negative_cor {anamiR} | R Documentation |
This function will calculate the correlation coefficient between each gene and miRNA from differential expressed data, which are produced by differExp_discrete or differExp_continuous. After filtering the positive and higher than cutoff value of correlation, this function would return a matrix with seven columns, including miRNA, gene, correlation coefficients and Fold change, P-adjust value for miRNA and gene. Each row represents one potential miRNA-target gene interaction.
negative_cor(mrna_data, mirna_data, method = c("pearson", "kendall", "spearman"), cut.off = -0.5)
mrna_data |
differential expressed data in matrix format, with sample name in columns and gene symbol in rows, which is generated by differExp_discrete or differExp_continuous. |
mirna_data |
differential expressed data in matrix format, with sample name in columns and miRNA in rows, which is generated by differExp_discrete or differExp_continuous, miRNA should be miRBase 21 version now. |
method |
methods for calculating correlation coefficient, including
"pearson", "spearman", "kendall". Default is "pearson". From function
|
cut.off |
an numeric value indicating a threshold of correlation coefficient for every potential miRNA-genes interactions. Default is -0.5, however, if no interaction pass the threshold, this function would add 0.2 value in threshold until at least one interaction passed the threshold. |
matrix format with each row indicating one potential miRNA-target gene interaction and seven columns are miRNA, gene, correlation coefficient and Fold change, P-adjust value for miRNA and gene.
cor
for calculation of correlation.
## Use the internal dataset data("mirna", package = "anamiR", envir = environment()) data("pheno.mirna", package = "anamiR", envir = environment()) data("mrna", package = "anamiR", envir = environment()) data("pheno.mrna", package = "anamiR", envir = environment()) ## SummarizedExperiment class require(SummarizedExperiment) mirna_se <- SummarizedExperiment( assays = SimpleList(counts=mirna), colData = pheno.mirna) ## SummarizedExperiment class require(SummarizedExperiment) mrna_se <- SummarizedExperiment( assays = SimpleList(counts=mrna), colData = pheno.mrna) ## Finding differential miRNA from miRNA expression data with t.test mirna_d <- differExp_discrete( se = mirna_se, class = "ER", method = "t.test" ) ## Finding differential mRNA from mRNA expression data with t.test mrna_d <- differExp_discrete( se = mrna_se, class = "ER", method = "t.test" ) ## Correlation cor <- negative_cor(mrna_data = mrna_d, mirna_data = mirna_d, method = "pearson" )