A correlation heatmap is created that uses hirachical clustering to determine sample similarity.

qc_sample_correlation(
  data,
  sample,
  grouping,
  intensity_log2,
  condition,
  digestion = NULL,
  run_order = NULL,
  method = "spearman",
  interactive = FALSE
)

Arguments

data

a data frame that contains at least the input variables.

sample

a character column in the data data frame that contains the sample names.

grouping

a character column in the data data frame that contains precursor or peptide identifiers.

intensity_log2

a numeric column in the data data frame that contains log2 intensity values.

condition

a character or numeric column in the data data frame that contains the conditions.

digestion

optional, a character column in the data data frame that contains information about the digestion method used. e.g. "LiP" or "tryptic control".

run_order

optional, a character or numeric column in the data data frame that contains the order in which samples were measured. Useful to investigate batch effects due to run order.

method

a character value that specifies the method to be used for correlation. "spearman" is the default but can be changed to "pearson" or "kendall".

interactive

a logical value that specifies whether the plot should be interactive. Determines if an interactive or static heatmap should be created using heatmaply or pheatmap, respectively.

Value

A correlation heatmap that compares each sample. The dendrogram is sorted by optimal leaf ordering.

Examples

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, method = "effect_random" ) # Create sample correlation heatmap qc_sample_correlation( data = data, sample = sample, grouping = peptide, intensity_log2 = peptide_intensity_missing, condition = condition )