参考文献:Quantifying tumor-infiltrating immune cells from transcriptomics data
从转录组学数据中量化肿瘤浸润性免疫细胞
这是一篇关于免疫量化的综述,列举了十多种量化免疫浸润的方法，部分方法可以使用CellMix 包的一些函数实现
Features of the computational tools for the quantification of tumor-infiltrating immune cells from transcriptomics data considered in this review: tool or function name, algorithm type** (M = marker genes, P = partial deconvolution, C = complete deconvolution)**, main method, cell types quantified using the embedded gene sets or signature profiles, code availability, name of the method in the CellMix package [9], reference publication
- M = marker genes 基于特征基因
- P = partial deconvolution 基于部分去卷积
- C = complete deconvolution) 完全去卷积
| Tool | Type | Method | Cell types | Code availability | CellMix | ref |
|---|---|---|---|---|---|---|
| TIminer | M | PrerankedGSEA | Different gene sets with 31 [10], 28 [11], and 64 cell types [12] | http://icbi.i-med.ac.at/software/timiner/timiner.shtml(Docker image) | --- | 13 |
| xCell | M | ssGSEA | 64 immune and non-immune cell types | http://xcell.ucsf.edu/ (R script, web tool) | --- | 12 |
| MCP-counter | M | Geometric mean of expression of marker genes | 8 immune cells, fibroblasts, and endothelial cells | http://github.com/ebecht/MCPcounter (R script) | --- | 14 |
| --- | P | Linear least squares regression | 17 immune cell types | --- | lsfit | 15 |
| --- | P | Constrained least square regression | --- | qprog | 16 | |
| DeconRNASeq | P | Constrained least square regression | --- | DeconRNASeq package available on Bioconductor (R package) | --- | 17 |
| PERT | P | Non-negative maximum likelihood | --- | Supplementary material in the original publication (Octave) | --- | 18 |
| CIBERSORT | P | Nu support vector regression | 22 immune cell types | https://cibersort.stanford.edu/ (R script, java executable, web tool) | --- | 19 |
| TIMER | P | Linear least square regression | 6 immune cell types | https://cistrome.shinyapps.io/timer/](https://cistrome.shinyapps.io/timer/) (web tool | --- | 20 |
| EPIC | P | Constrained least square regression | 6 immune cell types, fibroblasts, endothelial cells, and uncharacterized cells | https://gfellerlab.shinyapps.io/EPIC_1-1 (R script, web-interface) | --- | 21 |
| quanTIseq | P | Constrained least square regression | 10 immune cell types, uncharacterized cells | http://icbi.i-med.ac.at/software/quantiseq/doc/index.html (Docker image) | --- | 22 |
| deconf | C | Non-negative matrix factorization | --- | Supplementary material in the original publication (R package) | deconf | 23 |
| ssKL | C | Non-negative matrix factorization | --- | --- | ssKL | 24 |
| ssFrobenius | C | Non-negative matrix factorization | --- | --- | ssFrobenius | 25 |
| DSA | C | Quadratic programming | --- | https://github.com/zhandong/DSA (R package) | dsa | 26 |
| MMAD | C | Maximum likelihood over the residual sum of squares | --- | http://sourceforge.net/projects/mmad/(Matlab) | --- | 27 |
对于这些方法的简单描述，将在后续的文章中会提及一下
