protti (version 0.9.0)

qc_sample_correlation: Correlation based hirachical clustering of samples

Description

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

Usage

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

Value

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

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.

Examples

# \donttest{
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
)
# }