deChar_twoGroup

Source: R/deChar_twoGroup.R

This function is for two-group lipid characteristics analysis. It identifies the differentially expressed lipid characteristics based on the user-selected characteristic.

deChar_twoGroup(
  processed_se,
  char,
  ref_group,
  test = c("t-test", "Wilcoxon test"),
  significant = c("pval", "padj"),
  p_cutoff = 0.05,
  FC_cutoff = 1,
  transform = c("none", "log10", "square", "cube", "log2")
)

Arguments

processed_se

A SummarizedExperiment object constructed by as_summarized_experiment and processed by data_process.

char

Character. A lipid characteristic selected from the deChar list returned by list_lipid_char.

ref_group

Character. Group name of the reference group. It must be one of the group names in the group information table's group column.

test

Character. The method to use for comparing means. Allowed method include "t-test" and "Wilcoxon test". Default is 't-test'.

significant

Character. The p-value to be used for the statistically significant. Must be one of "pval" or "padj". Default is 'pval'.

p_cutoff

Numeric. Significant level. Default is 0.05.

FC_cutoff

Numeric. Significance of the fold-change. Default is 1.

transform

Character. Method for data transformation. Allowed methods include "none", "log10", "square", "cube", "log2". Select 'none' to skip data transformation. Default is 'log10'.

Value

Return a SummarizedExperiment object containing analysis results.

Examples

data("de_data_twoGroup")
processed_se <- data_process(
    de_data_twoGroup, exclude_missing=TRUE, exclude_missing_pct=70,
    replace_na_method='min', replace_na_method_ref=0.5,
    normalization='Percentage')
char_list <- list_lipid_char(processed_se)$deChar_list
#> There are 4 ratio characteristics that can be converted in your dataset.
names(char_list) <- NULL
deChar_se <- deChar_twoGroup(processed_se, char=char_list[14], ref_group="ctrl",
    test='t-test', significant="pval", p_cutoff=0.05,
    FC_cutoff=1, transform='log10')
#> There are 4 ratio characteristics that can be converted in your dataset.