R包GSVA富集分析的方法

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使用方法:

Rscript ../scripts/ssgsea_enrich_diff.r -husage: ../scripts/ssgsea_enrich_diff.r [-h] -g GMTFILE -i EXPR -m META                                       [-n GROUP_NAME] [--log2] [-t method]                                       [-k kcdf] [--group1 GROUP1]                                       [--group2 GROUP2] [-p PVALUECUTOFF]                                       [--no_diff] [-o OUTDIR] [-f PREFIX]Gene set variation analysis (GSVA):https://www..com/article/1586optional arguments:  -h, --help            show this help message and exit  -g GMTFILE, --gmtfile GMTFILE                        GSEA gmtfile function class file[required]  -i EXPR, --expr EXPR  Input gene expression file path[required]  -m META, --meta META  Input the clinical information file path that contains                        the grouping[required]  -n GROUP_NAME, --group_name GROUP_NAME                        Specifies the column name that contains grouping                        information[optional,default:m6acluster]  --log2                Whether to perform log2                        processing[optional,default:False]  -t method, --method method                        Method to employ in the estimation of gene-set                        enrichment scores per sample. By default this is set                        to gsva (Hänzelmann et al, 2013) and other options are                        ssgsea (Barbie et al, 2009), zscore (Lee et al, 2008)                        or plage (Tomfohr et al, 2005). The latter two                        standardize first expression profiles into z-scores                        over the samples and, in the case of zscore, it                        combines them together as their sum divided by the                        square-root of the size of the gene set, while in the                        case of plage they are used to calculate the singular                        value decomposition (SVD) over the genes in the gene                        set and use the coefficients of the first right-                        singular vector as pathway activity profile[default                        gsva]  -k kcdf, --kcdf kcdf  Character string denoting the kernel to use during the                        non-parametric estimation of the cumulative                        distribution function of expression levels across                        samples when method="gsva". By default,                        kcdf="Gaussian" which is suitable when input                        expression values are continuous, such as microarray                        fluorescent units in logarithmic scale, RNA-seq log-                        CPMs, log-RPKMs or log-TPMs. When input expression                        values are integer counts, such as those derived from                        RNA-seq experiments, then this argument should be set                        to kcdf="Poisson"[default Gaussian]  --group1 GROUP1       Designate the first group[optional,default C1]  --group2 GROUP2       Designate the second group[optional,default C2]  -p PVALUECUTOFF, --pvalueCutoff PVALUECUTOFF                        pvalue cutoff on enrichment tests to                        report[optional,default:0.05]  --no_diff             No screening was performed based on the difference                        analysis results[optional,default:False]  -o OUTDIR, --outdir OUTDIR                        output file directory[optional,default cwd]  -f PREFIX, --prefix PREFIX                        out file name prefix[optional,default kegg]

参数说明：

-g 参考基因集，如从MSigDB下载的KEGG基因集c2.cp.kegg.v6.2.symbols.gmt

-i 基因表达矩阵

-m 包含样本分组信息的样本信息文件

--log2 是否对基因表达矩阵进行log2转换

-n 指定样本信息文件中分组信息的列名

--group1 --group2 指定分组组名

-t 指定用于估计基因集的方法，默认为gsva

-k 指定gsva函数中的kcdf参数，使用read count数据时一般设为"Poisson"，使用log后的TPM等数据时一般就用默认值"Gaussian"

-p 指定p的阈值

--no_diff GSVA会先将表达矩阵转换成富集分数矩阵，然后再通过差异表达分析筛选富集结果，设置这个参数则不对富集结果进行筛选

使用举例：

Rscript ../scripts/ssgsea_enrich_diff.r -g enrich/c2.cp.kegg.v6.2.symbols.gmt \    -i ../02.sample_select/TCGA-KIRC_gene_expression_TPM_immu.tsv -m metadata_group.tsv \    -n m6acluster --log2 --group1 C1 --group2 C2 -p 0.05 -o enrich/C1_vs_C2

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