plot_pseudotime_metrics: Plot metrics in pseudotime
In kieranrcampbell/embeddr: Laplacian eigenmaps & principal curves for pseudotemporal ordering of single-cell RNA-seq data
Description
Usage
Arguments
Value
Examples
Description
Plot various metrics (mean, variance, CV2 and signal-to-noise ratio) using
a sliding window approach. Calculates metrics if not already done so.
Usage
1
plot_pseudotime_metrics(sce, ...)
Arguments
sce
An object of class SCESet
...
Additional arguments to be passed to calculate_metrics concerning
how the metrics are calculated.
Value
A ggplot graphic
Examples
1
2
3
4
5
library(scater)
data('sc_example_counts') ; sce <- newSCESet(countData = sc_example_counts)
sce <- embeddr(sce)
sce <- fit_pseudotime(sce)
plot_pseudotime_metrics(sce)
kieranrcampbell/embeddr documentation built on May 20, 2019, 9:24 a.m.
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Description Usage Arguments Value Examples
Description
Plot various metrics (mean, variance, CV2 and signal-to-noise ratio) using a sliding window approach. Calculates metrics if not already done so.
Usage
1 | plot_pseudotime_metrics(sce, ...)
|
Arguments
sce |
An object of class |
... |
Additional arguments to be passed to |
Value
A ggplot graphic
Examples
1 2 3 4 5 | library(scater)
data('sc_example_counts') ; sce <- newSCESet(countData = sc_example_counts)
sce <- embeddr(sce)
sce <- fit_pseudotime(sce)
plot_pseudotime_metrics(sce)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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