…