{"id":10503,"date":"2025-01-01T15:18:47","date_gmt":"2025-01-01T06:18:47","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&p=10503"},"modified":"2025-01-01T15:18:47","modified_gmt":"2025-01-01T06:18:47","slug":"feature-selection-and-dimension-reduction-for-single-cell-rna-seq-based-on-a-multinomial-model-seokhwan-moon","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/feature-selection-and-dimension-reduction-for-single-cell-rna-seq-based-on-a-multinomial-model-seokhwan-moon\/","title":{"rendered":"Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model – Seokhwan Moon"},"content":{"rendered":"
In this talk, we discuss the paper “Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model” by F. W. Townes et.al., Genome Biology, 2019.<\/p>\n
Abstract\u00a0<\/strong><\/p>\n
Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this talk, we discuss the paper “Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model” by F. W. Townes et.al., Genome Biology, 2019. Abstract\u00a0 Single-cell … <\/p>\n