整理这几天看的文献的重要信息。
1. 去卷积方法
CIBERSORT
在Cell Reports那篇文章提供的TCIA网址中得到了关于LM22的细胞占比数据，之前不理解，这篇文章有详细的说明：
虽然在这篇文章末尾提到了
We anticipate that CIBERSORT will prove valuable for analysis of cellular heterogeneity in microarray or RNA-Seq data derived from fresh, frozen, and fixed specimens, thereby complementing methods that require living cells as input.
TIMER
它的主要工作结果为：
We developed a computational method to estimate the abundance of six tumor-infiltrating immune cell types (B cells, CD4 T cells, CD8 T cells, neutrophils, macrophages, and dendritic cells) to study 23 cancer types in The Cancer Genome Atlas (TCGA)
设计的大致流程如下：
它指出了自己开发的方法相对于CIBERSORT的优点：
值得注意的是，在文末提供的方法部分，作者详细地介绍了方法的计算流程，重点强调了
说明最后的计算结果是一个系数，它表明的只是免疫细胞相对的丰富度。
2. GSEA
文章对TIMER的观点是：
RNA expression data corrected for tumor purity were used to estimate infiltration of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells ([Li et al., 2016](javascript:void(0);)). However, although such analyses of few major cell types are helpful for identifying clinical associations, higher resolution of the TIL landscape is required in order to dissect tumor-immune cell interactions and identify prognostic and predictive markers.
因此，该文章提供了更为细致的免疫浸润组分图景，有近30种细胞类型
Thus, it is of utmost importance to provide a comprehensive view of the intratumoral immune landscape including memory cells, cytotoxic cells (CD8+ T cells, natural killer [NK] cells, and NK T [NKT] cells), as well as immunosuppressive cells (Tregs and myeloid-derived suppressor cells [MDSCs]).
we estimated 28 subpopulations of TILs including major types related to adaptive immunity: activated T cells, central memory (Tcm), effector memory (Tem) CD4+ and CD8+ T cells, gamma delta T (Tγδ) cells, T helper 1 (Th1) cells, Th2 cells, Th17 cells, regulatory T cells (Treg), follicular helper T cells (Tfh), activated, immature, and memory B cells, as well as cell types related to innate immunity, such as macrophages, monocytes, mast cells, eosinophils, neutrophils, activated, plasmacytoid, and immature dendritic cells (DCs), NK cells, natural killer T (NKT) cells, and MDSCs.
在构建的TCIA中，它使用了CIBERSORT去卷积方法进行免疫浸润的计算，方法是通过将TCGA的RNAseq数据转化为CIBERSORT能够处理的MicroArray数据集。
重点是，该文章提供了一种新的免疫浸润细胞组分计算的思路：就是使用基因富集分析。
下面介绍了该方法及其优点
Our approach is based on the use of metagenes, i.e., non-overlapping sets of genes that are representative for specific immune cell subpopulations and are neither expressed in CRC cell lines nor in normal tissue. The expression of these sets of metagenes is then used to analyze statistical enrichment using gene set enrichment analysis (GSEA). The advantage of the metagene approach is the robustness of the method due to two characteristics: (1) the use of a set of genes instead of single genes that represent one immune subpopulation, because the use of single genes as markers for immune subpopulations can be misleading as many genes are expressed in different cell types; and (2) the assessment of relative expression changes of a set of genes in relation to the expression of all other genes in a sample. Thus, the calculations are less sensitive to noise resulting from sample impurity or sample preparation compared with the deconvolution methods.
这两种方法都能够在TCIA上使用：
Enrichment bubble plot.png
该方法提供了免疫细胞在病人样本中的富集度（上图是以绝对数目显示，也可以显示富集的相对比例），通过NES(averaged normalized enrichment score)与FDR进行样本的预筛。默认设定为NES>0, Q-value(FDR) < 0.1。可以下载相应的文件，包含样本名（Barcode），NES和Q-value 3列。