Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data

Geras, Agnieszka; Shafighi, Shadi Darvish; Domżał, Kacper; Filipiuk, Igor; Rączkowski, Łukasz; Szymczak, Paulina; Toosi, Hosein; Kaczmarek, Leszek; Koperski, Lukasz; Lagergren, Jens; Nowis, Dominika; Szczurek, Ewa

doi:10.1186/s13059-023-02951-8

Cited by 9 publications

(21 citation statements)

References 62 publications

(69 reference statements)

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“…To model M st , we use the multinomial distribution with the number of trials given as the number of all cells in spot s ( N s ) and the event probability parameter H s : where H s : is deterministic and computed based on the unnormalized abundance of cell types within spot s: The three parameters: θ st , Z st and π st are defined in precisely the same manner as in Celloscope (6).…”

Section: Formulation Of the St-assign Modelmentioning

confidence: 99%

“… The proposal distributions are analogous to proposal distributions used in Celloscope (6) accept for N s and M s : variables, for which new values have to be proposed together due to the relationship between them (Equations 6 and 7).…”

Section: Formulation Of the St-assign Modelmentioning

confidence: 99%

“…We propose a three-step procedure for proposing new values of M s : and N s . First, to sample a new value for the variable N s , we choose the ceiling of the truncated normal distribution as the proposal distribution (similarly as for Celloscope (6)): where σ N controls step-size and is the value from the previous iteration. Let denote the density of ⌈ T ⌉.…”

Section: Formulation Of the St-assign Modelmentioning

confidence: 99%

“…In this case study, 137 genes were selected and modeled as markers. The marker gene sets used previously while applying Celloscope (6) were extended by incorporating genes indicated as markers in (10) for the considered cell types.…”

Section: Simulation Studymentioning

confidence: 99%

“…Nonetheless, the mini-bulk nature of ST poses the challenge of decomposing hidden cell-type mixtures within each spatial location (spot). To overcome this, we have developed Celloscope (6), specifically designed to infer the prevalence of each cell type from ST data.…”

Section: Introductionmentioning

confidence: 99%

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Joint cell type identification in spatial transcriptomics and single-cell RNA sequencing data

Geras

Szczurek

2023

Preprint

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We present ST-Assign, a novel computational tool for joint cell-type annotation in single-cell RNA sequencing and cell-type mixture decomposition in spatial transcriptomics data. The model integrates the two data sources to enhance cell-type identification. It accounts for shared variables such as gene expression profiles and leverages prior knowledge about marker genes. We formulate the model as a generative graph-based structure and employ Markov chain Monte Carlo for the inference. We demonstrate the model's utility on mouse brain data, achieving simultaneous cell-type annotation and decomposition of cell-type mixtures. ST-Assign provides valuable insights into cell populations within organisms, improving our understanding of cellular heterogeneity.

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Section: Formulation Of the St-assign Modelmentioning

confidence: 99%

Section: Formulation Of the St-assign Modelmentioning

confidence: 99%

Section: Formulation Of the St-assign Modelmentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint cell type identification in spatial transcriptomics and single-cell RNA sequencing data

Geras

Szczurek

2023

Preprint

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Navigating the landscapes of spatial transcriptomics: How computational methods guide the way

Li,

Chen,

Yang

2024

WIREs RNA

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Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single‐cell, multi‐cellular, or sub‐cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi‐modal high‐throughput data source, which poses new challenges for the development of analytical methods for data‐mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever‐evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms.This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization

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Spatial transcriptomics deconvolution at single-cell resolution using Redeconve

Zhou,

Zhong,

Zhang

et al. 2023

Nat Commun

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Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.

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Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data

Cited by 9 publications

References 62 publications

Joint cell type identification in spatial transcriptomics and single-cell RNA sequencing data

Joint cell type identification in spatial transcriptomics and single-cell RNA sequencing data

Navigating the landscapes of spatial transcriptomics: How computational methods guide the way

Spatial transcriptomics deconvolution at single-cell resolution using Redeconve

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