Computational Molecular Biology 2025, Vol.15, No.4, 183-192 http://bioscipublisher.com/index.php/cmb 185 Figure 1 Inspirations in spatial transcriptomics. TME: tumor microenvironment; ScRNA-seq: single cell RNA sequencing (Adopted from Du et al., 2023) 3.2 Multi-dimensional characteristics of spatial data: spatial resolution, gene coverage and noise sources The data of spatial transcriptomes is quite "picky", with various differences piling up layer upon layer. Distinguishing the first is a problem - some technologies can see subcellular details, while others can only cover a few cells. The larger the capture point, the more signals will be mixed together. When it comes to gene coverage, sequencing methods can scan tens of thousands of genes, but imaging methods usually only focus on a few hundred pre-selected targets. Noise is even more troublesome. During sequencing, there is often background RNA doping or insufficient sensitivity to miss signals. During imaging, autofluorescence and localization errors may also occur. The high data dimension and the small sample size, when combined, make the analysis tricky. To extract reliable information from it, various noise reduction and correction methods have to be relied upon to support the process (Shan et al., 2022; Wang et al., 2022).
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