Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images.

Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images.
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稀疏空间转写图像的非参数渐晕校正。

DOI:
10.1007/978-3-030-87237-3_45
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发表时间:
2021-09
期刊:
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子:
--
通讯作者:
Varol E
Varol E
中科院分区:
其他
文献类型:
--
作者:
Rao BY;Peterson AM;Kandror EK;Herrlinger S;Losonczy A;Paninski L;Rizvi AH;Varol E

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空间转录组学技术,如STARmap,使复杂组织切片内的RNA转录本的亚细胞检测成为可能。来自这些技术的数据受到光学显微镜限制的影响,例如在图像捕获期间来自不均匀照明的阴影或渐晕效应。这些稀疏的空间分辨转录本的下游分析依赖于这些伪影的校正。本文介绍了一种新的非参数渐晕校正工具的空间转录组图像,估计照明场和背景使用一个有效的迭代切片直方图归一化例程。我们表明,我们的方法优于国家的最先进的阴影校正技术,无论是在照明和背景场估计,需要更少的输入图像进行充分的估计。我们进一步证明了我们的技术的一个重要的下游应用,表明通过我们的方法校正的空间转录组体积在啮齿动物海马中产生更高和更均匀的基因表达点调用。Python代码和一个演示文件可以重现我们的结果,在补充材料和这个github页面上提供:https://github.com/BoveyRao/Non-parametric-vc-for-sparse-st。
Spatial transcriptomics techniques such as STARmap enable the subcellular detection of RNA transcripts within complex tissue sections. The data from these techniques are impacted by optical microscopy limitations, such as shading or vignetting effects from uneven illumination during image capture. Downstream analysis of these sparse spatially resolved transcripts is dependent upon the correction of these artefacts. This paper introduces a novel non-parametric vignetting correction tool for spatial transcriptomic images, which estimates the illumination field and background using an efficient iterative sliced histogram normalization routine. We show that our method outperforms the state-of-the-art shading correction techniques both in terms of illumination and background field estimation and requires fewer input images to perform the estimation adequately. We further demonstrate an important downstream application of our technique, showing that spatial transcriptomic volumes corrected by our method yield a higher and more uniform gene expression spot-calling in the rodent hippocampus. Python code and a demo file to reproduce our results are provided in the supplementary material and at this github page: https://github.com/BoveyRao/Non-parametric-vc-for-sparse-st.
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