{'fraction_non_alphanumeric': 0.012491781722550954, 'fraction_numerical': 0.0, 'mean_word_length': 5.675438596491228, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Evaluating spatially variable gene detection methods for spatial transcriptomics data

The identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data analysis. Given the critical role it serves for downstream data interpretations, various methods for detecting spatially variable genes (SVGs) have been proposed. The availability of multiple methods for detecting SVGs bears questions such as whether different methods select a similar set of SVGs, how reliable is the reported statistics significance from each method, how accurate and robust is each method in terms of SVG detection, and how well the selected SVGs perform in downstream applications such as clustering of spatial domains. Besides these, practical considerations such as computational time and memory usage are also crucial for deciding which method to use. In this study, we address the above questions by systematically evaluating a panel of popular SVG detection methods on a large collection of spatial transcriptomics datasets, covering various tissue types, biotechnologies, and spatial resolutions. Our results shed light on the performance of each method from multiple aspects and highlight the discrepancy among different methods especially on calling statistically significant SVGs across datasets. Taken together, our work provides useful considerations for choosing methods for identifying SVGs and serves as a key reference for the future development of such methods.