BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20230101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240503T110000
DTEND;TZID=Asia/Seoul:20240503T120000
DTSTAMP:20260413T203141
CREATED:20240219T043810Z
LAST-MODIFIED:20240728T142252Z
UID:9239-1714734000-1714737600@www.ibs.re.kr
SUMMARY:Pedro Mendes\, Multiscale hybrid differential equation and agent-based models
DESCRIPTION:Abstract: Biological phenomena are notorious for crossing several temporal and spatial scales. While often it may be sufficient to focus on a single scale\, it is not rare that we have to consider several scales simultaneously. Computational modeling and simulation of biological systems thus frequently requires to include diverse temporal and spatial scales. A popular approach in systems biology is to combine differential equations and agent-based models\, where usually small sets of differential equations are used to represent the internal state of each cell\, with the cells being represented as interacting autonomous agents on a lattice. This type of hybrid models allows for parallel solution of smaller sets of differential equations rather than the solution of a single but very large set of differential equations. At certain discrete times\, the agents are allowed to communicate\, and only then are the different sets of differential equations able to influence each other. This time discretization of the cell-cell interactions carries an inherent approximation error compared to the continuous interaction of these cells in the single model of a large set of coupled differential equations. Here we study this approximation error and investigate the conditions in which it becomes negligible\, thus defining the domain where the multiscale approach is valid. The approach is illustrated with a classic model of Drosophila segment polarity network\, where a model based on a full set of differential equations (the original version of that model) is compared with a hybrid model combining differential equations and agent-based approach (implemented with the open source software simulators Vivarium and COPASI). This study is also relevant to other hybrid simulations\, such as those representing “whole-cell models”\, where partitions may be done at other organizational scales.
URL:https://www.ibs.re.kr/bimag/event/pedro-mendes-multiscale-hybrid-differential-equation-and-agent-based-models/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2024/02/Pedro-Mendes-e1722176551946.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240510T110000
DTEND;TZID=Asia/Seoul:20240510T120000
DTSTAMP:20260413T203141
CREATED:20240219T044117Z
LAST-MODIFIED:20240728T142006Z
UID:9242-1715338800-1715342400@www.ibs.re.kr
SUMMARY:Jingyi Jessica Li\, ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double dipping
DESCRIPTION:Abstract: In typical single-cell RNA-seq (scRNA-seq) data analysis\, a clustering algorithm is applied to find discrete cell clusters as putative cell types\, and then a statistical test is employed to identify the differentially expressed (DE) genes between the cell clusters. However\, this common procedure suffers the “double dipping” issue: the same data are used twice to find discrete cell clusters as putative cell types and DE genes as potential cell-type marker genes\, leading to false-positive cell-type marker genes even when the cell clusters are spurious. To overcome this challenge\, we propose ClusterDE\, a post-clustering DE method for controlling the false discovery rate (FDR) of identified DE genes regardless of clustering quality\, which can work as an add-on to popular pipelines such as Seurat. The core idea of ClusterDE is to generate real-data-based synthetic null data containing only one cell type\, in contrast to the real data\, for evaluating the whole procedure of clustering followed by a DE test. Using comprehensive simulation and real data analysis\, we show that ClusterDE has solid FDR control and the ability to identify canonical cell-type marker genes as top DE genes\, distinguishing them from common housekeeping genes. Notably\, the DE genes identified by ClusterDE are informative markers for discrete cell types and can guide the merging of spurious clusters. ClusterDE is fast\, transparent\, and adaptive to a wide range of clustering algorithms and DE tests.
URL:https://www.ibs.re.kr/bimag/event/jingyi-jessica-li-clusterde-a-post-clustering-differential-expression-de-method-robust-to-false-positive-inflation-caused-by-double-dipping/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2024/02/Jessica-li-e1722176393718.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR