filterTrajFeaturesByDL: Filter trajectory features by Detection Level (DL)

In dcellwanger/CellTrails: Reconstruction, visualization and analysis of branching trajectories

Description

Filters trajectory features that are detected in a minimum number of samples.

Usage

filterTrajFeaturesByDL(sce, threshold, show_plot = TRUE)

Arguments

sce	An SingleCellExperiment object
threshold	Minimum number of samples; if value < 1 it is interpreted as fraction, otherwise as absolute sample count
show_plot	Indicates if plot should be shown (default: TRUE)

Details

The detection level denotes the fraction of samples in which a feature was detected. For each trajectory feature listed in the CellTrailsSet object the relative number of samples having a feature expression value greater than 0 is counted. Features that are expressed in a fraction of all samples greater than threshold remain labeled as trajectory feature as listed in the SingleCellExperiment object, otherwise they may be not considered for dimensionality reduction, clustering, and trajectory reconstruction. If the parameter threshold fullfills threshold >= 1 it becomes converted to a relative fraction of the total sample count. Please note that spike-in controls are ignored and are not listed as trajectory features.

Value

A character vector

Author(s)

Daniel C. Ellwanger

See Also

trajFeatureNames

Examples

# Example data
set.seed(1101)
dat <- simulate_exprs(n_features=15000, n_samples=100)

# Create container
alist <- list(logcounts=dat)
sce <- SingleCellExperiment(assays=alist)

# Filter features
tfeat <- filterTrajFeaturesByDL(sce, threshold=2)
head(tfeat)

# Set trajectory features to object
trajFeatureNames(sce) <- tfeat

# Number of features
length(trajFeatureNames(sce)) #filtered
nrow(sce) #total

dcellwanger/CellTrails documentation built on May 12, 2020, 2:01 a.m.