plotPCAVariance: plotPCAVariance
In IMB-Computational-Genomics-Lab/ascend: ascend - Analysis of Single Cell Expression, Normalisation, and Differential expression
Description
Usage
Arguments
Value
Examples
Description
Generates a scree plot of PCs generated by
runPCA.
The principal components (PCs) are sorted from largest percent variance to
the smallest percent variance. This plot is for determining the optimal
number of PCs to retain for further analysis.
Usage
1
plotPCAVariance(object, n = 50)
Arguments
object
An EMSet that has undergone PCA.
n
Number of PCs to view on the plot.
Value
A ggplot2 glob that contains a scatter qplot.
Examples
1
2
3
4
5
# Load EMSet
em_set <- ascend::analyzed_set
# Plot PCA variance
pca_variance_plot <- plotPCAVariance(em_set, n = 10)
IMB-Computational-Genomics-Lab/ascend documentation built on Aug. 29, 2019, 4:10 a.m.
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Description Usage Arguments Value Examples
Description
Generates a scree plot of PCs generated by
runPCA.
The principal components (PCs) are sorted from largest percent variance to
the smallest percent variance. This plot is for determining the optimal
number of PCs to retain for further analysis.
Usage
1 | plotPCAVariance(object, n = 50)
|
Arguments
object |
An |
n |
Number of PCs to view on the plot. |
Value
A ggplot2 glob that contains a scatter qplot.
Examples
1 2 3 4 5 | # Load EMSet
em_set <- ascend::analyzed_set
# Plot PCA variance
pca_variance_plot <- plotPCAVariance(em_set, n = 10)
|
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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