Spage2vec: Unsupervised detection of spatial gene expression constellations

Partel, Gabriele; Wählby, Carolina

doi:10.1101/2020.02.12.945345

Cited by 3 publications

(2 citation statements)

References 31 publications

(23 reference statements)

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“…TissUUmaps also offers the possibility to visualize more complex features as spatial gene constellation learned by spage2vec ( tissuumaps.research.it.uu.se/spage2vec ) from Partel and Wählby (2020) , representing different subcellular spatial domains involved in cellular differentiation. Another example is presented in Ström et al (2020) where H&E slides of prostate biopsies are synchronized with a prediction map, displayed using WebGL shaders ( tissuumaps.research.it.uu.se/STHLM3 ).…”

Section: Discussionmentioning

confidence: 99%

TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data

2020

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Motivation Visual assessment of scanned tissue samples and associated molecular markers, such as gene expression, requires easy interactive inspection at multiple resolutions. This requires smart handling of image pyramids and efficient distribution of different types of data across several levels of detail. Results We present TissUUmaps, enabling fast visualization and exploration of millions of data points overlaying a tissue sample. TissUUmaps can be used both as a web service or locally in any computer, and regions of interest as well as local statistics can be extracted and shared among users. Availability and implementation TissUUmaps is available on github at github.com/wahlby-lab/TissUUmaps. Several demos and video tutorials are available at http://tissuumaps.research.it.uu.se/howto.html. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data

2020

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“…Spatial transcriptomics techniques have now been applied to study several different organs and tissues including lung [9], kidney [10] and brain [5,6,7,8,11]. These studies have led to new insights about the set of cell types in these regions, their location and their interactions [12,13,14] A key question in the analysis of single cell expression data (both for scRNA-Seq and for spatial transcriptomics) is the assignment of cell types. This is often the essential task performed in any analysis of such data and downstream analysis often relies on these assignments (for example, when studying cell-cell interactions [13,15]).…”

Section: Introductionmentioning

confidence: 99%

Cell Type Assignments for Spatial Transcriptomics Data

Teng

Yuan

Bar‐Joseph

2021

Preprint

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Motivation: Recent advancements in fluorescencein situhybridization (FISH) techniques enable themto concurrently obtain information on the location and gene expression of single cells. A key question inthe initial analysis of such spatial transcriptomics data is the assignment of cell types. To date, moststudies used methods that only rely on the expression levels of the genes in each cell for such assignments.To fully utilize the data and to improve the ability to identify novel sub-types we developed a newmethod, FICT, which combines both expression and neighborhood information when assigning cell types. Results: FICT optimizes a probabilistic function that we formalize and for which we provide learningand inference algorithms. We used FICT to analyze both simulated and several real spatial transcrip-tomics data. As we show, FICT can accurately identify cell types and sub-types improving on expressiononly methods and other methods proposed for clustering spatial transcriptomics data. Some of thespatial sub-types identified by FICT provide novel hypotheses about the new functions for excitatoryand inhibitory neurons.

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Machine learning for cell classification and neighborhood analysis in glioma tissue

Solorzano

Wik

Bontell

et al. 2021

Preprint

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Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell-cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel. However, due to variations in sample handling and artefacts from staining and scanning, cells of the same type may present different marker profiles both within and across samples. We address multiplexed immunofluorescence data from tissue microarrays of low grade gliomas and present a methodology using two different machine learning architectures and features insensitive to illumination to perform cell classification. The fully automated cell classification provides a measure of confidence for the decision and requires a comparably small annotated dataset for training, which can be created using freely available tools. Using the proposed method, we reached an accuracy of 83.5% on cell classification without the need for standardization of samples. Using our confidence measure, cells with low-confidence classifications could be excluded, pushing the classification accuracy to 94.47%. Next, we used the cell classification results to search for cell niches with an unsupervised learning approach based on graph neural networks. We show that the approach can re-detect specialized tissue niches in previously published data, and that our proposed cell classification leads to niche definitions that may be relevant for sub-groups of glioma, if applied to larger datasets.

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Spage2vec: Unsupervised detection of spatial gene expression constellations

Cited by 3 publications

References 31 publications

TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data

TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data

Cell Type Assignments for Spatial Transcriptomics Data

Machine learning for cell classification and neighborhood analysis in glioma tissue

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