multi_Differ {anamiR} | R Documentation |
This function will apply anova ,a statistical methods, for each gene or miRNA (row) to find not only whether expression data of multiple groups differential expressed or not, but also tell specifically two groups from all are differential expression.
multi_Differ(se, class, anova_p_value = 0.05, post_hoc = c("scheffe.test", "duncan.test", "HSD.test"), post_hoc_p_value = 0.05)
se |
|
class |
string. Choose one features from all rows of phenotype data. |
anova_p_value |
an numeric value indicating a threshold of p-value from anova for every genes or miRNAs (rows). Default is 0.05. |
post_hoc |
post hoc test for anova, including "scheffe.test", "HSD.test", "duncan.test". |
post_hoc_p_value |
an numeric value indicating a threshold of p-value from post hoc test for every genes or miRNAs (rows). Default is 0.05. |
data.frame format with extra columns containing information about differential expressed groups among all.
aov
for fit an analysis of variance model.
## Use the internal dataset data("mirna", package = "anamiR", envir = environment()) data("pheno.mirna", package = "anamiR", envir = environment()) ## SummarizedExperiment class require(SummarizedExperiment) mirna_se <- SummarizedExperiment( assays = SimpleList(counts=mirna), colData = pheno.mirna) ## Finding differential miRNA from miRNA expression data with anova aov <- multi_Differ(se = mirna_se, class = "Subtype", post_hoc = "scheffe.test")