Plot the most common missing value patterns

The patterns in missing values can be informative with respect to whether the experiment has worked, or if particular samples are outliers. This function uses an 'upset' plot to show the top 50 most common missing value patterns across the samples

Usage

plot_missing_upset(obj, i)

Arguments

obj

QFeatures. Proteomics dataset

i

string. Index for the SummarisedExperiment you wish to plot

Value

Returns a ggplot object.

Examples

set.seed(11)
library(ggplot2)

df <- diamonds[sample(nrow(diamonds), 1000), ]

tmt_qf <- QFeatures::readQFeatures(assayData = psm_tmt_total,
  quantCols = 36:45,
  name = "psms_raw")
#> Checking arguments.
#> Loading data as a 'SummarizedExperiment' object.
#> Formatting sample annotations (colData).
#> Formatting data as a 'QFeatures' object.