Abstract LB079: An end-to-end Visium spatial transcriptomics computational pipeline for generating low-code interactive reports of spatial insights

Abstract In recent years, methods for analyzing and interpreting expression data have evolved rapidly. Newer and more complex datasets are being developed faster than ever, and the resources needed to perform rigorous data analysis have increased dramatically. In spatial transcriptomics analysis, researchers face many technical challenges: data management, compute power, and programming knowledge. In addition to these technical barriers, a constantly evolving field can make it difficult for scientists to understand if they are performing adequate quality control, proper data integration, choosing situationally correct methods, and showing accurate biological insights. Transcriptomics data comes with many meaningful caveats that make having the proper technical resources just a single piece of the puzzle. These obstacles keep scientists with valuable biological questions from maximizing the power of Spatial Transcriptomics. To address these challenges, we developed an end-to-end Visium spatial transcriptomics computational pipeline for generating interactive figures and reports for low-code exploration. This pipeline automated data quality control, batch-effects correction, and multiple peer-reviewed analysis techniques to perform a spatially resolved analysis of tissue heterogeneity. To demonstrate the effectiveness of this pipeline, we performed an analysis on a cohort consisting of 3 patients with adenocarcinoma and 1 patient with signet ring cell carcinoma, each with 1 sample of primary colon tumor and its matched adjacent normal tissue. All samples were formalin-fixed paraffin-embedded (FFPE) provided by BioChain Institute. The tissue sections were stained with Hematoxylin and Eosin, and the transcriptome was mapped using 10x Genomics Visium Spatial Gene Expression for FFPE. The raw base call files were then processed into feature-barcode matrices, followed by quality control measures that included data filtering, sample integration, batch-effect removal, and normalization. The pipeline then combined multiple analysis methods for feature extraction from genes and spatial spots, including clustering, neighborhood detection, and gene module detection. Further, we leveraged clinical metadata to perform differential expression and abundance analyses of the extracted features across different tissue and disease types. The pipeline finally generated an interactive report detailing analysis of spatially-resolved tissue biology, pathology, and microenvironment, allowing a scientist to analyze and explore the data further. This pipeline to identified differences in the spatial arrangement between tumor and normal tissue, in addition to demonstrating the spatial arrangements that differentiate adenocarcinoma and signet ring cell carcinoma relating to the invasive front and body of the tumors. Citation Format: Steven Hamel, Elim Cheung, Ying Qu, Malachi Loviska, Aaron Mayer, Lutong Zhang, Tong Lu, Vidyodhaya Sundaram, Baowen Zhang, Aaron Chiou, Honesty Kim, Matthew Bieniosek, Alex Trevino. An end-to-end Visium spatial transcriptomics computational pipeline for generating low-code interactive reports of spatial insights [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB079.