Package 'sinaplot'

Expression data from 2095 AML/ALL and healthy bone marrow cells.

Description

Expression data from 2095 AML/ALL and healthy bone marrow cells.

Usage

data(blood)

Format

A data frame with 2095 rows and 2 columns (Class (AML/ALL subtype), Gene expression values).

Source

http://servers.binf.ku.dk/bloodspot/

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13159

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15434

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61804

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14468

http://cancergenome.nih.gov/

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sinaplot

Description

The SinaPlot is a data visualization chart suitable for plotting any single variable in a multiclass dataset. It is an enhanced jitter strip chart, where the width of the jitter is controlled by the density distribution of the data within each class.

Usage

sinaplot(x, ...)

## Default S3 method:
sinaplot(x, groups = NULL, method = c("density",
  "counts"), scale = TRUE, adjust = 0.75, bins = 50, bin_limit = 1,
  maxwidth = 1, seed = NULL, plot = TRUE, add = FALSE, log = FALSE,
  labels = NULL, xlab = "", ylab = "", col = NULL, pch = NULL, ...)

## S3 method for class 'formula'
sinaplot(formula, data = NULL, ..., subset,
  na.action = NULL, xlab, ylab)

Arguments

x	numeric vector or a data frame or a list of numeric vectors to be plotted.
...	arguments to be passed to plot.
groups	optional vector of length(x).
method	choose the method to spread the samples within the same bin along the x-axis. Available methods: "density" and "counts". See Details.
scale	a logical that indicates whether the width of each group should be scaled relative to the group with the highest density. Default: TRUE.
adjust	adjusts the bandwidth of the density kernel when method == "density" (see density).
bins	number of bins to divide the y-axis into when method == "counts". Default: 50.
bin_limit	if the samples within the same y-axis bin are more than bin_limit, the samples's X coordinates will be adjusted.
maxwidth	control the maximum width the points can spread into. Values between 0 and 1.
seed	a single value that controls the random sample jittering. Set to an integer to enable plot reproducibility. Default NULL.
plot	logical. When TRUE the sinaplot is produced, otherwise the function returns the new sample coordinates. Default: TRUE.
add	logical. If true add boxplot to current plot.
log	logical. If true it uses a logarihmic scale on the y-axis.
labels	labels for each group. Recycled if necessary. By default, these are inferred from the data.
xlab, ylab	axis labels.
pch, col	plotting characters and colors, specified by group. Recycled if necessary.
formula	a formula, such as y ~ grp, where y is a numeric vector of data values to be split into groups according to the grouping variable grp (usually a factor).
data	a data.frame (or list) from which the variables in formula should be taken.
subset	an optional vector specifying a subset of observations to be used for plotting.
na.action	a function which indicates what should happen when the data contain NAs. The default is to ignore missing values in either the response or the group.

Details

There are two available ways to define the x-axis borders for the samples to spread within:

• method = "density"

  A density kernel is estimated along the y-axis for every sample group. The borders are then defined by the density curve. Tuning parameter adjust can be used to control the density bandwidth in the same way it is used in density.

• method = "counts":

  The borders are defined by the number of samples that occupy the same bin and the parameter maxwidth in the following fashion:

  xBorder = nsamples * maxwidth

Value

x	discrete x-coordinates, split by group
y	input values
group	input groups
scaled	final x-coordinates, adjusted by sinaplot

NULL

NULL

Examples

## sinaplot on a formula:

data("blood", package = "sinaplot")
boxplot(Gene ~ Class, data = blood)
sinaplot(Gene ~ Class, data = blood, pch = 20, add = TRUE)

## sinaplot on a data.frame:

df <- data.frame(Uni05 = (1:100)/21, Norm = rnorm(100),
                  `5T` = rt(100, df = 5), Gam2 = rgamma(100, shape = 2))
boxplot(df)
sinaplot(df, add = TRUE, pch = 20)

## sinaplot on a list:

bimodal <- c(rnorm(300, -2, 0.6), rnorm(300, 2, 0.6))
uniform <- runif(500, -4, 4)
normal <- rnorm(800,0,3)

distributions <- list(uniform = uniform, bimodal = bimodal, normal = normal)
boxplot(distributions, col = 2:4)
sinaplot(distributions, add = TRUE, pch = 20)

## sinaplot on a vector:

x <- c(rnorm(200, 4, 1), rnorm(200, 5, 2), rnorm(400, 6, 1.5))
groups <- c(rep("Cond1", 200), rep("Cond2", 200), rep("Cond3", 400))

sinaplot(x, groups)

par(mfrow = c(2, 2))

sinaplot(x, groups, pch = 20, col = 2:4)
sinaplot(x, groups, scale = FALSE, pch = 20, col = 2:4)
sinaplot(x, groups, scale = FALSE, adjust = 1/6, pch = 20, col = 2:4)
sinaplot(x, groups, scale = FALSE, adjust = 3, pch = 20, col = 2:4)

#blood

par(mfrow = c(1,1))
sinaplot(blood$Gene, blood$Class)

old.mar <- par()$mar
par(mar = c(9,4,4,2) + 0.1)
groups <- levels(blood$Class)

sinaplot(blood$Gene, blood$Class, pch = 20, xaxt = "n", col = rainbow(18))
axis(1, at = 1:length(groups), labels = FALSE)
text(1:length(groups), y = par()$usr[3] - 0.1 * (par()$usr[4] - par()$usr[3]),
     xpd = TRUE, srt = 45, adj = 1, labels = groups)
par(mar = old.mar)

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