Monday, May 30, 2022 - 11:00 to 12:00
SCHOOL OF COMPUTER SCIENCE – Colloquium Series
The School of Computer Science at the University of Windsor is pleased to present…
PhD Seminar / Colloquium Presentation by Akram Vasighizaker
Date: Monday May 30, 2022
Meeting URL: https://us06web.zoom.us/j/83658492283?from=addon
Passcode: If interested in attending this event, contact the Graduate Secretary at firstname.lastname@example.org with sufficient notice before the event to obtain the passcode.
Identifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as clustering, are the most suitable approach in scRNA-seq data analysis when the cell types have not been well-characterized. These techniques can be used to identify a group of genes that belong to a specific cell type based on their similar gene expression patterns. However, due to the sparsity and high-dimensionality of scRNA-seq data, classical clustering methods are not efficient. Therefore, the use of non-linear dimensionality reduction techniques to improve clustering results is crucial. We introduce a method that is used to identify representative clusters of different cell types by combining non-linear dimensionality reduction techniques and clustering algorithms. We assess the impact of different dimensionality reduction techniques combined with the clustering of thirteen publicly available scRNA-seq datasets of different tissues, sizes, and technologies. We further performed gene set enrichment analysis to evaluate the proposed method's performance. As such, our results show that modified locally linear embedding combined with independent component analysis yields overall the best performance relative to the existing unsupervised methods across different datasets. This work is published in Scientific Reports Journal under the Nature Methods publication in Jan 2022.
Keywords: non-linear dimensionality reduction, clustering, single-cell RNA sequencing, cell type identification, unsupervised learning
Akram Vasighizaker is pursuing her Ph.D. at the School of Computer Science, the University of Windsor, in the domain of Machine Learning with a research focus on deep representation learning approaches. Also, she did her master's at Tarbiat Modares University, Tehran, Iran, in Computer Engineering. Akram has been awarded various scholarships, including Mitacs Award, the entrance scholarship in two opportunities, as well as the best paper award at the computer science conference. So far, she has published five journal papers, including Scientific Reports (under Nature Methods Publication) and IEEE/ACM Trans. Moreover, Akram has published eight conference papers and delivered five presentations as well, in top-tier conferences in machine learning and bioinformatics. Besides research, Akram has a passion for teaching, and her teaching experience with graduate and undergraduate students spans over a decade since she graduated with a bachelor's. In addition, she has participated in several extracurricular activities, which include being a student member of the WE-SPARK Health Institute and the Windsor Cancer Research Group, and the Canadian Association for Girls in Science (CAGIS), which let her engage in teaching Python to children. She actively follows up on her training exercise, and in her free time, she enjoys riding a bike!
PhD Dissertation Committee Members
Internal Reader: Dr. Saeed Samet
Internal Reader: Dr. Dima Alhadidi
External Reader: Dr. Esam Abdel-Raheem (Electrical and Computer Engineering)
Advisor: Dr. Luis Rueda
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