scANVI
结合变分自编码器与半监督学习的单细胞数据注释方法，能利用少量已知标签对大量未标注细胞进行自动类型推断。 该方法在保留 scVI 批次校正能力的同时引入细胞类型信息，显著提升跨数据集注释的准确性和可解释性。
| Property | Value |
|---|---|
| Purpose | 基于半监督深度学习的单细胞类型注释 |
| Time Complexity | O(c * g * e) |
| Space Complexity | O(c * g) |
| Year | 2021 |
| Difficulty | Advanced |
| Languages | Python |
| Category | Single-Cell Genomics |
Complexity Analysis
- Time Complexity:
O(c * g * e) - Space Complexity:
O(c * 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
scVI · CellTypist · scArches