Session Details
[1BT-03]The latest trend in Single Cell Analysis with Illumina Solutions
Wed. Nov 27, 2024 11:45 AM - 12:35 PM JST
Wed. Nov 27, 2024 2:45 AM - 3:35 AM UTC
Wed. Nov 27, 2024 2:45 AM - 3:35 AM UTC
Room 3(Fukuoka International Congress Center, 4F 401+402+403)
chairperson:Reiko Fujiwara(Illumina K.K.)
This seminar will be led by Dr. Itoshi Nikaido, who will speak on the following topics. Illumina will also provide information on our latest new products, including PIPseq, an instrument-free single-cell solution scheduled for release in 2025.
Single-cell RNA sequencing (scRNA-seq) has been widely applied in various research fields for the analysis of cell type identification, lineage inference, and cell-cell interactions. With advancements in experimental methods, scRNA-seq has been evolving toward higher precision, larger scale, and multi-modal capabilities.
Meanwhile, the advent of ChatGPT has drawn attention to large-scale generative AI models (foundation models) that can handle multiple tasks within a single AI framework. Foundation models are expected to have applications beyond natural language processing, extending to scientific data, and omics fields have already seen the emergence of such AI models. Given that foundation models require vast amounts of training data, they are well-suited to large-scale data generated by scRNA-seq.
In this presentation, I will introduce recent technological advancements in scRNA-seq methods that enable large-scale data production, such as PIP-seq, which operates without specialized equipment, and the highly sensitive Quartz-Seq2. Furthermore, I will discuss the applications of generative AI to scRNA-seq data.
Single-cell RNA sequencing (scRNA-seq) has been widely applied in various research fields for the analysis of cell type identification, lineage inference, and cell-cell interactions. With advancements in experimental methods, scRNA-seq has been evolving toward higher precision, larger scale, and multi-modal capabilities.
Meanwhile, the advent of ChatGPT has drawn attention to large-scale generative AI models (foundation models) that can handle multiple tasks within a single AI framework. Foundation models are expected to have applications beyond natural language processing, extending to scientific data, and omics fields have already seen the emergence of such AI models. Given that foundation models require vast amounts of training data, they are well-suited to large-scale data generated by scRNA-seq.
In this presentation, I will introduce recent technological advancements in scRNA-seq methods that enable large-scale data production, such as PIP-seq, which operates without specialized equipment, and the highly sensitive Quartz-Seq2. Furthermore, I will discuss the applications of generative AI to scRNA-seq data.
[1BT-03-01]Diversifying Single-Cell RNA Sequencing Technologies and Their Applications in Generative AI
〇Itoshi Nikaido1 (1.Medical Research Laboratory (MRL), Institute of Integrated Research (IIR), Institute of Science Tokyo)
[1BT-03-02]Illumina's latest products for multi-omics analysis
〇Kensuke Suzuki1 (1.Illumina K.K.)