Alignment and Integration of Spatial Transcriptomics Data

Zeira, Ron; Land, Max; Raphael, Benjamin J.

doi:10.1101/2021.03.16.435604

Cited by 9 publications

(11 citation statements)

References 51 publications

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“…As one of the first efforts in this direction, Probabilistic Alignment of Spatial Transcriptomics Experiments (PASTE) (Zeira et al, 2021) was developed to align adjacent tissue slices in Spatial Transcriptomics (ST) data (Ståhl et al, 2016). PASTE uses an optimal transport framework to identify mappings between the spatial locations of adjacent slices.…”

Section: Methods For Spatial Genomics Slice Alignmentmentioning

confidence: 99%

“…We fit GPSA to each of these datasets using a template-based alignment with the first slice as the template, repeating the experiment five times for each condition. For comparison, we ran PASTE (Zeira et al, 2021) and extracted the aligned coordinates using the estimated linear transformation from PASTE. For each method, we computed the error of the aligned coordinates.…”

Section: Robustness Of Gpsa To Observation Noisementioning

confidence: 99%

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Alignment of spatial genomics and histology data using deep Gaussian processes

Jones

Townes

et al. 2022

Preprint

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Spatially-resolved genomic technologies have allowed us to study the physical organization of cells and tissues, and promise an understanding of the local interactions between cells. However, it remains difficult to precisely align spatial observations across slices, samples, scales, individuals, and technologies. Here, we propose a probabilistic model that aligns a set of spatially-resolved genomics and histology slices onto a known or unknown common coordinate system into which the samples are aligned both spatially and in terms of the phenotypic readouts (e.g., gene or protein expression levels, cell density, open chromatin regions). Our method consists of a two-layer Gaussian process: the first layer maps the observed samples' spatial locations into a common coordinate system, and the second layer maps from the common coordinate system to the observed readouts. Our approach also allows for slices to be mapped to a known template coordinate space if one exists. We show that our registration approach enables complex downstream spatially-aware analyses of spatial genomics data at multiple resolutions that are impossible or inaccurate with unaligned data, including an analysis of variance, differential expression across the z-axis, and association tests across multiple data modalities.

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Section: Methods For Spatial Genomics Slice Alignmentmentioning

confidence: 99%

Section: Robustness Of Gpsa To Observation Noisementioning

confidence: 99%

Alignment of spatial genomics and histology data using deep Gaussian processes

Jones

Townes

et al. 2022

Preprint

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“…That is, to find a translation vector and a rotation matrix that minimize the weighted distances between matched spots where represents the spot coordinate matrix of the k th layer. The problem was solved by using SVD (see PASTE 27 for more details). For Figure 6a, only three tissue sections, sample 8, 9 and 10 were displayed to visualize the alignment and integration result.…”

Section: Methodsmentioning

confidence: 99%

STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing

Sun

Xiao

Liu

et al. 2021

Preprint

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The recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell-types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrate STRIDE's utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell-types to spatial locations but also improved the identification of spatial localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions, and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/DongqingSun96/STRIDE.

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“…Effective modeling of such data requires new methods that can account for spatial dependency of gene expression in the 3D space, gene expression variability driven by spatial differences associated with biological variables such as sex, age, race, and body size. To account for these factors, the gene expression data from tissue sections across different individuals need to be registered to a common coordinate framework [75] , [76] . Once the gene expression data are registered, methods that can account for variations across space and individuals can be utilized to identify marker genes that define the transcriptional landmarks.…”

Section: Outlook and Future Research Directionsmentioning

confidence: 99%