Plot row standard deviations versus row means

meanSdPlot generates a hexagonal heatmap of the row standard deviations versus row means from SummarizedExperiment objects. See meanSdPlot.

Usage

meanSdPlot(
  x,
  ranks = TRUE,
  xlab = ifelse(ranks, "rank(mean)", "mean"),
  ylab = "sd",
  pch,
  plot = TRUE,
  bins = 50,
  ...
)

Arguments

x

SummarizedExperiment, Data object.

ranks

Logical, Whether or not to plot the row means on the rank scale.

xlab

Character, x-axis label.

ylab

Character, y-axis label.

pch

Ignored - exists for backward compatibility.

plot

Logical, Whether or not to produce the plot.

bins

Numeric vector, Data object before normalization.

...

Other arguments, Passed to stat_binhex.

Value

A scatter plot of row standard deviations versus row means(generated by stat_binhex)

Examples

# Load example
data(Silicosis_pg)
data <- Silicosis_pg
data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")

# Differential test
ecols <- grep("LFQ.", colnames(data_unique))
se <- make_se_parse(data_unique, ecols,mode = "delim")
filt <- filter_se(se, thr = 0, fraction = 0.4, filter_formula = ~ Reverse != "+" & Potential.contaminant!="+")
#> filter base on missing number is <= 0 in at least one condition.
#> filter base on missing number fraction < 0.4 in each row
#> filter base on giving formula 
norm <- normalize_vsn(filt)
#> vsn2: 8762 x 20 matrix (1 stratum). 
#> Please use 'meanSdPlot' to verify the fit.

# Plot meanSdPlot
meanSdPlot(norm)
#> Warning: Computation failed in `stat_binhex()`
#> Caused by error in `compute_group()`:
#> ! The package "hexbin" is required for `stat_binhex()`