ScholarGate
Асистент

Порівняння методів

Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.

Аналіз eQTL на рівні окремих клітин×Single-cell GWAS×
ГалузьБіоінформатикаБіоінформатика
РодинаProcess / pipelineProcess / pipeline
Рік появи20202019–2022 (rapid emergence with large-scale scRNA-seq atlases)
Автор методуCuomo et al.; Kim-Hellmuth et al. (pioneering sc-eQTL frameworks, 2020)Multiple groups (Price lab, De Jager lab, others); scDRS framework by Zhang et al. 2022
ТипStatistical genomics pipelineIntegrative genomic analysis pipeline
Основоположне джерелоCuomo, A. S. E., et al. (2020). Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nature Communications, 11(1), 810. link ↗Zhang, M. J., Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., ... & Price, A. L. (2022). Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nature Genetics, 54(8), 1224-1234. link ↗
Інші назвиsc-eQTL analysis, single-cell eQTL mapping, scRNA-seq eQTL, cell-type-specific eQTLsc-GWAS, single-cell GWAS integration, cell-type-specific GWAS, single-cell genetic association analysis
Пов'язані66
ПідсумокSingle-cell eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression in a cell-type-specific manner by jointly analysing single-cell RNA-seq profiles and donor genotype data. Unlike bulk eQTL methods, it resolves regulatory effects that are diluted or masked when cell types are mixed, enabling discovery of variants whose effects are confined to particular cell states or developmental stages.Single-cell GWAS is an integrative bioinformatics pipeline that maps genome-wide association study (GWAS) signals onto single-cell transcriptomic landscapes to identify which cell types and individual cells carry disproportionate genetic risk for a disease or trait. By leveraging single-cell RNA-seq atlases alongside GWAS summary statistics, it moves beyond tissue-level associations to reveal the precise cellular contexts in which disease-associated genetic variants exert their effects.
ScholarGateНабір даних
  1. v1
  2. 2 Джерела
  3. PUBLISHED
  1. v1
  2. 2 Джерела
  3. PUBLISHED

Перейти до пошуку Завантажити слайди

ScholarGateПорівняння методів: Single-cell eQTL analysis · Single-cell GWAS. Отримано 2026-06-18 з https://scholargate.app/uk/compare