পদ্ধতির তুলনা করুন

	নেটওয়ার্ক-ভিত্তিক RNA-seq ডিফারেনশিয়াল এক্সপ্রেশন বিশ্লেষণ	একক-কোষ আরএনএ-সিকোয়েন্স বিশ্লেষণ
ক্ষেত্র	জৈব তথ্যবিজ্ঞান	জৈব তথ্যবিজ্ঞান
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2002–2005	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
প্রবর্তক	Ideker et al. (network scoring); Zhang & Horvath (WGCNA framework)	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
ধরন	Integrative computational pipeline	High-throughput single-cell transcriptomic profiling pipeline
মৌলিক উৎস	Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1), Article 17.	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.
অপর নাম	network-aware DE analysis, gene network differential expression, co-expression network DE, NB-DEA	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
সম্পর্কিত	5	5
সারসংক্ষেপ	Network-based RNA-seq differential expression analysis integrates conventional differential expression testing with gene interaction networks — such as protein-protein interaction graphs or weighted co-expression networks — to identify not just individual differentially expressed genes but coherent, biologically meaningful gene modules that change together between conditions. This approach substantially reduces false positives and surfaces pathway-level signals invisible to gene-by-gene testing.	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.