differExp_continuous {anamiR} | R Documentation

## Find differential expression genes or miRNAs from given 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"
    )

[Package anamiR version 1.8.0 Index]