filterTrajFeaturesByCOV: Filter features by Coefficient of Variation (COV)
In CellTrails: Reconstruction, visualization and analysis of branching trajectories

Description Usage Arguments Details Value Author(s) See Also Examples

Filters trajectory features by their coefficient of variation.

1	filterTrajFeaturesByCOV(sce, threshold, design = NULL, show_plot = TRUE)

`sce`	An `SingleCellExperiment` object
`threshold`	Minimum coefficient of variation; numeric value between 0 and 1
`design`	A numeric matrix describing the factors that should be blocked
`show_plot`	Indicates if plot should be shown (default: TRUE)

For each trajectory feature x listed in the SingleCellExperiment object the coefficient of variation is computed by CoV(x) = sd(x) / mean(x). Features with a CoV(x) greater than threshold remain labeled as trajectory feature in the SingleCellExperiment object, otherwise they are not considered for dimensionality reduction, clustering and trajectory reconstruction. Please note that spike-in controls are ignored and are not listed as trajectory features.

To account for systematic bias in the expression data (e.g., cell cycle effects), a design matrix can be provided for the learning process. It should list the factors that should be blocked and their values per sample. It is suggested to construct a design matrix with model.matrix.

A character vector

Daniel C. Ellwanger

trajFeatureNames model.matrix

# Simulate 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 incrementally
trajFeatureNames(sce) <- filterTrajFeaturesByDL(sce, threshold=2)
trajFeatureNames(sce) <- filterTrajFeaturesByCOV(sce, threshold=0.5)

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