Boyi Guo, Wodan Ling, Sang Ho Kwon, Pratibha Panwar, Shila Ghazanfar, Keri Martinowich, Stephanie C. Hicks
{"title":"Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities","authors":"Boyi Guo, Wodan Ling, Sang Ho Kwon, Pratibha Panwar, Shila Ghazanfar, Keri Martinowich, Stephanie C. Hicks","doi":"arxiv-2408.00367","DOIUrl":null,"url":null,"abstract":"Advances in spatially-resolved transcriptomics (SRT) technologies have\npropelled the development of new computational analysis methods to unlock\nbiological insights. As the cost of generating these data decreases, these\ntechnologies provide an exciting opportunity to create large-scale atlases that\nintegrate SRT data across multiple tissues, individuals, species, or phenotypes\nto perform population-level analyses. Here, we describe unique challenges of\nvarying spatial resolutions in SRT data, as well as highlight the opportunities\nfor standardized preprocessing methods along with computational algorithms\namenable to atlas-scale datasets leading to improved sensitivity and\nreproducibility in the future.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":"75 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.00367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Advances in spatially-resolved transcriptomics (SRT) technologies have
propelled the development of new computational analysis methods to unlock
biological insights. As the cost of generating these data decreases, these
technologies provide an exciting opportunity to create large-scale atlases that
integrate SRT data across multiple tissues, individuals, species, or phenotypes
to perform population-level analyses. Here, we describe unique challenges of
varying spatial resolutions in SRT data, as well as highlight the opportunities
for standardized preprocessing methods along with computational algorithms
amenable to atlas-scale datasets leading to improved sensitivity and
reproducibility in the future.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
