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Factor analysis decomposes single-cell gene expression data into a minimal set of gene programs that correspond to processes executed by cells in a sample. However, matrix factorization methods are prone to technical artifacts and poor factor interpretability. We address these concerns with Spectra, an algorithm that combines user-provided gene programs with the detection of novel programs that together best explain expression covariation. Spectra incorporates existing gene sets and cell-type labels as prior biological information, explicitly models cell type and represents input gene sets as a gene-gene knowledge graph using a penalty function to guide factorization toward the input graph. We show that Spectra outperforms existing approaches in challenging tumor immune contexts, as it finds factors that change under immune checkpoint therapy, disentangles the highly correlated features of CD8 + T cell tumor reactivity and exhaustion, finds a program that explains continuous macrophage state changes under therapy and identifies cell-type-specific immune metabolic programs., (© 2023. The Author(s).)

Optimal transport (OT) and the related Wasserstein metric ( W ) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standard multidimensional scaling (MDS). We present Wasserstein Wormhole, a transformer-based autoencoder that embeds empirical distributions into a latent space wherein Euclidean distances approximate OT distances. Extending MDS theory, we show that our objective function implies a bound on the error incurred when embedding non-Euclidean distances. Empirically, distances between Wormhole embeddings closely match Wasserstein distances, enabling linear time computation of OT distances. Along with an encoder that maps distributions to embeddings, Wasserstein Wormhole includes a decoder that maps embeddings back to distributions, allowing for operations in the embedding space to generalize to OT spaces, such as Wasserstein barycenter estimation and OT interpolation. By lending scalability and interpretability to OT approaches, Wasserstein Wormhole unlocks new avenues for data analysis in the fields of computational geometry and single-cell biology. Software is available at http://wassersteinwormhole.readthedocs.io/en/latest/.

Patients with cancer frequently receive immune-checkpoint inhibitors (ICIs), which may modulate immune responses to COVID-19 vaccines. Recently, cytokine release syndrome (CRS) was observed in a patient with cancer who received BTN162b2 vaccination under ICI treatment. Here, we analyzed adverse events and serum cytokines in patients with 23 different tumors undergoing (n = 64) or not undergoing (n = 26) COVID-19 vaccination under ICI therapy in a prospectively planned German single-center cohort study (n = 220). We did not observe clinically relevant CRS (≥grade 2) after vaccination (95% CI 0-5.6%; Common Terminology of Adverse Events v.5.0) in this small cohort. Within 4 weeks after vaccination, serious adverse events occurred in eight patients (12.5% 95% CI 5.6-23%): six patients were hospitalized due to events common under cancer therapy including immune related adverse events and two patients died due to conditions present before vaccination. Despite absence of CRS symptoms, a set of pairwise-correlated CRS-associated cytokines, including CXCL8 and interleukin-6 was >1.5-fold upregulated in 40% (95% CI 23.9-57.9%) of patients after vaccination. Hence, elevated cytokine levels are common and not sufficient to establish CRS diagnosis., (© 2022. The Author(s).)