immune_infiltrates_rnaseq.r RNA-seq 转录组数据免疫侵润分析

使用方法：

$ Rscript immune_infiltrates_rnaseq.r -h
usage: immune_infiltrates_rnaseq.r
       [-h] -i expset [-g gene.info] [-t type] [--tpm] [-o outdir]
RNA seq 免疫侵润分析. In general values should be TPM-normalized ,not log-
transformed.
optional arguments:
  -h, --help            show this help message and exit
  -i expset, --expset expset
                        input gene expression set matrix from RNA-seq data tsv
                        format [required]
  -g gene.info, --gene.info gene.info
                        input gene info data [required]
  -t type, --type type  TIMER uses indication-specific reference profiles.
                        [optional]
  --tpm                 whether convert fpkm to tpm [optional, default: False]
  -o outdir, --outdir outdir
                        output file directory [default cwd]

参数说明：

-i 输入基因表达量文件，建议用TPM标准化之后的数据，如果是FPKM可以设置--tpm进行转换；第一列ID为基因NAME

ID	TCGA-D7-A74A-01A-11R-A32D-31	TCGA-BR-7704-01A-11R-2055-13	TCGA-VQ-A91N-01A-11R-A414-31	TCGA-CD-A4MH-01A-11R-A251-31
NUP50	18.65505	31.59232	28.23382	28.76485
CXCR4	64.85805	125.123	56.35244	69.98976
NT5E	111.4818	69.8587	79.37382	25.05824
EFNA3	8.247857	42.03308	43.46432	26.66024
STC1	4.781111	21.36327	40.81077	19.51568
ZBTB7A	95.51678	103.4768	158.3024	126.2677
CLDN9	1.187456	2.476138	0.366081	7.347344

-t 指定癌症类型， TIMER计算需要，可以指定类型，如果不指定不输出TIMER的分析结果：

# "kich" "blca" "brca" "cesc" "gbm" "hnsc" "kirp" "lgg" "lihc" "luad"

# "lusc" "prad" "sarc" "pcpg" "paad" "tgct" "ucec" "ov" "skcm" "dlbc"

# "kirc" "acc" "meso" "thca" "uvm" "ucs" "thym" "esca" "stad" "read"

# "coad" "chol"

方法说明：

使用R包：immunedeconv

可以输出以下方法的免疫侵润分析结果，mcp_counter 需要联网才可以运行，有时报错不出结果；

quantiseq
timer
cibersort
cibersort_abs
mcp_counter
xcell
epic

不同方法文献引用：https://github.com/icbi-lab/immunedeconv

quanTIseq	free (BSD)	Finotello, F., Mayer, C., Plattner, C., Laschober, G., Rieder, D., Hackl, H., ..., Sopper, S. (2019). Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome medicine, 11(1), 34. https://doi.org/10.1186/s13073-019-0638-6
TIMER	free (GPL 2.0)	Li, B., Severson, E., Pignon, J.-C., Zhao, H., Li, T., Novak, J., … Liu, X. S. (2016). Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biology, 17(1), 174. https://doi.org/10.1186/s13059-016-1028-7
CIBERSORT	free for non-commerical use only	Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., … Alizadeh, A. A. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods, 12(5), 453–457. https://doi.org/10.1038/nmeth.3337
MCPCounter	free (GPL 3.0)	Becht, E., Giraldo, N. A., Lacroix, L., Buttard, B., Elarouci, N., Petitprez, F., … de Reyniès, A. (2016). Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biology, 17(1), 218. https://doi.org/10.1186/s13059-016-1070-5
xCell	free (GPL 3.0)	Aran, D., Hu, Z., & Butte, A. J. (2017). xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biology, 18(1), 220. https://doi.org/10.1186/s13059-017-1349-1
EPIC	free for non-commercial use only (Academic License)	Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E., & Gfeller, D. (2017). Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. ELife, 6, e26476. https://doi.org/10.7554/eLife.26476

免疫侵润分析方法

基于标记基因的方法 a
基于反卷积的方法 b

(a)基于标记基因的方法： 基于基因列表（signature），代表细胞类型的特征。通过查看特征基因的表达值，可以独立量化每种细胞类型，直接使用基因表达值（MCP-counter）或通过执行特征富集的统计测试（xCell）。

(b)反卷积方法： 将问题表述为一个方程组，该方程组将样本的基因表达描述为不同细胞类型贡献的加权总和。通过求解逆问题，可以在给定特征矩阵和混合基因表达的情况下推断细胞类型分数。如 ν-Support Vector Regression (SVR) (CIBERSORT) ，约束最小二乘回归 (quantTIseq, EPIC) 或线性最小二乘回归 (TIMER)。

有关更多信息，请查看Finotello 和 Trajanoski ( 2018 ) 的综述。

不同软件计算免疫得分scores解读

In general, cell-type scores allow for the comparison (1) between samples, (2) between cell-types or (3) both. Between-sample comparisons allow to make statements such as “In patient A, there are more CD8+ T cells than in patient B”. Between-cell-type comparisons allow to make statements such as “In a certain patient, there are more B cells than T cells”. For more information, see our Benchmark paper (Sturm et al. (2019)).

Methods that allow between-sample comparisons

MCP-counter
xCell
TIMER

Methods that allow between-cell-type comparisions

CIBERSORT

Methods that allow both

EPIC
quanTIseq

发表于 2021-06-15 16:18
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分类：TCGA