השוואת שיטות
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| ניתוח ביטוי דיפרנציאלי ב-RNA-seq בייסיאני× | ניתוח RNA-seq של תא בודד× | |
|---|---|---|
| תחום | ביואינפורמטיקה | ביואינפורמטיקה |
| משפחה | Process / pipeline | Process / pipeline |
| שנת המקור≠ | 2010–2013 | 2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016 |
| הוגה השיטה≠ | Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq) | Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015) |
| סוג≠ | Bayesian statistical inference pipeline | High-throughput single-cell transcriptomic profiling pipeline |
| מקור מכונן≠ | Leng, N., Dawson, J. A., Thomson, J. A., Ruotti, V., Rissman, A. I., Smits, B. M., Haag, J. D., Gould, M. N., Stewart, R. M., & Kendziorski, C. (2013). EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics, 29(8), 1035–1043. link ↗ | 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. DOI ↗ |
| כינויים | Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq | scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling |
| קשורות≠ | 6 | 5 |
| תקציר≠ | Bayesian RNA-seq differential expression analysis applies hierarchical Bayesian models to RNA sequencing read-count data to identify genes whose expression levels differ significantly between biological conditions. Rather than relying solely on p-values, these methods quantify the posterior probability that a gene is differentially expressed, borrowing statistical strength across genes and naturally accommodating low sample sizes common in genomics experiments. | 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. |
| ScholarGateמערך נתונים ↗ |
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