A Study on Fatigue Properties of a TiNi/Al Shape Memory Alloy

Park, Young Chul; Kang, Jung-Ho; Lee, Jin Kyung; Sung, Jang Hyun; Baek, Seok Heum; Jo, Young Jik

doi:10.4028/0-87849-463-4.309

Cited by 1 publication

(1 citation statement)

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“…Historically, the sequencing-based approaches were limited to a few thousand spots, with each spot consisting of roughly 10 cells (Du et al 2023;Park et al). More recently, the resolution of these approaches has significantly increased allowing researchers to profile tens of thousands of spots at almost single cell resolution (Nagendran et al 2023).…”

Section: Introductionmentioning

confidence: 99%

SuperSpot: Coarse Graining Spatial Transcriptomic Data into Metaspots

Teleman,

Gabriel,

Hérault

et al. 2024

Preprint

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SummarySpatial Transcriptomics is revolutionizing our ability to phenotypically characterize complex biological tissues and decipher cellular niches. As of today, thousands of genes can be detected across hundreds of thousands of spots. Akin to standard single-cell RNA-Seq data, spatial transcriptomic data are very sparse due to the limited amount of RNA within each spot. Building upon the metacell concept, we present a workflow, called SuperSpot, to combine adjacent and transcriptionally similar spots into “metaspots”. The process involves representing spots as nodes in a graph with edges connecting spots in spatial proximity and edge weights representing transcriptional similarity. Hierarchical clustering is used to aggregate spots into metaspots at a user-defined resolution. We demonstrate that metaspots can be used to reduce the size of spatial transcriptomic data and remove some of the dropout noise.Availability and implementationSuperSpot is an R package available athttps://github.com/GfellerLab/SuperSpot.

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Section: Introductionmentioning

confidence: 99%