Linganisha mbinu

	Uchambuzi wa eQTL	Uchanganuzi wa RNA-seq wa seli moja
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2001 (term coined); widely adopted after 2005	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Mwanzilishi	Ritsert C. Jansen & Jan-Peter Nap	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
Aina	Association mapping method	High-throughput single-cell transcriptomic profiling pipeline
Chanzo asilia	Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391.	Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502.
Majina mbadala	eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
Zinazohusiana	6	5
Muhtasari	eQTL analysis identifies genomic loci (variants, typically SNPs) whose genotype statistically associates with variation in the expression level of one or more genes. By jointly profiling DNA-level variation and RNA-level expression in the same individuals, eQTL studies decode the regulatory grammar of the genome — revealing which variants control how much a gene is transcribed, in which tissues, and under what conditions.	Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations.