cell type assignments for spatial transcriptomics data

A key question in the initial analysis of such spatial transcriptomics data is the assignment of cell types. To date, most studies 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 new method, FICT ...

To address the challenges presented by spatial annotation, we propose the use of a probabilistic model, SpatialAnno, which performs cell/domain-type assignments for SRT data and has the capability of leveraging non-marker genes to assign cell/domain types via a factor model while accounting for spatial information via a Potts model (16, 17). To ...

Abstract. In the analysis of both single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data, classifying cells/spots into cell/domain types is an essential analytic step for many secondary analyses. Most of the existing annotation methods have been developed for scRNA-seq datasets without any consideration of ...

Polygons indicate the region of the corresponding cell type layer, and the color represents cell types. d Top: spatial cell locations identified as L4, L5 IT, L6b/L6 CT/L6 IT, Oligo, and VLMC by ...

Learn how Spatial-ID, a novel cell typing method, integrates single-cell RNA-seq data and spatial information of transcriptomics to annotate cell types in mouse brain.

For instance, if a spot was labeled as "Immune cell" by the pathologist, cell type assignments of "T cell," "Dendritic cell," or "B cell" would all be ... we repurposed the existing scType tool to annotate cell types from spatial transcriptomics data and benchmarked its performance against existing spatial cell type annotation ...

A key question in the initial analysis of such spatial transcriptomics data is the assignment of cell types. To date, most studies 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 new method, FICT ...

Overall, this analysis shows that a spot-based approach, such as SSAM, is equally capable of revealing good-quality segmentation-free cell type assignment for spatial transcriptomics pixel data ...

The recent development of spatially resolved transcriptomics (SRT) technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data.

Cell-type modeling in spatial transcriptomics data elucidates spatially variable colocalization and communication between cell-types in mouse brain Francisco Jose Grisanti Canozo,1,2 Zhen Zuo,1 James F. Martin,1,2 and Md. Abul Hassan Samee1,3,* 1Baylor College of Medicine, Houston, TX 77030, USA 2Texas Heart Institute, Houston, TX 77030, USA ...

Panpipes also includes four workflows dedicated to spatial transcriptomics, including: "Ingestion," "Preprocessing," "Clustering," and "Deconvolution" (Fig. 1).The unifying aim across these workflows is to guide the user through the key decision-making steps of the analytical process and to gather all the data necessary to annotate cell types and states.

We developed a neural network model, spatial transcriptomics cell-types assignment using neural networks (STANN), to overcome these challenges. Analysis of STANN's predicted cell types in mouse olfactory bulb (MOB) sc-ST data delineated MOB architecture beyond its morphological layer-based conventional description. We find that cell-type ...

the initial analysis of such spatial transcriptomics data is the assignment of cell t ypes. To date, most. studies used methods that only rely on the expression levels of the genes in each cell ...

Since scRNA-seq profiles the transcriptome, including the marker genes, we can assign cell types unambiguously to the cells in scRNA-seq data. Using these cell-type assignments in the scRNA-seq data, we can build models that learn to map a cell to its cell type, i.e., classify the cell, given the transcriptional expression of only the shared ...

Realizing the critical importance of cell-type decomposition, multiple groups have developed ST deconvolution methods. The aim of this work is to review state-of-the-art methods for ST deconvolution, comparing their strengths and weaknesses. In particular, we construct ST spots from single-cell level ST data to assess the performance of 10 ...

Recently, several methods have been developed for annotating spatial transcriptomics data [5,6,7,8,9,10]. These methods often start by clustering cells. ... For the MERFISH data, cell-type assignment is performed by Louvain community detection applied to a neighborhood graph which is constructed using low-dimension representation of gene ...

A spatial transcriptomics experiment results in a map of a biological tissue, along with associated information about the locations on that map (the expression of a range of genes). Typically, this information is analyed as regions, which could be anything from a single cell to a large multicellular section of tissue.

Since many single-cell RNA-seq (scRNA-seq) data are obtained after cell sorting, such as when investigating immune cells, tracking cellular landscape by integrating single-cell data with spatial transcriptomic data is limited due to cell type and cell composition mismatch between the two datasets. We developed a method, spSeudoMap, which utilizes sorted scRNA-seq data to create virtual cell ...

The framework we propose uses single-cell data to infer proportion estimates of each cell type at every capture location within the spatial data, eliminating any need for interpretation or ...

In the analysis of both single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data, classifying cells/spots into cell/domain types is an essential analytic step for many secondary analyses. Most of the existing annotation methods have been developed for scRNA-seq datasets without any consideration of spatial information. Here, we present SpatialAnno, an efficient ...

To represent the interactive nature of cells and cell types, we adopt two cell-cell similarity networks as our primary data objects: one for gene expression and another for spatial location. To form the cell spot-cell spot gene expression similarity network, we first apply standard pre-processing steps including scaling, removal of technical ...

Spatial transcriptomic technologies have the potential to reveal critical relationships between the function of genes and cells and their spatial organization. Here, we provide a sharing model for spatial transcriptomics data with the aim of establishing a set of primary data and metadata needed to reproduce analyses and facilitate computational methods development.

Cell assignments were verified using comparison to known marker gene expression, protein expression, and location within tissue spatial organization. ... D. Henderson, J. M. Beechem, Insitutype: likelihood-based cell typing for single cell spatial transcriptomics. bioRxiv 2022.10.19 ... Z. Yang, E. Piazza, J. M. Beechem, Advances in mixed cell ...

We (1) selected genes that are expressed in both the scRNA-seq reference data and the spatial transcriptomic data, (2) selected among them the candidate cell-type-informative genes with a mean ...

The spatial location of cells within tissues and organs is crucial for the manifestation of their specific functions.Spatial transcriptomics technology enables comprehensive measurement of the gene expression patterns in tissues while retaining spatial information. However, current popular spatial transcriptomics techniques either have shallow sequencing depth or low resolution. We present ...

Here, we develop JSTA, a computational framework for joint cell segmentation and cell type annotation that utilizes prior knowledge of cell type-specific gene expression. Simulation results show that leveraging existing cell type taxonomy increases RNA assignment accuracy by more than 45%. Using JSTA, we were able to classify cells in the mouse ...

Schematic of the spASE method for detecting allele-specific expression (ASE) in spatial transcriptomics while accounting for mixtures. a Given a 2D spatial transcriptomics data set, we assume that for each gene j, each spot i can potentially source transcripts from multiple cell types. Each cell type can potentially have a different rate of expression for gene j and could also have a different ...

Similarly, SDINet requires the conversion of RNA-seq data into histogram form. What's more, single-cell spatial transcriptomics data, which reflects the expression and spatial information of single cells, provide researchers with the opportunity to integrate the location for exploring GRNs in depth (Svensson et al. 2018, Littman et al. 2023).

SpatialAnno: Probabilistic cell/domain-type assignment of spatial transcriptomics data with SpatialAnno. SpatialAnno is a package for annotation on spatial transcriptomics datasets developed by Jin Liu's lab.

In the analysis of both single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data, classifying cells/spots into cell/domain types is an essential analytic step for many secondary analyses. Most of the existing annotation methods have been developed for scRNA-seq datasets without any consideration of spatial ...