Mudassar Iqbal -- New Computational Methods for Single Cell Epigenomics and Multiomics data [ONLINE]
| Dates: | 15 November 2023 |
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| Times: | 13:00 - 14:00 |
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| What is it: | Seminar |
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| Organiser: | Department of Mathematics |
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| Who is it for: | University staff, External researchers, Current University students |
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| Speaker: | Mudassar Iqbal |
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Join us for this seminar by Mudassar Iqbal (University of Manchester) as part of the North West Seminar Series in Mathematical Biology and Data Sciences. Details of the full series can be found here https://www.cms.livjm.ac.uk/APMSeminar/
The talk will be hosted by the University of Liverpool and available to watch via zoom. Please contact carl.whitfield@manchester.ac.uk or mirela.domijan@liverpool.ac.uk for the zoom link, or sign up to the mailing list.
Abstract: In this talk, I will aim to discuss two current projects:
1 - Scaled Poisson mixture model for clustering of single cell chromatin accessibility data:
Single cell ATACseq methods are increasingly employed for studying chromatin accessibility. Due to technical issues in the sequencing protocols, there may be large differences in sequencing depth across individual cells/nuclei 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 artefacts rather than by the underlying biology. We develop a probabilistic mixture model-based clustering approach where the underlying Poisson distribution is scaled with a cell-specific parameter modelling sequencing depth. We develop a bespoke EM based inference approach combined with a model-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 cell ATAC-seq datasets with annotated cell types and show that our method is tolerant to variation in sequencing depth and provides biologically meaningful clustering.
2 - Scalable joint non-negative matrix factorisation for paired single cell gene expression and chromatin accessibility data:
Non-negative Matrix Factorisation (NMF) can perform fast and interpretable dimensionality reduction. NMF based methods are becoming popular in single cell genomics field. We develop a joint matrix factorisation approach, intNMF, for paired single cell RNA and ATAC modalities. Our approach is highly scalable and generates an interpretable joint embedding (latent topics) to represent cells with multiple modalities as well as define/link latent topics to measured features in individual modalities. In this talk, I will introduce intNMF and present benchmarking results against the state of the art methods on publicly available large-scale paired RNA+ATAC datasets
To subscribe to the mailing list for this event series, please send an e-mail with the phrase “subscribe math-lifesci-seminar” in the message body to listserv@listserv.manchester.ac.uk
Speaker
Mudassar Iqbal
Role: Senior Lecturer in Health Data Sciences
Organisation: University of Manchester
Travel and Contact Information
