GSEA_res {anamiR} | R Documentation |
This function will use differExp_discrete and negative_cor to do the deeper analysis of given data which is from GSEA_ana.
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)
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 |
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
|
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 |
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. |
list format containing matrix for each chosen pathway. The format of matrix is like the output from negative_cor.
differExp_discrete and negative_cor.
## 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)