GSEA_res {anamiR} | R Documentation

## Pipeline of anamiR is applied to given output from GSEA_ana.

### Description

This function will use differExp_discrete and negative_cor to do the deeper analysis of given data which is from GSEA_ana.

### Usage

    GSEA_res(table, pheno.data, class, DE_method = c("t.test", "limma",
      "wilcox.test", "DESeq"), limma.trend = FALSE, t_test.var = FALSE,
      log2 = FALSE, p_adjust.method = "BH", cor_cut = -0.3)

### Arguments

`table` | list format containing both selected gene and miRNA expression data for each chosen pathway. output of GSEA_ana
`pheno.data` | phenotype data.
`class` | string. Choose one features from all rows of phenotype data.
`DE_method` | statistical method for finding differential genes or miRNAs, including "t.test", "wilcox.test", "limma". Default is "t.test".
`limma.trend` | logical, only matter when limma is chosen to be the method. From function `eBayes`.
`t_test.var` | logical, only matter when limma is chosen to be the method. Whether to treat the two variances as being equal. From function `t.test`
`log2` | logical, if this data hasn't been log2 transformed yet, this one should be TRUE. Default is FALSE.
`p_adjust.method` | Correction method for multiple testing. (If you are using DESeq for method, this param would not affect the result) From function `p.adjust`. Default is "BH".
`cor_cut` | an numeric value indicating a threshold of correlation coefficient for every potential miRNA-genes interactions. Default is -0.3, however, if no interaction pass the threshold, this function would add 0.2 value in threshold until at least one interaction passed the threshold.

### Value

list format containing matrix for each chosen pathway. The format of matrix is like the output from negative_cor.

### See Also

differExp_discrete and negative_cor.

### Examples

    ## Load example data

    require(data.table)

    cc <- system.file("extdata", "pheno_data.csv", package = "anamiR")
    pheno.data <- fread(cc, fill = TRUE, header = TRUE)

    ## adjust data format
    pheno_name <- pheno.data[["Sample"]]
    pheno.data <- pheno.data[, -1]
    pheno.data <- as.matrix(pheno.data)
    row.names(pheno.data) <- pheno_name
    data(table_pre)

    result <- GSEA_res(table = table_pre, pheno.data = pheno.data,
     class = "ER", DE_method = "limma", cor_cut = 0)

[Package anamiR version 1.8.0 Index]