Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results.

Transcriptomics is widely used to assess the state of biological systems. There are many tools for the different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis, which typically provides the basis for interpretation and follow-up experiments. To address this, we introduce FLOP, a comprehensive nextflow-based workflow combining methods to perform end-to-end analyses of transcriptomics data. We illustrate FLOP on datasets ranging from end-stage heart failure patients to cancer cell lines. We discovered effects not noticeable at the gene-level, and observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space. Moreover, we performed three benchmarks to evaluate the 12 pipelines included in FLOP, and confirmed that filtering is essential in scenarios of expected moderate-to-low biological signal. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings.

Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma.

Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. Here, we applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy. We examined formalin-fixed paraffin-embedded tissue from (1) regions identified by the AI model as highly associated with short or extended chemotherapy response, and (2) background tumor regions (not identified by the AI model as highly associated with outcome status) from the same tumors. We show that the transcriptomic profiles of AI-identified regions are more distinct than background regions from the same tumors, are superior in predicting outcome, and differ in several pathways including those associated with chemoresistance in HGSC. Further, we find that poor outcome and good outcome regions are enriched by different tumor subpopulations, suggesting distinctive interaction patterns. In summary, our work presents proof of concept that AI-guided spatial transcriptomic analysis improves recognition of biologic features relevant to patient outcomes.

DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics.

Single-cell transcriptomics and spatially-resolved imaging/sequencing technologies have revolutionized biomedical research. However, they suffer from lack of spatial information and a trade-off of resolution and gene coverage, respectively. We propose DOT, a multi-objective optimization framework for transferring cellular features across these data modalities, thus integrating their complementary information. DOT uses genes beyond those common to the data modalities, exploits the local spatial context, transfers spatial features beyond cell-type information, and infers absolute/relative abundance of cell populations at tissue locations. Thus, DOT bridges single-cell transcriptomics data with both high- and low-resolution spatially-resolved data. Moreover, DOT combines practical aspects related to cell composition, heterogeneity, technical effects, and integration of prior knowledge. Our fast implementation based on the Frank-Wolfe algorithm achieves state-of-the-art or improved performance in localizing cell features in high- and low-resolution spatial data and estimating the expression of unmeasured genes in low-coverage spatial data.