iplot {sparrow} | R Documentation |
It is informative to look at the individual log fold changes of the genes within a gene set to explore the degree to which they (1) are coherent with respect to each other; and (2) see how the compare to the background distribution of log fold changes of the entire transcriptome.
You can visualize this behavior via a type = "density"
plot, or a
type = "boxplot". It is also common to plot either the individual log fold changes
value = "logFC"or t-statistics
value = "t"'.
iplot( x, name, value = "logFC", type = c("density", "gsea", "boxplot"), tools = c("wheel_zoom", "box_select", "reset", "save"), main = NULL, with.legend = TRUE, collection = NULL, shiny_source = "mggenes", width = NULL, height = NULL, ggtheme = ggplot2::theme_bw(), trim = 0.005, ... )
x |
A |
name |
the name of the geneset to plot |
value |
A string indicating the column name for the value of the
gene-level metadata to plot. Default is |
type |
plot the distributions as a |
tools |
the tools to display in the rbokeh plot |
main |
A title to display. If not specified, the gene set name
will be used, otherwise you can pass in a custom title, or |
with.legend |
Draws a legend to map point color to meaning. There are three levels a point (gene level statistic) can be color as, "notsig", "psig", and "sig". "notsig" implies that the FDR >= 10%, "psig" means that FDR <= 10%, but the logFC is "unremarkable" (< 1), and "sig" means that both the FDR <= 10% and the logFC >= 1 |
collection |
If you have genesets with duplicate names in |
shiny_source |
the name of this element that is used in shiny callbacks.
Defaults to |
width, height |
the width and height of the output plotly plot |
ggtheme |
a ggplot theme, like the thing returned from
|
trim |
used to define the upper and lower quantiles to max out the individual gene statistics in the selected geneset. |
... |
pass through parameters to internal boxplot/density/gsea plotting functions |
the ploty plot object
mgr <- exampleSparrowResult() iplot(mgr, "BURTON_ADIPOGENESIS_PEAK_AT_2HR", value = c("t-statistic" = "t"), type = "density") iplot(mgr, "BURTON_ADIPOGENESIS_PEAK_AT_2HR", value = c("log2FC" = "logFC"), type = "boxplot") iplot(mgr, "BURTON_ADIPOGENESIS_PEAK_AT_2HR", value = c("-statistic" = "t"), type = "gsea")