Linganisha mbinu

	Uchanganuzi wa RNA-seq wa seli moja	Uchambuzi wa kipekee wa chembechembe
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016	2016 (Monovar; foundational single-cell SNV calling)
Mwanzilishi	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)	Hamim Zafar, Ken Chen, Nicholas Navin and colleagues
Aina	High-throughput single-cell transcriptomic profiling pipeline	Computational genomics pipeline
Chanzo asilia	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.	Zafar, H., Wang, Y., Nakhleh, L., Navin, N., & Chen, K. (2016). Monovar: single-nucleotide variant detection in single cells. Nature Methods, 13(6), 505–507.
Majina mbadala	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling	scVariant calling, single-cell SNV calling, scDNA-seq variant detection, single-cell somatic mutation calling
Zinazohusiana	5	1
Muhtasari	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.	Single-cell variant calling is a bioinformatics pipeline that identifies DNA sequence variants — single-nucleotide variants (SNVs), small insertions and deletions, and copy-number alterations — within individual cells rather than across a bulk tissue mixture. By resolving the mutational landscape cell by cell, it reveals intra-tumoral heterogeneity, clonal architecture, and somatic mutation patterns that bulk sequencing obscures. The approach is central to cancer genomics, developmental biology, and any study where cell-to-cell genetic diversity is the primary question.