NLM IRP Seminar Schedule

TBD

Condition-Aware Cell Type Deconvolution of Bulk Tissues

Transcriptome analysis is a key tool allowing to investigate healthy and diseased tissues at the molecular level. While single-cell RNA sequencing offers valuable insights into cell types and states, complex sample preparation procedures and higher cost restrict its widespread adoption compared to the older bulk RNA sequencing, which is already widely established, has lower cost, and exhaustive population-level data collections available. In addition, newer technology in form of spatial transcriptomics, which offers locality-based insights, essentially produces data from many thousands of localized bulk mixtures. However, bulk expression data comprise a mixture of heterogeneous cell types and capture average expression. Thus, deconvolving bulk mixtures and inferring cell type populations from bulk expression, remains indispensable.

Many computational methods have been developed to infer cell type proportions from bulk data, generally with the use of reference data based on single-cell sequencing, which guides the process. However, technological inconsistencies between the bulk mixtures and the reference affect the accuracy of such approaches. Moreover, medical conditions are also associated with tissue reprogramming, possibly resulting in changes in cell type composition.

In this talk, a new model for cell type deconvolution is introduced; to our knowledge the first one to offer condition-aware cell type deconvolution. It allows both the incorporation of a quantitative condition that may have a sample-specific effect on expression of certain genes in certain cell types, as well as an implicit mechanism of correction for inconsistencies between the reference and the bulk mixtures. We give an efficient method to solve the model, inferring both cell type proportions as well as the trend of the influence of the quantitative condition on genes expression in cell type populations. Our benchmarks demonstrate the increased accuracy of this model over more basic models, and increased resilience to inconsistencies between the reference and the bulk expression.

The conformal central charge of the spin-1/2 XX model derived from long-chain asymptotics

The spin-interacting models have wide applications in studying biological systems such as pattern generations, neural networks, and the spread of disease. In addition, the central charge of conformal field theory (CFT) could quantify the universality classes and give the magnitude of the Casimir effect. It has led to research on the categorization of cell membranes with multiple phases which have implications for cell trafficking and communications. To better understand the spin interaction systems, we revisit the well-known XX model, along with the energy spectrum and the ground state degeneracy. While imposing the translational invariance, we obtain the energy spectrum of the finite-length periodic chain via Jordan-Wigner transformation with suitable momentum mode choices. The finite open chain violates the translational symmetry and is solved by matrix analysis in addition to the Jordan-Wigner transformation. By investigating the long chain length asymptotics, we find different dominant correction terms for chains under open and periodic boundary conditions as well as for chains of even and odd number of sites. By comparing the asymptotic form of the ground state energy with the one from CFT, we confirm that the conformal central charge for the XX chain is c = 1 for the even chain lengths, albeit for open boundary conditions there exists an additional boundary energy term. For the odd number site chains, while the boundary energy for the open boundary remains the same, the system is not describable by CFT with the central charge c = 1.