Article

Optimizing multiplexed imaging experimental design through tissue spatial segregation estimation

2021-11-28

Abstract excerpt

Recent advances in multiplexed imaging methods allow simultaneous detection of dozens of proteins and hundreds of RNAs enabling deep spatial characterization of both healthy and diseased tissues. Parameters for design of optimal sequencing-based experiments have been established, but such parameters, especially those estimating how much area has to be imaged to capture all cell phenotype clusters, are lacking for...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 136838cb-8ecc-5098-899e-7105615eb7bb
DOI: 10.1101/2021.11.28.470262

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Optimizing multiplexed imaging experimental design through tissue spatial segregation estimationDOI 10.1101/2021.11.28.470262Read

Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clusteringSemantic proximity Read

Unsupervised discovery of tissue architecture in multiplexed imagingSemantic proximity Read

Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic modelSemantic proximity Read

ClumPyCells resolves spatial aggregation in complex tissues overcoming size biasesSemantic proximity Read

Toward Computationally Complete Spatial OmicsSemantic proximity Read

A new spatial multi-omics approach to deeply characterize human cancer tissue using a single tissue sectionSemantic proximity Read

CellMAPS: A no-code model-based customisable multiplex image analysis workflowSemantic proximity Read

Robust biomarker discovery through multiplatform multiplex image analysis of breast cancer clinical cohortsSemantic proximity Read

A deep learning-based multiscale integration of spatial omics with tumor morphologySemantic proximity Read

HESTIA: Scalable Multimodal Integration of Histology and High-Resolution Spatial Transcriptomics for Robust Spatial Domain IdentificationSemantic proximity Read

Automated Intelligent Microscopy for Phenotype Identification, Spatial Localization, and Retargeting of Cells.Semantic proximity Read

Integrative Spatial Omics for Systems-Level Mapping of Pathological NichesSemantic proximity Read