Year | Month |
2024 | Jan Feb Mar May |
2023 | Jan Feb Mar Apr May Jun Jul Aug Oct Nov Dec |
2022 | Jan Feb Jun Jul Aug Oct Nov Dec |
2021 | Jul Aug Sep Oct Nov |
Title: | An integrated approach for analyzing spatial transcriptomic data by leveraging single-cell data |
Speaker: | Dr. Xiang Wan, Shenzhen Research Institute of Big Data, Shenzhen, China |
Time/Place: | 14:30:00 - 15:30:00 FSC1217 |
Abstract: | Single-cell RNA sequencing (scRNA-seq) characterizes the whole transcriptome of individual cells within a given organ but it does not capture the spatial distribution of cells. As spatial information is so critical to understand communication between cells, many scientific questions related to cellular communication cannot be fully addressed by scRNA-seq alone. To study the spatial distribution of gene expression, many spatial transcriptomics (ST) technologies have been developed to quantify spatially localized transcriptomes, which accelerated the capacity to elucidate the development of healthy tissue and tumor microenvironment of cancers. In this talk, I will introduce our recent ongoing work, a unified framework to combine the scRNA-seq data with various forms of spatial data collected from the same tissue. Our method addresses two challenges in spatial transcriptomics: single-cell resolution cell type identification and single-cell resolved high-throughput transcriptome inference. |
Title: | Semiparametric Efficient G-estimation with Invalid Instrumental Variables |
Speaker: | Dr. Zhonghua Liu, Department of Biostatistics, Columbia University, New York, USA |
Time/Place: | 15:30:00 - 16:30:00 FSC1217 |
Abstract: | The instrumental variable method is widely used in the health and social sciences for identification and estimation of causal effects in the presence of potential unmeasured confounding. In order to improve efficiency, multiple instruments are routinely used, leading to concerns about bias due to possible violation of the instrumental variable assumptions. To address this concern, we introduce a new class of g-estimators that are guaranteed to remain consistent and asymptotically normal for the causal effect of interest provided that a set of at least $gamma$ out of $K$ candidate instruments are valid, for $gammaleq K$ set by the analyst ex ante, without necessarily knowing the identity of the valid and invalid IVs. We provide formal semiparametric efficiency theory supporting our results. Both simulation studies and applications to the UK Biobank data demonstrate the superior empirical performance of our estimators compared to competing methods. |
Title: | Step-size Independent Theoretical Bounds for Preconditioning Techniques |
Speaker: | Dr Xuelei Lin, School of Science, Harbin Institute of Technology (Shenzhen) |
Time/Place: | 16:00:00 - 17:00:00 Zoom, Meeting ID: 910 6258 3121 |
Abstract: | It is well-known that the condition number of coefficient matrices arising from discretization of differential equations increases as the grid gets refined, because of which iterative solvers converges slowly for the linear systems when the grid is dense. In this talk, preconditioning techniques for dicretization of some differential equations are introduced, with which some Krylov subspace solvers for the preconditioned systems are proven to have a linear convergence rate independent of the stepsizes. Numerical results are also reported. |
The Department has a distinguished record in teaching and research. A number of faculty members have been recipients of relevant awards.
Learn MoreDr S. Hon recevied the Early Career Award (21/22) from the Research Grants Council.
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