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 M3//EN
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CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20231109T160944Z
DTSTART:20231115T130000Z
DTEND:20231115T140000Z
SUMMARY:Mudassar Iqbal -- New Computational Methods for Single Cell Epige
 nomics and Multiomics data [ONLINE]
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}a1wk-lmp18h
 35-1b3j61
DESCRIPTION:Join us for this seminar by Mudassar Iqbal  (University of Ma
 nchester) as part of the North West Seminar Series in Mathematical Biolo
 gy and Data Sciences. Details of the full series can be found here https
 ://www.cms.livjm.ac.uk/APMSeminar/\n\nThe talk will be hosted by the Uni
 versity of Liverpool and available to watch via zoom. Please contact car
 l.whitfield@manchester.ac.uk or mirela.domijan@liverpool.ac.uk for the z
 oom link\, or sign up to the mailing list.\n\nAbstract: In this talk\, I
  will aim to discuss two current projects:\n1 - Scaled Poisson mixture m
 odel for clustering of single cell chromatin accessibility data:\nSingle
  cell ATACseq methods are increasingly employed for studying chromatin a
 ccessibility. Due to technical issues in the sequencing protocols\, ther
 e may be large differences in sequencing depth across individual cells/n
 uclei and this can strongly impact on the downstream analysis. Commonly 
 employed clustering approaches can be influenced by the sequencing depth
  of the cells and can produce clusters defined by these sequencing artef
 acts rather than by the underlying biology. We develop a probabilistic m
 ixture model-based clustering approach where the underlying Poisson dist
 ribution is scaled with a cell-specific parameter modelling sequencing d
 epth. We develop a bespoke EM based inference approach combined with a m
 odel-based feature-selection to improve performance. Our method is able 
 to robustly identify clusters using informative open chromatin features.
  We validate our method on synthetic data and apply it to real single ce
 ll ATAC-seq datasets with annotated cell types and show that our method 
 is tolerant to variation in sequencing depth and provides biologically m
 eaningful clustering.\n\n2 - Scalable joint non-negative matrix factoris
 ation for paired single cell gene expression and chromatin accessibility
  data:\nNon-negative Matrix Factorisation (NMF) can perform fast and int
 erpretable dimensionality reduction. NMF based methods are becoming popu
 lar in single cell genomics field. We develop a joint matrix factorisati
 on approach\, intNMF\, for paired single cell RNA and ATAC modalities. O
 ur approach is highly scalable and generates an interpretable joint embe
 dding (latent topics) to represent cells with multiple modalities as wel
 l as define/link latent topics to measured features in individual modali
 ties. In this talk\, I will introduce intNMF and present benchmarking re
 sults against the state of the art methods on publicly available large-s
 cale paired RNA+ATAC datasets\n\nTo subscribe to the mailing list for th
 is event series\, please send an e-mail with the phrase “subscribe math-
 lifesci-seminar” in the message body to listserv@listserv.manchester.ac.
 uk
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Online
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