Two Sample T-test Comparisons.

This function performs pairwise comparisons between two groups for each combination of a categorical predictor (with exactly two levels) and a continuous outcome variable. It first converts any character variables in data to factors and, if specified, applies a log2 transformation to the continuous variables. The function conducts a Shapiro-Wilk normality test to decide whether to use a Two-Sample T-Test vs. a Wilcoxon Rank Sum Test. The resulting p-values are printed and returned.

Usage

cyt_ttest(data, scale = NULL, format_output = FALSE, progress = NULL)

Arguments

data

A matrix or data frame containing continuous and categorical variables.

scale

A character specifying a transformation for continuous variables. Options are NULL (default) and "log2". When scale = "log2", a log2 transformation is applied and a two-sample t-test is used; when scale is NULL, a Mann-Whitney U test is performed.

format_output

Logical. If TRUE, returns the results as a tidy data frame. Default is FALSE.

Value

If format_output is FALSE, returns a list of p-values (named by Outcome and Categorical variable). If TRUE, returns a data frame with columns "Outcome", "Categorical", "Comparison", and "P_value".

Examples

data_df <- ExampleData1[, -c(3)]
data_df <- dplyr::filter(data_df, Group != "ND", Treatment != "Unstimulated")

cyt_ttest(data_df[, c(1, 2, 5:6)], scale = "log2", format_output = TRUE)
#>   Outcome Categorical      Comparison
#> 1   IFN.G       Group   PreT2D vs T2D
#> 2   IL.10       Group   PreT2D vs T2D
#> 3   IFN.G   Treatment CD3/CD28 vs LPS
#> 4   IL.10   Treatment CD3/CD28 vs LPS
#>                                                Test Estimate Statistic P_value
#> 1 Wilcoxon rank sum test with continuity correction   -2.463    1599.0   0.008
#> 2 Wilcoxon rank sum test with continuity correction   -0.956    1625.0   0.012
#> 3 Wilcoxon rank sum test with continuity correction    9.024    4132.5   0.000
#> 4 Wilcoxon rank sum test with continuity correction    1.690    3091.0   0.000