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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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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
TZNAME:KST
DTSTART:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211126T100000
DTEND;TZID=Asia/Seoul:20211126T110000
DTSTAMP:20260427T090742
CREATED:20211124T190000Z
LAST-MODIFIED:20211122T014405Z
UID:5190-1637920800-1637924400@www.ibs.re.kr
SUMMARY:A Random Matrix Theory Approach to Denoise Single-Cell Data
DESCRIPTION:We will discuss about “A Random Matrix Theory Approach to Denoise Single-Cell Data”\, Aparicio et al.\, Patterns\, 2020 \nSingle-cell technologies provide the opportunity to identify new cellular states. However\, a major obstacle to the identification of biological signals is noise in single-cell data. In addition\, single-cell data are very sparse. We propose a new method based on random matrix theory to analyze and denoise single-cell sequencing data. The method uses the universal distributions predicted by random matrix theory for the eigenvalues and eigenvectors of random covariance/Wishart matrices to distinguish noise from signal. In addition\, we explain how sparsity can cause spurious eigenvector localization\, falsely identifying meaningful directions in the data. We show that roughly 95% of the information in single-cell data is compatible with the predictions of random matrix theory\, about 3% is spurious signal induced by sparsity\, and only the last 2% reflects true biological signal. We demonstrate the effectiveness of our approach by comparing with alternative techniques in a variety of examples with marked cell populations.
URL:https://www.ibs.re.kr/bimag/event/a-random-matrix-theory-approach-to-denoise-single-cell-data/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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