promor (version 0.2.1)

pre_process: Pre-process protein intensity data for modeling

Description

This function pre-processes protein intensity data from the top differentially expressed proteins identified with find_dep for modeling.

Usage

pre_process(
  fit_df,
  norm_df,
  sig = "adjP",
  sig_cutoff = 0.05,
  fc = 1,
  n_top = 20,
  find_highcorr = TRUE,
  corr_cutoff = 0.9,
  save_corrmatrix = FALSE,
  file_path = NULL,
  rem_highcorr = TRUE
)

Value

A model_df object, which is a data frame of protein intensities with proteins indicated by columns.

Arguments

fit_df

A fit_df object from performing find_dep.

norm_df

The norm_df object from which the fit_df object was obtained.

sig

Criteria to denote significance in differential expression. Choices are "adjP" (default) for adjusted p-value or "P" for p-value.

sig_cutoff

Cutoff value for p-values and adjusted p-values in differential expression. Default is 0.05.

fc

Minimum absolute log-fold change to use as threshold for differential expression. Default is 1.

n_top

The number of top hits from find_dep to be used in modeling. Default is 20.

find_highcorr

Logical. If TRUE (default), finds highly correlated proteins.

corr_cutoff

A numeric value specifying the correlation cutoff. Default is 0.90.

save_corrmatrix

Logical. If TRUE, saves a copy of the protein correlation matrix in a tab-delimited text file labeled "Protein_correlation.txt" in the directory specified by file_path.

file_path

A string containing the directory path to save the file.

rem_highcorr

Logical. If TRUE (default), removes highly correlated proteins (predictors or features).

Author

Chathurani Ranathunge

Details

This function creates a data frame that contains protein intensities for a user-specified number of top differentially expressed proteins.

• Note: Most models will benefit from reducing correlation between proteins (predictors or features), therefore we recommend removing those proteins at this stage to reduce pairwise-correlation.

• If no or few proteins meet the significance threshold for differential expression, you may adjust sig, fc, and/or sig_cutoff accordingly to obtain more proteins for modeling.

See Also

• find_dep, normalize_data

• caret: findCorrelation

Examples

## Create a model_df object with default settings.
covid_model_df1 <- pre_process(fit_df = covid_fit_df, norm_df = covid_norm_df)

## Change the correlation cutoff.
covid_model_df2 <- pre_process(covid_fit_df, covid_norm_df, corr_cutoff = 0.95)

## Change the significance criteria to include more proteins
covid_model_df3 <- pre_process(covid_fit_df, covid_norm_df, sig = "P")

## Change the number of top differentially expressed proteins to include
covid_model_df4 <- pre_process(covid_fit_df, covid_norm_df, sig = "P", n_top = 24)