This study employed a longitudinal, treatment trajectory matched design comprising 15 clinically characterized triple negative breast cancer (TNBC) samples from 7 patients receiving standard of care neoadjuvant chemotherapy. Six patients contributed paired pre-treatment biopsy and post-treatment surgical specimens, while one patient provided three sequential samples, including a treatment-naÃ¯ve biopsy, post-surgical specimen, and metastatic site sample. Patients were classified based on pathological assessment and follow-up as achieving pathological complete remission (pCR) or non-pCR. Chemotherapy regimens included anthracycline- and taxane-based combinations with or without carboplatin, and metastatic sites involved breast, lung, bone, and lymph nodes. Single-cell transcriptomic profiling was performed using the 10x Genomics Chromium Flex platform, generating four multiplexed libraries.
Computational methods for interpreting single-cell transcriptomes often rely on task-specific post hoc analyses to estimate pathway activity, cell identity, or metabolic status. These approaches operate in silos, lack shared structure, and do not exploit known relationships among biological entities. We present \textbf{KNIFE} (Knowledge graph-based NMF for Inference of Functional Entities), a unified matrix factorization framework that reconstructs single-cell gene expression while simultaneously estimating enrichment or activity scores across heterogeneous biological entities. KNIFE incorporates a plug-in knowledge graph that connects genes, pathways, regulons, and cell types, enabling information sharing through graph-regularized latent representations. This allows entity activity estimation to be formulated as a single optimization problem, replacing a patchwork of independent scoring methods. KNIFE generalizes classical enrichment approaches, improves robustness in sparse settings, and provides a modular foundation for extensible, interpretable single-cell analysis.
Computational methods for interpreting single-cell transcriptomes often rely on task-specific post hoc analyses to estimate pathway activity, cell identity, or metabolic status. These approaches operate in silos, lack shared structure, and do not exploit known relationships among biological entities. We present \textbf{KNIFE} (Knowledge graph-based NMF for Inference of Functional Entities), a unified matrix factorization framework that reconstructs single-cell gene expression while simultaneously estimating enrichment or activity scores across heterogeneous biological entities. KNIFE incorporates a plug-in knowledge graph that connects genes, pathways, regulons, and cell types, enabling information sharing through graph-regularized latent representations. This allows entity activity estimation to be formulated as a single optimization problem, replacing a patchwork of independent scoring methods. KNIFE generalizes classical enrichment approaches, improves robustness in sparse settings, and provides a modular foundation for extensible, interpretable single-cell analysis.
Indraprastha Institute of Information Technology, Delhi