limma-voom
将线性模型和精度权重应用于 RNA-seq 数据的差异表达分析方法，继承了 limma 在芯片数据中的优势。 该方法通过 voom 转换为每个观测值分配精度权重，使得线性建模框架能够适用于计数数据。
| Property | Value |
|---|---|
| Purpose | 基于线性模型和精度权重的 RNA-seq 差异表达分析 |
| Time Complexity | O(n * g) |
| Space Complexity | O(g) |
| Year | 2014 |
| Category | Gene Expression Analysis |
Complexity Analysis
- Time Complexity:
O(n * g) - Space Complexity:
O(g)
Performance Insight: The time complexity of this algorithm is polynomial.
Note: Complexity analysis is based on theoretical models. Actual runtime is affected by input scale, hardware, and implementation optimizations. Benchmark for your specific workload.
Literature & Implementation
Related Tools
DESeq2 · edgeR · limma
Tags
differential-expression precision-weight linear-model bioconductor