Người báo cáo: Đỗ Văn Hoàn (Học viện Kỹ thuật quân sự)
Thời gian: 9h00 đến 10h00 sáng thứ 4 ngày 12/10/2022 theo hình thức trực tuyến.
Tóm tắt:Emerging single cell genomics technologies such as single cell RNA-seq (scRNA-seq) provide new opportunities for discovery of previously unknown cell types and facilitating the study of biological processes such as cancer progression. Due to the high dimensionality of the data produced by the technologies, computation and mathematics have been the cornerstone in decoding meaningful information from the data. Computational models have been challenged by the exponential growth of the data thanks to the growth of large-scale genomic projects such as the Human Cell Atlas. In addition, recent single-cell technologies have enabled us to measure multiple modalities (characteristics) in the same cell. This requires us to establish new computational methods which can cope with multiple layers of the data. In this talk, we will introduce Sphetcher, a general framework to deal with big data. Sphetcher makes use of the thresholding technique to efficiently pick representative cells that evenly cover the transcriptomic space occupied by the original data set. We show that the spherical sketch computed by Sphetcher constitutes a more accurate representation of the original transcriptomic landscape. In addition, we will present j-SNE as the generalizations to the joint visualization of multimodal omics data, e.g., CITE-seq data that simultaneously measures gene and protein marker expression. We show that j-SNE can automatically learn the relative importance of each modality in order to obtain a concise representation of the data. Visualization using dimensionality reduction techniques such as j-SNE is a fundamental step in the analysis of high-dimensional data and it has played an important role in discovering the dynamic trends in multimodal omics data. |
