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Quantitative Issues in Cancer Research Working Group Seminar

Building 2, Room 426

Christian Covington, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Multiverse Analysis for Causal Inference (and vice versa) Abstract: Multiverse analysis is a framework developed in the quantitative social and behavioral sciences, designed to represent “non-statistical” uncertainty that arises in data analysis when making choices about conceptual operationalization, data preparation, etc. … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

Quantitative Issues in Cancer Research Working Group Seminar

Building 2, Room 426

Omar Melikechi, Postdoctoral Research Fellow, Department of Biostatistics, Harvard University Integrated path stability selection Abstract: Feature selection aims to identify important features in a data set, which can lead to more accurate and interpretable results. For example, it has been used to identify genes that are associated with certain diseases. Stability selection is a popular … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

Quantitative Issues in Cancer Research Working Group Seminar

Building 2, Room 426

Phillip Nicol, PhD Candidate, Department of Biostatistics, Harvard University Identifying spatially variable genes by projecting to morphologically relevant directions Abstract: Spatial transcriptomics allows for high-resolution sequencing while retaining two-dimensional sample coordinates. A common goal is to identify spatially variable genes within a predefined cell type or domain. However, in many cases this region is implicitly … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"