getResult {TCC} | R Documentation

## Obtain the summaries of results after the differential expression analysis

### Description

This function is generally used after the `estimateDE` function. It retrieves the summaries of differential expression (DE) results from TCC-class object. The retrieved information includes p-values, q-values, coordinates of M-A plot (i.e., M and A values), and so on.

### Usage

    getResult(tcc, sort = FALSE, ...)

### Arguments

`tcc` | TCC-class object
`sort` | logical. If `TRUE`, the retrieved results are sorted in order of the `stat$rank` field in the TCC-class object. If `FALSE`, the results are retrieved by the original order.
`...` | further arguments for calculating the coordinates of M-A plot. See `plot` for details.

### Value

A data frame object containing following fields:

`gene_id` | character vector indicating the id of the count unit, usually gene.
`a.value` | numeric vector of average expression level on log2 scale (i.e., A-value) for each gene across the compared two groups. It corresponds to the x coordinate in the M-A plot.
`m.value` | numeric vector of fold-change on \log_2 scale (i.e., M-value) for each gene between the two groups compared. It corresponds to the y coordinate in the M-A plot.
`p.value` | numeric vector of p-value.
`q.value` | numeric vector of q-value calculated based on the p-value using the `p.adjust` function with default parameter settings.
`rank` | numeric vector of gene rank in order of the p-values.
`estimatedDEG` | numeric vector consisting of 0 or 1 depending on whether each gene is classified as non-DEG or DEG. The threshold for classifying DEGs or non-DEGs is preliminarily given when performing `estimateDE`.

### Examples

    # Obtaining DE results by an exact test in edgeR coupled with
    # the DEGES/edgeR normalization factors.
    data(hypoData)
    group <- c(1, 1, 1, 2, 2, 2)
    tcc <- new("TCC", hypoData, group)
    tcc <- calcNormFactors(tcc, norm.method = "tmm", test.method = "edger",
                           iteration = 1, FDR = 0.1, floorPDEG = 0.05)
    tcc <- estimateDE(tcc, test.method = "edger", FDR = 0.1)
    result <- getResult(tcc, sort = TRUE)
    head(result)

    # Obtaining DE results by an negative binomial test in DESeq
    # coupled with the iterative DEGES/DESeq normalization method.
    tcc <- new("TCC", hypoData, group)
    tcc <- calcNormFactors(tcc, norm.method = "deseq", test.method = "deseq",
                           iteration = 1, FDR = 0.1, floorPDEG = 0.05)
    tcc <- estimateDE(tcc, test.method = "deseq", FDR = 0.1)
    result <- getResult(tcc, sort = TRUE)
    head(result)

[Package TCC version 1.24.0 Index]