Atta Lyla, Clifton Kalen, Anant Manjari, Aihara Gohta, Fan Jean
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA.
bioRxiv. 2024 Mar 6:2023.08.30.555624. doi: 10.1101/2023.08.30.555624.
Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.
基于成像的空间分辨转录组学（im-SRT）技术的最新进展，现在能够对固定组织中靶向基因及其位置进行高通量分析。基因表达数据的标准化通常是必要的，以考虑可能混淆潜在生物信号的技术因素。在这里，我们研究了不同基因计数标准化方法与不同靶向基因panel在im-SRT数据分析和解释中的潜在影响。使用不同的模拟基因panel，这些panel过度代表在特定组织区域或细胞类型中表达的基因，我们展示了基于每个细胞检测到的基因计数的标准化方法如何以区域或细胞类型特异性方式差异影响标准化基因表达量。我们表明，这些标准化诱导的效应可能会降低包括差异基因表达、基因倍数变化和空间可变基因分析在内的下游分析的可靠性，与从更能代表组织组成细胞类型基因表达的基因panel获得的结果相比，会引入假阳性和假阴性结果。对于不使用检测到的基因计数进行基因表达量调整的标准化方法，如细胞体积或细胞面积标准化，不会观察到这些效应。我们建议在可行的情况下使用基于非基因计数的标准化方法，并在必要时在使用基于基因计数的标准化方法之前评估基因panel的代表性。总体而言，我们提醒标准化方法和基因panel的选择可能会影响im-SRT数据的生物学解释。
