View source: R/plot_hexbin_feature_plus.R
plot_hexbin_feature_plus | R Documentation |
Plot of gene expression and meta data of single cell data in bivariate hexagon cells.
plot_hexbin_feature_plus(
sce,
col,
mod = "RNA",
type,
feature,
action,
colors = NULL,
title = NULL,
xlab = NULL,
ylab = NULL,
expand_hull = 3,
...
)
sce |
A |
col |
A string referring to the name of one column in the meta data of sce by which to colour the hexagons. |
mod |
A string referring to the name of the modality used for plotting.
For RNA modality use "RNA". For other modalities use name of alternative
object for the |
type |
A string referring to the type of assay in the
|
feature |
A string referring to the name of one feature. |
action |
A string specifying how gene expression of observations in
binned hexagon cells are to be summarized. Possible actions are
|
colors |
A vector of strings specifying which colors to use for plotting the different levels in the selected column of the meta data. |
title |
A string containing the title of the plot. |
xlab |
A string containing the title of the x axis. |
ylab |
A string containing the title of the y axis. |
expand_hull |
A numeric value determining the expansion of the line marking different clusters. |
... |
Additional arguments passed on to
|
This function plots any gene expresssion in the hexagon cell
representation calculated with make_hexbin
as well as at the
same time representing outlines of clusters. The chosen gene
expression is summarized by one of four actions prop_0
,
mode
, mean
and median
:
prop_0
Returns the proportion of observations in the bin greater than 0. The associated meta data column needs to be numeric.
mode
Returns the mode of the observations in the bin. The associated meta data column needs to be numeric.
mean
Returns the mean of the observations in the bin. The associated meta data column needs to be numeric.
median
Returns the median of the observations in the bin. The associated meta data column needs to be numeric.
A ggplot2{ggplot}
object.
# For SingleCellExperiment object
library(TENxPBMCData)
library(scater)
tenx_pbmc3k <- TENxPBMCData(dataset = "pbmc3k")
rm_ind <- calculateAverage(tenx_pbmc3k) < 0.1
tenx_pbmc3k <- tenx_pbmc3k[!rm_ind, ]
tenx_pbmc3k <- logNormCounts(tenx_pbmc3k)
tenx_pbmc3k <- runPCA(tenx_pbmc3k)
tenx_pbmc3k <- make_hexbin(tenx_pbmc3k, 10, dimension_reduction = "PCA")
tenx_pbmc3k$random <- factor(sample(1:3, ncol(tenx_pbmc3k), replace = TRUE))
plot_hexbin_feature_plus(tenx_pbmc3k,
col = "random", type = "counts",
feature = "ENSG00000135250", action = "mean"
)
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