differExp_continuous: Find differential expression genes or miRNAs from given...

In AllenTiTaiWang/anamiR: An integrated analysis package of miRNA and mRNA expression data

Description

This function will apply linear regression model to find differential expression genes or miRNAs with continuous phenotype data,and then filter the genes or miRNAs (rows) which have bigger p-value than cutoff.

Usage

differExp_continuous(se, class, log2 = FALSE, p_value.cutoff = 0.05)

Arguments

se	SummarizedExperiment for input format.
class	string. Choose one features from all rows of phenotype data.
log2	logical, if this data hasn't been log2 transformed yet, this one should be TRUE. Default is FALSE.
p_value.cutoff	an numeric value indicating a threshold of p-value for every genes or miRNAs (rows). Default is 0.05.

Value

data expression data in matrix format, with sample name in columns and gene symbol or miRNA name in rows.

See Also

lm for fitting linear models.

Examples

## 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 lm
differExp_continuous(
    se = mirna_se, class = "Survival"
)

AllenTiTaiWang/anamiR documentation built on May 5, 2019, 4:55 a.m.