Texas A&M University 3 credits VIBS
Principles and concepts in single-cell RNA sequencing (scRNAseq) experiments; real-world applications of scRNAseq with examples; machine learning (ML) methods for single-cell data analysis; practical and effective ML methods and concepts; applications of ML methods in high-dimensional scRNAseq data; algorithm design and development of scientific software using high-level high-performance scientific computer languages; emerging techniques for integrative single-cell data analysis, and the assumptions, advantages, and limitations of these techniques. Prerequisites: Graduate classification and approval of instructor.
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