qc_sample_correlation: Correlation based hirachical clustering of samples

In protti: Bottom-Up Proteomics and LiP-MS Quality Control and Data Analysis Tools

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
)

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
)

protti documentation built on Jan. 22, 2023, 1:11 a.m.