Emerging artificial intelligence applications in Spatial Transcriptomics analysis

Li, Yijun; Stanojevic, Stefan; Garmire, Lana X.

doi:10.1016/j.csbj.2022.05.056

Cited by 15 publications

(12 citation statements)

References 100 publications

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“…In the data types with spatial measurements, the spatial relationships need to be considered in addition to the feature matrix. We refer readers to other recent reviews covering this area [84] , [85] . Given that the multi-omics integration and alignment computational research is a thriving area, we have created an open review document online using the manubot protocol ( https://github.com/lanagarmire/multiomics_review_manubot ; accessed on July 18, 2022).…”

Section: Discussionmentioning

confidence: 99%

Computational Methods for Single-Cell Multi-Omics Integration and Alignment

Stanojevic

Ristivojevic

et al. 2022

Genomics, Proteomics &Amp; Bioinformatics

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Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology. Multi-omics assays offer even greater opportunities to understand cellular states and biological processes. The problem of integrating different omics data with very different dimensionality and statistical properties remains, however, quite challenging. A growing body of computational tools is being developed for this task, leveraging ideas ranging from machine translation to the theory of networks, and represents another frontier on the interface of biology and data science. Our goal in this review is to provide a comprehensive, up-to-date survey of computational techniques for the integration of single-cell multi-omics data, while making the concepts behind each algorithm approachable to a non-expert audience.

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Section: Discussionmentioning

confidence: 99%

Computational Methods for Single-Cell Multi-Omics Integration and Alignment

Stanojevic

Ristivojevic

et al. 2022

Genomics, Proteomics &Amp; Bioinformatics

Self Cite

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“…Techniques based on machine learning (ML) principles have been widely used to segment or cluster spatial gene expression datasets 11,15,[19][20][21][22][23][24][25][26][27][28][29][30][31] . These segmentation and clustering techniques include but are not limited to k-nearest neighbors, hierarchical clustering, spectral clustering, and deep learning based methods.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, segmentation and clustering are the two main categories of machine learning approaches in the analysis of spatial gene expression data [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] . While these methods yield a set of spatially nonoverlapping or in some cases overlapping regions, the problem formulation focuses on local information and does not explicitly model the global structure of the entire gene expression data.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised pattern discovery in spatial gene expression atlas reveals mouse brain regions beyond established ontology

Cahill

Wang

Lee

et al. 2023

Preprint

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The growth of large-scale spatial gene expression data requires new computational tools to extract major trends in gene expression in their native spatial context. Here, we describe an unsupervised and interpretable computational framework to (1) pre-process 3D spatial gene expression datasets by imputation of missing voxels, (2) identify principal patterns (PPs) of 3D spatial gene expression profiles using the stability-driven non-negative matrix factorization (staNMF) technique, and (3) systematically compare these PPs to known anatomical regions and ontology. This framework, referred to as osNMF (ontology discovery via staNMF), identifies PPs that are derived purely from thousands of 3D spatial gene expression profiles in the Allen Mouse Brain Atlas. These 3D PPs present stable and spatially coherent regions of the mouse brain, potentially without human labor and bias. We demonstrate that osNMF PPs offer new brain patterns that are highly correlated with combinations of expert-annotated brain regions, while also identifying a unique ontology based purely on spatial gene expression data. Compared to principal component analysis (PCA) and other clustering algorithms, our PPs exhibit better spatial coherence, more accurately match expert labeling and are more stable across multiple bootstrapped simulations. We also used osNMF to define marker genes and build putative spatial gene interaction networks. Our findings highlight the capability of osNMF to rapidly generate new atlases from a large set of spatial gene expression data without supervision and uncover novel relationships between brain regions that were difficult to discern using conventional manual approaches.

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“…As these methods were recently published in the last three years and continuously evolving, their performance has not yet been comprehensively benchmarked. While there exist general reviews on various aspects of spatial transcriptomics analysis [20][21][22] , they have only surveyed and categorised methods predicting SGE from H&E without any benchmarking.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking the translational potential of spatial gene expression prediction from histology

Chan,

Wang,

et al. 2023

Preprint

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Spatial transcriptomics has enabled the quantification of gene expression at spatial coordinates, offering crucial insights into molecular underpinnings of diseases. In light of this, several methods predicting spatial gene expression from paired histology images have offered the opportunity of enhancing the utility of readily obtainable and cost-effective haematoxylin-and-eosin-stained histology images. To this end, we conducted a comprehensive benchmarking study encompassing six developed methods. These methods were reproduced and evaluated using HER2-positive breast tumour and human cutaneous squamous cell carcinoma datasets, followed by external validation using The Cancer Genome Atlas data. Our evaluation incorporates diverse metrics which capture the performance of predicted gene expression, model generalisability, translational potential, usability and computational efficiency of each method. Our findings demonstrate the capacity of methods to spatial gene expression from histology and highlight key areas that can be addressed to support the advancement of this emerging field.

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Emerging artificial intelligence applications in Spatial Transcriptomics analysis

Cited by 15 publications

References 100 publications

Computational Methods for Single-Cell Multi-Omics Integration and Alignment

Computational Methods for Single-Cell Multi-Omics Integration and Alignment

Unsupervised pattern discovery in spatial gene expression atlas reveals mouse brain regions beyond established ontology

Benchmarking the translational potential of spatial gene expression prediction from histology

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