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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240802T140000
DTEND;TZID=Asia/Seoul:20240802T160000
DTSTAMP:20260424T032417
CREATED:20240729T000958Z
LAST-MODIFIED:20240729T001043Z
UID:9893-1722607200-1722614400@www.ibs.re.kr
SUMMARY:Yun Min Song\, RNA velocity of single cells
DESCRIPTION:In this talk\, we discuss the paper “RNA velocity of single sells” by Gioele La Manno et.al.\, Nature\, 2018. \nAbstract \nRNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy\, sensitivity and throughput. However\, this approach captures only a static snapshot at a point in time\, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage\, demonstrate its use on multiple published datasets and technical platforms\, reveal the branching lineage tree of the developing mouse hippocampus\, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics\, particularly in humans.
URL:https://www.ibs.re.kr/bimag/event/yun-min-song-rna-velocity-of-single-cells/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240809T140000
DTEND;TZID=Asia/Seoul:20240809T160000
DTSTAMP:20260424T032417
CREATED:20240730T001308Z
LAST-MODIFIED:20240730T001308Z
UID:9901-1723212000-1723219200@www.ibs.re.kr
SUMMARY:Gyuyoung Hwang\, A universal description of stochastic oscillators
DESCRIPTION:In this talk\, we discuss the paper “A universal description of stochastic oscillators”\, by Alberto Perez-Cervera et. al.\, PNAS\, 2023. \nAbstract  \nMany systems in physics\, chemistry\, and biology exhibit oscillations with a pronounced random component. Such stochastic oscillations can emerge via different mechanisms\, for example\, linear dynamics of a stable focus with fluctuations\, limit-cycle systems perturbed by noise\, or excitable systems in which random inputs lead to a train of pulses. Despite their diverse origins\, the phenomenology of random oscillations can be strikingly similar. Here\, we introduce a nonlinear transformation of stochastic oscillators to a complex-valued function Q1*(x) that greatly simplifies and unifies the mathematical description of the oscillator’s spontaneous activity\, its response to an external time-dependent perturbation\, and the correlation statistics of different oscillators that are weakly coupled. The function Q1* (x) is the eigenfunction of the Kolmogorov backward operator with the least negative (but nonvanishing) eigenvalue λ1 = μ1 + iω1. The resulting power spectrum of the complex-valued function is exactly given by a Lorentz spectrum with peak frequency ω1 and half-width μ1; its susceptibility with respect to a weak external forcing is given by a simple one-pole filter\, centered around ω1; and the cross-spectrum between two coupled oscillators can be easily expressed by a combination of the spontaneous power spectra of the uncoupled systems and their susceptibilities. Our approach makes qualitatively different stochastic oscillators comparable\, provides simple characteristics for the coherence of the random oscillation\, and gives a framework for the description of weakly coupled oscillators.
URL:https://www.ibs.re.kr/bimag/event/gyuyoung-hwang-a-universal-description-of-stochastic-oscillators/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240816T140000
DTEND;TZID=Asia/Seoul:20240816T160000
DTSTAMP:20260424T032417
CREATED:20240730T001615Z
LAST-MODIFIED:20240730T001615Z
UID:9903-1723816800-1723824000@www.ibs.re.kr
SUMMARY:Kevin Spinicci\, SMSSVD : Submatrix selection singular value decomposition
DESCRIPTION:In this talk\, we discuss the paper\, “SMSSVD : Submatrix selection singular value decomposition”\, by Rasmus Henningsson and Magnus Fontes\, Bioinformatics\, 2019. \nAbstract \n\nMotivation\nHigh throughput biomedical measurements normally capture multiple overlaid biologically relevant signals and often also signals representing different types of technical artefacts like e.g. batch effects. Signal identification and decomposition are accordingly main objectives in statistical biomedical modeling and data analysis. Existing methods\, aimed at signal reconstruction and deconvolution\, in general\, are either supervised\, contain parameters that need to be estimated or present other types of ad hoc features. We here introduce SubMatrix Selection Singular Value Decomposition (SMSSVD)\, a parameter-free unsupervised signal decomposition and dimension reduction method\, designed to reduce noise\, adaptively for each low-rank-signal in a given data matrix\, and represent the signals in the data in a way that enable unbiased exploratory analysis and reconstruction of multiple overlaid signals\, including identifying groups of variables that drive different signals. \n\n\nResults\nThe SMSSVD method produces a denoised signal decomposition from a given data matrix. It also guarantees orthogonality between signal components in a straightforward manner and it is designed to make automation possible. We illustrate SMSSVD by applying it to several real and synthetic datasets and compare its performance to golden standard methods like PCA (Principal Component Analysis) and SPC (Sparse Principal Components\, using Lasso constraints). The SMSSVD is computationally efficient and despite being a parameter-free method\, in general\, outperforms existing statistical learning methods.
URL:https://www.ibs.re.kr/bimag/event/kevin-spinicci-smssvd-submatrix-selection-singular-value-decomposition/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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