Accurate inference of genome-wide spatial expression with iSpatial.

Accurate inference of genome-wide spatial expression with iSpatial.
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利用iSpatial精确推断全基因组空间表达。

DOI:
10.1126/sciadv.abq0990
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发表时间:
2022-08-26
期刊:
影响因子:
13.6
通讯作者:
--
中科院分区:
综合性期刊1区
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--
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空间分辨的转录组分析可以揭示组织结构和背景依赖性细胞-细胞或细胞-环境相互作用的分子见解。由于当前的技术限制,以单细胞分辨率获得全基因组空间转录组具有挑战性。在这里,我们开发了一种名为 iSpatial 的新算法,通过整合空间转录组和单细胞 RNA-seq 数据集来推导整个转录组的空间模式。与其他现有方法相比,iSpatial 在预测基因表达和空间分布方面具有更高的准确性。此外,它还减少了原始数据集中的假阳性和假阴性信号。通过使用多个空间转录组数据集测试 iSpatial,我们证明了它对来自不同组织和不同技术的数据集的广泛适用性。因此,我们提供了一种计算方法来揭示单细胞分辨率下整个转录组的空间组织。凭借公共领域提供的大量高质量数据集,iSpatial 提供了一种独特的方式来了解复杂组织和疾病过程的结构和功能。 iSpatial 集成了空间转录组和单细胞 RNA-seq 数据,以得出整个转录组的空间模式。
Spatially resolved transcriptomic analyses can reveal molecular insights underlying tissue structure and context-dependent cell-cell or cell-environment interaction. Because of the current technical limitation, obtaining genome-wide spatial transcriptome at single-cell resolution is challenging. Here, we developed a new algorithm named iSpatial to derive the spatial pattern of the entire transcriptome by integrating spatial transcriptomic and single-cell RNA-seq datasets. Compared to other existing methods, iSpatial has higher accuracy in predicting gene expression and spatial distribution. Furthermore, it reduces false-positive and false-negative signals in the original datasets. By testing iSpatial with multiple spatial transcriptomic datasets, we demonstrate its wide applicability to datasets from different tissues and by different techniques. Thus, we provide a computational approach to reveal spatial organization of the entire transcriptome at single-cell resolution. With numerous high-quality datasets available in the public domain, iSpatial provides a unique way to understand the structure and function of complex tissues and disease processes. iSpatial integrates spatial transcriptomic and single-cell RNA-seq data to derive the spatial pattern of the entire transcriptome.
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