השוואת שיטות
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| מיפוי רצפי תא בודד× | ניתוח ביטוי דיפרנציאלי ב-RNA-seq× | |
|---|---|---|
| תחום | ביואינפורמטיקה | ביואינפורמטיקה |
| משפחה | Process / pipeline | Process / pipeline |
| שנת המקור≠ | 2013–2016 | 2008–2010 (RNA-seq DE methodology established) |
| הוגה השיטה≠ | Adapted from bulk RNA-seq aligners; single-cell extensions by Dobin et al. (STAR) and 10x Genomics Cell Ranger team | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| סוג≠ | Computational pipeline step | Quantitative genomics pipeline |
| מקור מכונן≠ | Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21. DOI ↗ | Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. DOI ↗ |
| כינויים | scRNA-seq alignment, single-cell read mapping, scSeq alignment, cell barcode-aware alignment | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| קשורות≠ | 1 | 6 |
| תקציר≠ | Single-cell sequence alignment is the computational step that maps millions of short sequencing reads produced by single-cell RNA-seq experiments back to a reference genome or transcriptome. Unlike bulk RNA-seq alignment, each read carries a cell barcode and a Unique Molecular Identifier (UMI) that together identify the originating cell and the individual RNA molecule. Accurate alignment and barcode demultiplexing are prerequisites for constructing the cell-by-gene count matrix that drives all downstream single-cell analyses. | RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values. |
| ScholarGateמערך נתונים ↗ |
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