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Ph.D Proposal: Marmar Moussa
May 18, 2018 @ 12:00 pm - 1:00 pm UTC-5
Title: Computational Methods for the Analysis of Single Cell RNA-Seq Data.
PhD Candidate: Marmar Moussa
Major Advisor: Dr. Ion Mandoiu
Associate Advisors: Dr. Mukul Bansal, Dr. Sheida Nabavi
Day/Time: Friday, May 18, 2018 12:00 noon
Location: Homer Babbidge Library Class of 1947 Conference Room
Abstract:
Single cell transcriptional profiling is critical for understanding cellular heterogeneity and identification of novel cell types as well as for studying growth and development of tissues and tumors. Leveraging the recent advances in single cell RNA sequencing (scRNA-Seq) technology requires novel computational methods that are robust to high levels of technical and biological noise and scale to datasets of millions of cells.
In this work, we address several challenges in the analysis work-flow of scRNA-seq data: First, we propose novel computational approaches for unsupervised clustering of scRNA-seq data based on the Term Frequency – Inverse Document Frequency (TF-IDF) (a transformation that has been successfully used in the field of text analysis). For this part, we present empirical experimental results showing that TF-IDF methods consistently outperform commonly used scRNA-Seq clustering approaches.
Second, we study the so called ‘drop-out’ effect that is considered one of the most notable challenges in scRNA-Seq data analysis, where only a fraction of the transcriptome of each cell is captured. The random nature of drop-outs, however, makes it possible to consider imputation methods as means of correcting for drop-outs. In this part we study some existing scRNA-Seq imputation methods and propose a novel iterative imputation approach based on efficiently computing highly similar cells. We then present the results of a comprehensive assessment of existing and proposed methods on real scRNA-Seq datasets with varying per cell sequencing depth.
Third, we present the current work in progress, a web-based work-flow for the analysis and interactive visualization of single-cell RNA-seq data.
And finally, as part of the single cell analysis work-flow and our future work, we study current computational methods for assigning and/or ordering cells based on their cell-cycle stages from single-cell transcriptome data and identify open issues in this area.
