{"title":"Spatial Transcriptomics Brings New Challenges and Opportunities for Trajectory Inference.","authors":"Matthieu Heitz, Yujia Ma, Sharvaj Kubal, Geoffrey Schiebinger","doi":"10.1146/annurev-biodatasci-040324-030052","DOIUrl":null,"url":null,"abstract":"<p><p>Spatial transcriptomics (ST) brings new dimensions to the analysis of single-cell data. While some methods for data analysis can be ported over without major modifications, they are the exception rather than the rule. Trajectory inference (TI) methods in particular can suffer from significant challenges due to spatial batch effects in ST data. These can add independent sources of noise to each time point. Pioneering methods for TI on ST data have focused primarily on addressing the batch effects in physical arrangement, i.e., where tissues are deformed in different ways at different time points. However, other challenges arise due to the measurement granularity of ST technologies, as well as a bias from slicing. In this review, we examine the sources of these challenges, and we explore how they are addressed with current state-of-the-art STTI methods. We conclude by highlighting some opportunities for future method development.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Biomedical Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1146/annurev-biodatasci-040324-030052","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Spatial transcriptomics (ST) brings new dimensions to the analysis of single-cell data. While some methods for data analysis can be ported over without major modifications, they are the exception rather than the rule. Trajectory inference (TI) methods in particular can suffer from significant challenges due to spatial batch effects in ST data. These can add independent sources of noise to each time point. Pioneering methods for TI on ST data have focused primarily on addressing the batch effects in physical arrangement, i.e., where tissues are deformed in different ways at different time points. However, other challenges arise due to the measurement granularity of ST technologies, as well as a bias from slicing. In this review, we examine the sources of these challenges, and we explore how they are addressed with current state-of-the-art STTI methods. We conclude by highlighting some opportunities for future method development.
空间转录组学（ST）为单细胞数据分析带来了新的维度。虽然有些数据分析方法无需进行重大修改即可移植，但它们只是例外，而不是常规。特别是轨迹推断（TI）方法，由于 ST 数据的空间批次效应，可能会面临巨大的挑战。这可能会给每个时间点增加独立的噪声源。ST 数据轨迹推断的开创性方法主要侧重于解决物理排列中的批次效应，即组织在不同时间点以不同方式变形。然而，由于 ST 技术的测量粒度以及切片产生的偏差，也带来了其他挑战。在本综述中，我们研究了这些挑战的来源，并探讨了当前最先进的 STTI 方法如何应对这些挑战。最后，我们强调了未来方法发展的一些机遇。
期刊介绍：
The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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