SconeExperiment-class {scone} | R Documentation

## Class SconeExperiment

### Description

Objects of this class store, at minimum, a gene expression matrix and a set of covariates (sample metadata) useful for running `scone`. These include, the quality control (QC) metrics, batch information, and biological classes of interest (if available).

The typical way of creating `SconeExperiment` objects is via a call to the `SconeExperiment` function or to the `scone` function. If the object is a result to a `scone` call, it will contain the results, e.g., the performance metrics, scores, and normalization workflow comparisons. (See Slots for a full list).

This object extends the `SummarizedExperiment` class.

The constructor `SconeExperiment` creates an object of the class `SconeExperiment`.

### Usage

    SconeExperiment(object, ...)

    ## S4 method for signature 'SummarizedExperiment'
    SconeExperiment(
      object,
      which_qc = integer(),
      which_bio = integer(),
      which_batch = integer(),
      which_negconruv = integer(),
      which_negconeval = integer(),
      which_poscon = integer(),
      is_log = FALSE
    )

    ## S4 method for signature 'matrix'
    SconeExperiment(
      object,
      qc,
      bio,
      batch,
      negcon_ruv = NULL,
      negcon_eval = negcon_ruv,
      poscon = NULL,
      is_log = FALSE
    )

### Arguments

`object` | Either a matrix or a `SummarizedExperiment` containing the raw gene expression.
`...` | see specific S4 methods for additional arguments.
`which_qc` | index that specifies which columns of 'colData' correspond to QC measures.
`which_bio` | index that specifies which column of 'colData' corresponds to 'bio'.
`which_batch` | index that specifies which column of 'colData' corresponds to 'batch'.
`which_negconruv` | index that specifies which column of 'rowData' has information on negative controls for RUV.
`which_negconeval` | index that specifies which column of 'rowData' has information on negative controls for evaluation.
`which_poscon` | index that specifies which column of 'rowData' has information on positive controls.
`is_log` | are the expression data in log scale?
`qc` | numeric matrix with the QC measures.
`bio` | factor with the biological class of interest.
`batch` | factor with the batch information.
`negcon_ruv` | a logical vector indicating which genes to use as negative controls for RUV.
`negcon_eval` | a logical vector indicating which genes to use as negative controls for evaluation.
`poscon` | a logical vector indicating which genes to use as positive controls.

### Details

The QC matrix, biological class, and batch information are stored as elements of the 'colData' of the object.

The positive and negative control genes are stored as elements of the 'rowData' of the object.

### Value

A `SconeExperiment` object.

### Slots

`which_qc`

  integer. Index of columns of 'colData' that contain the QC metrics.

`which_bio`

  integer. Index of the column of 'colData' that contains the biological classes information (it must be a factor).

`which_batch`

  integer. Index of the column of 'colData' that contains the batch information (it must be a factor).

`which_negconruv`

  integer. Index of the column of 'rowData' that contains a logical vector indicating which genes to use as negative controls to infer the factors of unwanted variation in RUV.

`which_negconeval`

  integer. Index of the column of 'rowData' that contains a logical vector indicating which genes to use as negative controls to evaluate the performance of the normalizations.

`which_poscon`

  integer. Index of the column of 'rowData' that contains a logical vector indicating which genes to use as positive controls to evaluate the performance of the normalizations.

`hdf5_pointer`

  character. A string specifying to which file to write / read the normalized data.

`imputation_fn`

  list of functions used by scone for the imputation step.

`scaling_fn`

  list of functions used by scone for the scaling step.

`scone_metrics`

  matrix. Matrix containing the "raw" performance metrics. See `scone` for a description of each metric.

`scone_scores`

  matrix. Matrix containing the performance scores (transformed metrics). See `scone` for a discussion on the difference between scores and metrics.

`scone_params`

  data.frame. A data frame containing the normalization schemes applied to the data and compared.

`scone_run`

  character. Whether `scone` was run and in which mode ("no", "in_memory", "hdf5").

`is_log`

  logical. Are the expression data in log scale?

`nested`

  logical. Is batch nested within bio? (Automatically set by `scone`).

`rezero`

  logical. TRUE if `scone` was run with `zero="preadjust"` or `zero="strong"`.

`fixzero`

  logical. TRUE if `scone` was run with `zero="postadjust"` or `zero="strong"`.

`impute_args`

  list. Arguments passed to all imputation functions.

### See Also

`get_normalized`, `get_params`, `get_batch`, `get_bio`, `get_design`, `get_negconeval`, `get_negconruv`, `get_poscon`, `get_qc`, `get_scores`, and `get_score_ranks` to access internal fields, `select_methods` for subsetting by method, and `scone` for running scone workflows.

### Examples

    set.seed(42)
    nrows <- 200
    ncols <- 6
    counts <- matrix(rpois(nrows * ncols, lambda=10), nrows)
    rowdata <- data.frame(poscon=c(rep(TRUE, 10), rep(FALSE, nrows-10)))
    coldata <- data.frame(bio=gl(2, 3))
    se <- SummarizedExperiment(assays=SimpleList(counts=counts),
                              rowData=rowdata, colData=coldata)

    scone1 <- SconeExperiment(assay(se), bio=coldata$bio, poscon=rowdata$poscon)

    scone2 <- SconeExperiment(se, which_bio=1L, which_poscon=1L)

[Package scone version 1.18.0 Index]