IDEAS: individual level differential expression analysis for single-cell RNA-seq data.
Mengqi ZhangSi LiuZhen MiaoFang HanRaphael GottardoWei SunPublished in: Genome biology (2022)
We consider an increasingly popular study design where single-cell RNA-seq data are collected from multiple individuals and the question of interest is to find genes that are differentially expressed between two groups of individuals. Towards this end, we propose a statistical method named IDEAS (individual level differential expression analysis for scRNA-seq). For each gene, IDEAS summarizes its expression in each individual by a distribution and then assesses whether these individual-specific distributions are different between two groups of individuals. We apply IDEAS to assess gene expression differences of autism patients versus controls and COVID-19 patients with mild versus severe symptoms.
Keyphrases
- rna seq
- single cell
- genome wide identification
- gene expression
- high throughput
- end stage renal disease
- electronic health record
- genome wide
- coronavirus disease
- chronic kidney disease
- sars cov
- newly diagnosed
- ejection fraction
- autism spectrum disorder
- big data
- data analysis
- binding protein
- machine learning
- patient reported outcomes
- deep learning
- physical activity