SconeExperiment-class: Class SconeExperiment

In YosefLab/scone: Single Cell Overview of Normalized Expression data

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)

YosefLab/scone documentation built on Feb. 19, 2025, 4:22 p.m.