Make a SummarizedExperiment object

This function is a utility wrapper for SummarizedExperiment that provides automatic subsetting for row and column data, as well as automatic handling of transgenes and spike-ins.

Usage

makeSummarizedExperiment(
  assays = S4Vectors::SimpleList(),
  rowRanges = GenomicRanges::GRanges(),
  rowData = NULL,
  colData = S4Vectors::DataFrame(),
  metadata = list(),
  transgeneNames = NULL,
  denylist = TRUE,
  sort = TRUE,
  sessionInfo = TRUE
)

Arguments

assays: SimpleList. Count matrices, which must have matching dimensions. Counts can be passed in as either a dense matrix (matrix) or sparse matrix (sparseMatrix).
rowRanges: GenomicRanges or GenomicRangesList. Genomic ranges (e.g. genome annotations). Metadata describing the assay rows.
rowData: DataFrame. Metadata describing the assay rows, if genomic ranges are not available. Use rowRanges (GenomicRanges) instead, if possible.
colData: DataFrame. Metadata describing the assay columns. For bulk RNA-seq, this data describes the samples. For single-cell RNA-seq, this data describes the cells.
metadata: list. Metadata.
transgeneNames: character. Vector indicating which assay rows denote transgenes (e.g. EGFP, TDTOMATO).
denylist: logical(1). Apply a denylist check on illegal column names defined in colData. This is useful for catching names that are not considered best practice, and other values that may conflict with Acid Genomics packages. Refer to metadataDenylist for the current list of offending values.
sort: logical(1). Ensure all row and column names are sorted alphabetically. This includes columns inside rowData and colData, and metadata slot names. Assay names are required to contain counts as the first assay.
sessionInfo: logical(1). Slot session information into metadata.

Value

Providing rowRanges: RangedSummarizedExperiment.
Providing rowData: SummarizedExperiment.

Note

Updated 2022-09-20.

Session information

This function improves upon the standard constructor by slotting useful session information into the metadata slot by default:

date: Today's date, returned from Sys.Date.
sessionInfo: sessioninfo::session_info() return. This behavior can be disabled by setting sessionInfo = FALSE.
wd: Working directory, returned from getwd.

Examples

## Rows (genes)
genes <- c(
    sprintf("gene%02d", seq_len(3L)),
    "EGFP" # transgene
)
print(genes)
#> [1] "gene01" "gene02" "gene03" "EGFP"  

## Columns (samples)
samples <- sprintf("sample%02d", seq_len(4L))
print(samples)
#> [1] "sample01" "sample02" "sample03" "sample04"

## Counts (assay)
counts <- matrix(
    data = seq_len(length(genes) * length(samples)),
    nrow = length(genes),
    ncol = length(samples),
    dimnames = list(genes, samples)
)
## Primary assay must be named "counts".
assays <- S4Vectors::SimpleList("counts" = counts)
print(assays)
#> List of length 1
#> names(1): counts

## Row data (genomic ranges)
## Note that we haven't defined the transgene here.
## It will be handled automatically in the function call.
rowRanges <- AcidGenomes::emptyRanges(head(genes, n = length(genes) - 1L))
print(rowRanges)
#> GRanges object with 3 ranges and 0 metadata columns:
#>          seqnames    ranges strand
#>             <Rle> <IRanges>  <Rle>
#>   gene01  unknown     1-100      *
#>   gene02  unknown   101-200      *
#>   gene03  unknown   201-300      *
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

## Column data
colData <- S4Vectors::DataFrame(
    "age" = rep(
        x = c(3L, 6L),
        times = length(samples) / 2L
    ),
    "genotype" = rep(
        x = c("wildtype", "knockout"),
        times = 1L,
        each = length(samples) / 2L
    ),
    row.names = samples
)
print(colData)
#> DataFrame with 4 rows and 2 columns
#>                age    genotype
#>          <integer> <character>
#> sample01         3    wildtype
#> sample02         6    wildtype
#> sample03         3    knockout
#> sample04         6    knockout

## Minimal mode.
x <- makeSummarizedExperiment(assays = assays)
print(x)
#> class: SummarizedExperiment 
#> dim: 4 4 
#> metadata(3): date sessionInfo wd
#> assays(1): counts
#> rownames(4): EGFP gene01 gene02 gene03
#> rowData names(0):
#> colnames(4): sample01 sample02 sample03 sample04
#> colData names(0):

x <- makeSummarizedExperiment(
    assays = assays,
    rowRanges = rowRanges,
    colData = colData,
    transgeneNames = "EGFP"
)
print(x)
#> class: RangedSummarizedExperiment 
#> dim: 4 4 
#> metadata(3): date sessionInfo wd
#> assays(1): counts
#> rownames(4): EGFP gene01 gene02 gene03
#> rowData names(0):
#> colnames(4): sample01 sample02 sample03 sample04
#> colData names(2): age genotype