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protti

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
)

Arguments

data

a dataframe contains at least the input variables.
sample

the name of the column containing the sample names.
grouping

the name of the column containing precursor or peptide identifiers.
intensity_log2

the name of the column containing log2 intensity values.
condition

the name of the column containing the conditions.
digestion

optional, the name of the column containing information about the digestion method used. Eg. "LiP" or "tryptic control".
run_order

optional, the name of the column containing the order in which samples were measured. Useful to investigate batch effects due to run order.
method

the method to be used for correlation. "spearman" is the default but can be changed to "pearson" or "kendall".
interactive

logical, default is FALSE. 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

## Not run: 
qc_sample_correlation(
  data,
  sample = r_file_name,
  grouping = eg_precursor_id,
  intensity_log2 = intensity_log2,
  condition = r_condition
)

## End(Not run)
protti

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

v0.1.1
MIT + file LICENSE
Authors
Jan-Philipp Quast [aut, cre], Dina Schuster [aut], ETH Zurich [cph, fnd]
Initial release

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