qc_pca | R Documentation |
Plots a principal component analysis based on peptide or precursor intensities.
qc_pca( data, sample, grouping, intensity, condition, components = c("PC1", "PC2"), digestion = NULL, plot_style = "pca" )
data |
a data frame that contains sample names, peptide or precursor identifiers, corresponding intensities and a condition column indicating e.g. the treatment. |
sample |
a character column in the |
grouping |
a character column in the |
intensity |
a numeric column in the |
condition |
a column in the |
components |
a character vector indicating the two components that should be displayed in the plot. By default these are PC1 and PC2. You can provide these using a character vector of the form c("PC1", "PC2"). |
digestion |
optional, a character column in the |
plot_style |
a character value that specifies what plot should be returned. If
|
A principal component analysis plot showing PC1 and PC2. If plot_style = "scree"
, a
scree plot for all dimensions is returned.
set.seed(123) # Makes example reproducible # Create example data data <- create_synthetic_data( n_proteins = 100, frac_change = 0.05, n_replicates = 3, n_conditions = 2, ) # Plot scree plot qc_pca( data = data, sample = sample, grouping = peptide, intensity = peptide_intensity_missing, condition = condition, plot_style = "scree" ) # Plot principal components qc_pca( data = data, sample = sample, grouping = peptide, intensity = peptide_intensity_missing, condition = condition )
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