classify,Asset-method {Allspice}R Documentation

Data classification

Description

Assigns category labels to new data.

Usage

classify(obj, dat, covariates)

Arguments

obj

An object of the class Asset.

dat

A matrix that containts variables as rows and samples as columns.

covariates

A data.frame or matrix that containts samples as rows and covariates as columns.

Details

The input data must be normalized and standardized the same way as the training data for the asset. See normalize() and standardize() for details.

Value

Returns a data frame that contains predicted category labels and performance indicators. The column 'CATEG' contains the final predictions, including "Unclassified" or "Ambiguous" for samples that could not be reliably classified. The columns 'MATCH.1st' and 'MATCH.2nd' contain the first and second best matching categories, respectively.

The column 'BIOMRK' contains a standardized biomarker score that indicates how similar the sample is with respect to the best-matching category. The column 'PROX' tells the likelihood of how likely it is that the observed biomarker score would have been generated by a training sample from the best-matching category (balanced group sizes). The column 'EXCL' tells the likelihood that the sample does not share characteristic features with any other category.

The returned data frame also has the attribute "biomarkers" that contains biomarker scores for all categories.

Examples

# Import ALL subtyping asset.
base <- system.file(package = "Allspice")
folder <- file.path(base, "subtypes")
a <- asset(folder)

# Simulated data.
simu <- bcellALL(5)

# Standardize RNA read counts.
expres <- normalize(a, dat = simu$counts)
expres <- standardize(a, dat = expres)

# Predict categories.
res <- classify(a, dat = expres, covariates = simu$metadata)
print(res[,c("LABEL","PROX","EXCL")])

[Package Allspice version 1.0.5 Index]