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DTSTART;TZID=America/New_York:20250306T160000
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SUMMARY:Universal Prediction of Cell-cycle Position Using Transfer Learning
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday\, March 6\, 2025\n4:00pm\nHarvard TH Chan School of Public Health\, FXB G13 \nKasper Hansen\, PhD\nAssociate Professor\, McKusick-Nathans Insitute of Genetic Medicine\, Department of Biostatistics\, Johns Hopkins University \nA significant barrier to progress in biomedical data science is the development of prediction models that work across contexts such as different instruments\, facilities or hospitals. This is particularly difficult for predictions based on genomics data. Here\, we present an example of a generalizable prediction model. \nThe cell cycle is a highly conserved\, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation\, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. \nHere\, we present tricycle\, an R/Bioconductor package\, which addresses this challenge by leveraging key features of the biology of the cell cycle\, the mathematical properties of principal component analysis of periodic functions\, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding\, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays\, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset\, we show that tricycle predictions generalize to datasets with multiple cell types\, across tissues\, species\, and even sequencing assays.
URL:https://ds.dfci.harvard.edu/event/universal-prediction-of-cell-cycle-position-using-transfer-learning/
LOCATION:Harvard TH Chan School of Public Health\, FXB G13\, 677 Huntington Ave\, Boston\, MA\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/02/khansen.jpg
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UID:5867-1740672000-1740675600@ds.dfci.harvard.edu
SUMMARY:Single-cell Multi-sample Multi-condition Data Integration to Uncover Disease Signatures
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday February 27th at 4pm\nHSPH FXB Room G13 \nYingxin Lin\, PhD\nPostdoctoral Associate in the Department of Biostatistics at the Yale School of Public Health \nThe recent emergence of multi-sample multi-condition single-cell multi cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. In this talk\, I will present scMerge2\, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. scMerge2 is generalized to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large data collection with over five million cells from 1000+ individuals\, we demonstrate that the integration of multi-sample multi-condition scRNAseq from multiple cohorts reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression.
URL:https://ds.dfci.harvard.edu/event/single-cell-multi-sample-multi-condition-data-integration-to-uncover-disease-signatures/
LOCATION:Harvard TH Chan School of Public Health\, FXB G13\, 677 Huntington Ave\, Boston\, MA\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/02/headshot-scaled-e1739970101940.jpg
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