Seyednami Niyakan

Seyednami Niyakan

Ph.D. candidate

Texas A&M University

Biography

I'm a PhD candidate in Genomic Signal Processing (GSP) lab in Electrical and Computer Engineering department of Texas A&M University working under supervision of professor Xiaoning Qian. My research interests are Bayesian Machine Learning and its application in analyzing heterogeneous and high dimensional biological data with a focus on omics data analysis.

A significant part of my research is related to Bayesian deep learning models and their application in analyzing high dimensional data such as omics data. With this goal in mind, I have developed and published several Bayesian models and computational tools for analyzing different omics data technologies such as:

  • SimCD: Single-cell RNA-seq data analysis tool [Github] [Paper]
  • MUSTANG: Spatial Transcriptomics data analysis tool [Github] [Paper]
  • Joint Metabolomics and RNA-seq transcriptomics data analysis pipeline [Paper]

    • As of having interest in solving challenges in scRNA-seq data analysis, I participated in IEEE COVID-19 Single-cell transcriptomics Data Hackathon where I won first place by presenting Biomarker identification for COVID-19 severity based on BALF scRNA-seq data. To learn more about this work you can check on this Github page.

    Apart from academic research, I have experience working in collaborative industry environments right alongside engineers, scientists and biologists. During summer of 2021 I did an internship as a Bioinformatics scientist with Department of Quantitative health sciences at Mayo Clinic. My internship project was mainly focused on gene-drug relationship discovery and learning interactions between genes, drugs and diseases. In summer of 2022, I joined Genentech as a data scientist intern and I worked on developing pipelines for integrating and analyzing large single-cell RNA-seq datasets.

    Interests

    • Bayesian Statistics
    • Computational Biology
    • Machine Learning

    Education

    • PhD in Electrical Engineering, 2018 - present

      Texas A&M University

    • BSc in Electrical Engineering, 2013 - 2018

      Sharif University of Technology

    Featured Publications

    MUSTANG: MUlti-sample Spatial Transcriptomics data ANalysis tool with cross-sample transcriptional Guidance

    Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. We here present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression based similarity information sharing as well as spatial correlation in gene expression patterns within samples.
    Niyakan et al.
    PDF Code
    MUSTANG

    SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data

    Single-Cell RNA sequencing (scRNA-seq) measurements have facilitated genome-scale transcriptomic profiling of individual cells, with the hope of deconvolving cellular dynamic changes in corresponding cell sub-populations to better understand molecular mechanisms of different development processes...
    Niyakan et al.
    PDF Code
    SimCD

    Biomarker identification for COVID-19 severity based on BALF scRNA-seq data

    Recently, Single-cell RNA sequencing (scRNA-seq) transcriptome profiling of Bronchoalveolar lavage fluid (BALF) cells has been done to elucidate the potential mechanisms underlying in COVID-19. With the aim of better utilizing this atlas of BALF cells in response to the virus,...
    Niyakan et al.
    PDF Code Slides
    Biomarker identification for COVID-19 severity based on BALF scRNA-seq data

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