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X-WR-CALDESC:Events for Dana-Farber Cancer Institute
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DTSTART;TZID=America/New_York:20250205T120000
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UID:5793-1738756800-1738760400@ds.dfci.harvard.edu
SUMMARY:Nice-Driven Cell Identities in the Self-Renewing Stomach Corpus Epithelium
DESCRIPTION:CompBio Connections Seminar\nWednesday February 5 at 12:00pm\nCenter for Life Sciences Building\, Zelen Commons\nKe Li\, PhD\, Research Fellow\, Dana-Farber Cancer Institute and Harvard Medical School \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/nice-driven-cell-identities-in-the-self-renewing-stomach-corpus-epithelium/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2025/01/Ke_headshot_square.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250206T160000
DTEND;TZID=America/New_York:20250206T170000
DTSTAMP:20260415T235007
CREATED:20250114T202055Z
LAST-MODIFIED:20250207T140322Z
UID:5776-1738857600-1738861200@ds.dfci.harvard.edu
SUMMARY:Choosing Good Subsamples for Regression Modelling: Nearly-True Models?
DESCRIPTION:Harvard Biostatistics Colloquium Series\nThursday February 6th\n4:00-5:00PM\nHarvard TH Chan School of Public Health\, FXB G12 \nThomas Lumley\, PhD\, Chair in Biostatistics\, University of Aukland\, New Zealand; Affiliate Professor\, University of Washington\, Department of Biostatistics
URL:https://ds.dfci.harvard.edu/event/choosing-good-subsamples-for-regression-modelling-nearly-true-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2025/01/lumley.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250213T110000
DTEND;TZID=America/New_York:20250213T120000
DTSTAMP:20260415T235007
CREATED:20250207T182809Z
LAST-MODIFIED:20250211T172627Z
UID:5831-1739444400-1739448000@ds.dfci.harvard.edu
SUMMARY:Analysis and Design of RNA sequences with Deep Learning
DESCRIPTION:Data Science Seminar\nThursday February 13th at 11am\nCenter for Life Sciences Building\, 111081 \nJoseph Valencia\, Oregon State University
URL:https://ds.dfci.harvard.edu/event/analysis-and-design-of-rna-sequences-with-deep-learning/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/02/joseph-e1738952849381.jpg
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DTSTART;TZID=America/New_York:20250219T120000
DTEND;TZID=America/New_York:20250219T130000
DTSTAMP:20260415T235007
CREATED:20250211T172609Z
LAST-MODIFIED:20250219T130227Z
UID:5846-1739966400-1739970000@ds.dfci.harvard.edu
SUMMARY:Current Methods in Single Cell FFPE Analysis
DESCRIPTION:CompBio Connections\nFebruary 19\, 2025 at 12pm\nDFCI Center for Life Science Building\, Zelen Commons \nAnthony Anselmo\nLead Bioinformatician\nCenter for Cancer Genomics\, DFCI \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/current-methods-in-single-cell-ffpe-analysis/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2025/02/anthony_anselmo_phsweb-e1739294749828.jpeg
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DTSTART;TZID=America/New_York:20250227T160000
DTEND;TZID=America/New_York:20250227T170000
DTSTAMP:20260415T235007
CREATED:20250219T130212Z
LAST-MODIFIED:20250228T180912Z
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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