เปรียบเทียบวิธี

	การวิเคราะห์ข้อมูล RNA-seq ระดับเซลล์เดี่ยวแบบอนุกรมเวลา	การวิเคราะห์ความแปรผันของจำนวนสำเนา
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2014-2018 (pseudotime and RNA velocity frameworks)	1998–2006
ผู้ริเริ่ม	Trapnell et al. (pseudotime/Monocle); La Manno et al. (RNA velocity)	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
ประเภท	Computational bioinformatics pipeline	Genomic structural variant detection pipeline
แหล่งต้นตำรับ	Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology, 32(4), 381-386.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
ชื่อเรียกอื่น	scRNA-seq time course analysis, longitudinal scRNA-seq, temporal single-cell transcriptomics, dynamic single-cell gene expression analysis	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
ที่เกี่ยวข้อง	6	6
สรุป	Time-series single-cell RNA-seq analysis captures gene expression across multiple time points at single-cell resolution to reveal how cell populations emerge, transition, and diverge during dynamic biological processes such as development, differentiation, or disease progression. By combining pseudotime ordering, RNA velocity, and differential dynamics testing, researchers reconstruct the temporal trajectory of individual cells and identify the gene regulatory changes that drive biological transitions.	Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population diversity. The pipeline typically processes SNP array intensities or read-depth signals from whole-genome sequencing, applies segmentation algorithms, calls gain and loss events, and annotates them against gene and clinical databases.