Extraction and replacement methods for objects of class dbFrame

Description

Methods for replacing and accessing slots in a dbFrame.

Usage

bc_key(x)

bc_ids(x)

deltas(x)

normed_bcs(x)

mhl_dists(x)

sep_cutoffs(x)

mhl_cutoff(x)

counts(x)

yields(x)

## S4 method for signature 'dbFrame'
exprs(object)

## S4 method for signature 'dbFrame'
bc_key(x)

## S4 method for signature 'dbFrame'
bc_ids(x)

## S4 method for signature 'dbFrame'
deltas(x)

## S4 method for signature 'dbFrame'
normed_bcs(x)

## S4 method for signature 'dbFrame'
mhl_dists(x)

## S4 method for signature 'dbFrame'
sep_cutoffs(x)

## S4 method for signature 'dbFrame'
mhl_cutoff(x)

## S4 method for signature 'dbFrame'
counts(x)

## S4 method for signature 'dbFrame'
yields(x)

## S4 replacement method for signature 'dbFrame,numeric'
mhl_cutoff(x) <- value

## S4 replacement method for signature 'dbFrame,ANY'
mhl_cutoff(x) <- value

## S4 replacement method for signature 'dbFrame,numeric'
sep_cutoffs(x) <- value

## S4 replacement method for signature 'dbFrame,ANY'
sep_cutoffs(x) <- value

Arguments

Value

exprs

extracts the raw data intensities.

bc_key

extracts the barcoding scheme.

bc_ids

extracts currently made event assignments.

deltas

extracts barcode separations computed from normalized intensities. sep_cutoffs apply to these values (see applyCutoffs).

normed_bcs

extracts normalized barcode intensities (see assignPrelim).

sep_cutoffs, sep_cutoffs<-

extracts or replaces separation cutoffs. If option sep_cutoffs is not specified, these will be used by applyCutoffs. Replacement value must be a non- negative numeric with length one or same length as the number of barcodes.

mhl_cutoff, mhl_cutoff<-

extracts or replaces the Mahalanobis distance threshold above which events are to be unassigned. Replacement value must be a single non-negative and non-zero numeric.

counts

extract the counts matrix (see dbFrame).

yields

extract the yields matrix (see dbFrame).

Author(s)

Helena Lucia Crowell helena.crowell@uzh.ch

Examples

data(sample_ff, sample_key)
re <- assignPrelim(x = sample_ff, y = sample_key)

# set global cutoff parameter
sep_cutoffs(re) <- 0.4
re <- applyCutoffs(x = re)

# subset a specific population, e.g. A1: 111000
a1 <- bc_ids(re) == "A1"
head(exprs(sample_ff[a1, ]))

# subset unassigned events
unassigned <- bc_ids(re) == 0
head(exprs(sample_ff[unassigned, ]))