NLM IRP Seminar Schedule
UPCOMING SEMINARS
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April 30, 2024 Wenya Rowe
The conformal central charge of the spin-1/2 XX model derived from long-chain asymptotics -
May 2, 2024 OPEN
TBD -
May 7, 2024 OPEN
TBD -
May 9, 2024 Pascal Mutz
TBD -
May 14, 2024 Stanley Liang
TBD
RECENT SEMINARS
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April 25, 2024 Ermin Hodzic
Condition-Aware Cell Type Deconvolution of Bulk Tissues -
April 23, 2024 OPEN
TBD -
April 16, 2024 Jaya Srivastava
Regulatory plasticity of the human genome -
April 11, 2024 Sergey Shmakov
Comprehensive survey of the TnpB RNA-guided nucleases -
April 2, 2024 Yifan Yang
Fairness and Bias in Biomedical AI
Scheduled Seminars on April 25, 2024
Contact NLM_IRP_Seminar_Scheduling@mail.nih.gov with questions about this seminar.
Abstract:
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.