se_to_matrix {quantiseqr} | R Documentation

## SummarizedExperiment to matrix

### Description

SummarizedExperiment to matrix

### Usage

    se_to_matrix(se, assay = "abundance")

### Arguments

`se` | A `SummarizedExperiment` object, or any of its derivates, which contains the information on the TPM expression values, which are stored in a specified assay slot.
`assay` | A character string, specifying the name of the `assays` component of the `se` object. Defaults to "abundance", as this is the common convention used e.g. by the `tximport` package to store the values imported from the transcript level quantifications

### Value

A matrix object, containing the TPM values, ready to be used in the framework of `quantiseqr`

### Examples

    library("SummarizedExperiment")
    library("macrophage")
    data("gse", package = "macrophage")
    se <- gse

    # If using ENSEMBL or Gencode gene annotation, you might want to convert the row names
    ## in this case, the gene symbols are provided as rowData information
    rownames(se) <- rowData(se)$SYMBOL

    tpm_matrix <- se_to_matrix(se, assay = "abundance")

    ## otherwise, you can map the identifiers via
    library("org.Hs.eg.db")
    library("AnnotationDbi")
    se <- gse
    # keep the parts before the '.', used in the Gencode annotation
    rownames(se) <- substr(rownames(se), 1, 15)
    gene_names <- mapIds(org.Hs.eg.db,
                         keys = rownames(se),
                         column = "SYMBOL",
                         keytype = "ENSEMBL")
    rownames(se) <- gene_names

    # If you require to convert the counts to TPMs by hand, you need a vector of
    # gene lengths as well, and then run this simple function on the count matrix
    counts_to_tpm <- function(counts, lengths) {
      ratio <- counts / lengths
      mytpm <- ratio / sum(ratio) * 1e6
      return(mytpm)
    }
    # then run via
    # tpmdata <- counts_to_tpm(count_matrix, genelength_vector)

[Package quantiseqr version 1.2.0 Index]