an SCESet object containing expression values and
experimental information. Must have been appropriately prepared. For the
plotExpressionDefault method, the object argument is a
data.frame in 'long' format providing expression values for a set of features
to plot, plus metadata used in the aesth argument, but this is not
meant to be a user-level operation.
...
optional arguments (from those listed above) passed to
plotExpressionSCESet or plotExpressionDefault
features
a character vector of feature names or Boolean
vector or numeric vector of indices indicating which features should have
their expression values plotted
x
character string providing a column name of pData(object) or
a feature name (i.e. gene or transcript) to plot on the x-axis in the
expression plot(s). If a feature name, then expression values for the feature
will be plotted on the x-axis for each subplot.
exprs_values
character string indicating which values should be used
as the expression values for this plot. Valid arguments are "tpm"
(default; transcripts per million), "norm_tpm" (normalised TPM
values), "fpkm" (FPKM values), "norm_fpkm" (normalised FPKM
values), "counts" (counts for each feature), "norm_counts",
"cpm" (counts-per-million), "norm_cpm" (normalised
counts-per-million), "exprs" (whatever is in the 'exprs' slot
of the SCESet object; default), "norm_exprs" (normalised
expression values) or "stand_exprs" (standardised expression values)
or any other slots that have been added to the "assayData" slot by
the user.
colour_by
optional character string supplying name of a column of
pData(object) which will be used as a variable by which to colour
expression values on the plot.
shape_by
optional character string supplying name of a column of
pData(object) which will be used as a variable to define the shape of
points for expression values on the plot.
size_by
optional character string supplying name of a column of
pData(object) which will be used as a variable to define the size of
points for expression values on the plot.
ncol
number of columns to be used for the panels of the plot
xlab
label for x-axis; if NULL (default), then x will be
used as the x-axis label
show_median
logical, show the median for each group on the plot
show_violin
logical, show a violin plot for the distribution
for each group on the plot
show_smooth
show a smoothed fit through the data points?
theme_size
numeric scalar giving default font size for plotting theme
(default is 10)
log2_values
should the expression values be transformed to the
log2-scale for plotting (with an offset of 1 to avoid logging zeroes)?
aesth
an aes object to use in the call to ggplot.
ylab
character string defining a label for the y-axis (y-axes) of the
plot.
Details
Plot expression values (default log2(transcripts-per-million +
1), if available) for a set of features.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(scater)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: ggplot2
Attaching package: 'scater'
The following object is masked from 'package:stats':
filter
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/scater/plotExpression.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotExpression
> ### Title: Plot expression values for a set of features (e.g. genes or
> ### transcripts)
> ### Aliases: plotExpression plotExpression,SCESet-method
> ### plotExpression,data.frame-method plotExpressionDefault
> ### plotExpressionSCESet
>
> ### ** Examples
>
> ## prepare data
> data("sc_example_counts")
> data("sc_example_cell_info")
> pd <- new("AnnotatedDataFrame", data = sc_example_cell_info)
> example_sceset <- newSCESet(countData = sc_example_counts, phenoData = pd)
> example_sceset <- calculateQCMetrics(example_sceset)
>
> ## default plot
> plotExpression(example_sceset, 1:6, "Mutation_Status")
>
> ## explore options
> plotExpression(example_sceset, 1:6, x="Mutation_Status", exprs_values="exprs",
+ colour_by="Cell_Cycle", show_violin=TRUE, show_median=TRUE)
> plotExpression(example_sceset, 1:6, x="Mutation_Status", exprs_values="counts",
+ colour_by="Cell_Cycle", show_violin=TRUE, show_median=TRUE)
>
> ## plot expression against expression values for Gene_0004
> plotExpression(example_sceset, 1:4, "Gene_0004")
> plotExpression(example_sceset, 1:4, "Gene_0004", show_smooth = TRUE)
Warning messages:
1: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
pseudoinverse used at -0.056653
2: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
neighborhood radius 2.8396
3: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
reciprocal condition number 0
4: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
There are other near singularities as well. 7.7447
5: In predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata)) as.matrix(model.frame(delete.response(terms(object)), :
pseudoinverse used at -0.056653
6: In predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata)) as.matrix(model.frame(delete.response(terms(object)), :
neighborhood radius 2.8396
7: In predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata)) as.matrix(model.frame(delete.response(terms(object)), :
reciprocal condition number 0
8: In predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata)) as.matrix(model.frame(delete.response(terms(object)), :
There are other near singularities as well. 7.7447
>
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.
