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TZID:Asia/Jerusalem

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UID:1128@biology.technion.ac.il

DTSTART;TZID=Asia/Jerusalem:20221207T130000

DTEND;TZID=Asia/Jerusalem:20221207T133000

DTSTAMP:20221102T093818Z

URL:https://biology.technion.ac.il/en/seminars/msc-graduate-seminar-or-hev
 deli-aran-lab/

SUMMARY:Msc Graduate Seminar-Or Hevdeli [No Categories]
DESCRIPTION:Location: hybrid- in the Faculty Auditorium/ZOOM:  Or Hevdeli\n
  Affiliation: \n Host:Dr. Dvir Aran\n &nbsp\;\n\nReducing scRNA-seq data v
 ia nonnegative matrix factorization for annotation and clustering analysis
 \n\nabstract: \n\nSingle-cell RNA sequencing (scRNA-seq) provides an unpre
 cedented opportunity to dissect tissue heterogeneity and characterize indi
 vidual cells. Clustering similar cells and determining their cell type is 
 an essential step for analyzing scRNA-seq data. Due to the high dimension 
 of gene expression data and technical limitations of scRNA-seq\, to achiev
 e good clustering we first need to perform dimensionality reduction on the
  transcriptional data. Unsupervised approaches\, such as PCA\, are the com
 mon approach\, however\, current clustering algorithms that use the top PC
 s often fail to separate closely related\, but different cells from one an
 other\n\nHere\, we propose a novel mixed semi-supervised and unsupervised 
 nonnegative matrix factorization (NMF)-based framework for both dimensiona
 lity reduction and annotation. The framework consists of three phases: dec
 omposition\, projection\, and annotation prediction. In the first phase\, 
 variant methods of NMFs are tested to identify the latent genes base for r
 eference transcriptomic datasets of pure cell types. These latent bases de
 monstrate a combination of relating genes that generate a low-dimensional 
 space. Then\, unlabeled data is represented by the latent bases via solvin
 g a linear least squares problem. In the annotation phase\, the correlatio
 n between the reference and the test data is calculated using SingleR\, ba
 sed on the new representation. The proposed algorithm is evaluated on seve
 ral pairs of scRNA-seq datasets for annotation. Experimental results demon
 strate the effectiveness of our method when compared with annotation metho
 ds without dimensionality reduction. Furthermore\, it is possible to use l
 atent bases not only to reduce dimensions but also to characterize relatio
 nships between genes and discover new markers. We hope this approach will 
 assist researchers in extracting more accurate and novel insights from the
 ir scRNA-seq data and will encourage follow-up research in this field. 
LOCATION:hybrid- in the Faculty Auditorium/ZOOM:

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TZID:Asia/Jerusalem

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DTSTART:20221030T010000

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