pd_row_t_test: Row-wise tests of difference using the probabilistic dropout...

In const-ae/proDA: Differential Abundance Analysis of Label-Free Mass Spectrometry Data

Row-wise tests of difference using the probabilistic dropout model

Description

This is a helper function that combines the call of proDA() and test_diff(). If you need more flexibility use those functions.

Usage

pd_row_t_test(
  X,
  Y,
  moderate_location = TRUE,
  moderate_variance = TRUE,
  alternative = c("two.sided", "greater", "less"),
  pval_adjust_method = "BH",
  location_prior_df = 3,
  max_iter = 20,
  epsilon = 0.001,
  return_fit = FALSE,
  verbose = FALSE
)

pd_row_f_test(
  X,
  ...,
  groups = NULL,
  moderate_location = TRUE,
  moderate_variance = TRUE,
  pval_adjust_method = "BH",
  location_prior_df = 3,
  max_iter = 20,
  epsilon = 0.001,
  return_fit = FALSE,
  verbose = FALSE
)

Arguments

X, Y, ...	the matrices for condition 1, 2 and so on. They must have the same number of rows.
moderate_location	boolean values to indicate if the location and the variances are moderated. Default: TRUE
moderate_variance	boolean values to indicate if the location and the variances are moderated. Default: TRUE
alternative	a string that decides how the hypothesis test is done. This parameter is only relevant for the Wald-test specified using the 'contrast' argument. Default: "two.sided"
pval_adjust_method	a string the indicates the method that is used to adjust the p-value for the multiple testing. It must match the options in p.adjust. Default: "BH"
location_prior_df	the number of degrees of freedom used for the location prior. A large number (> 30) means that the prior is approximately Normal. Default: 3
max_iter	the maximum of iterations proDA() tries to converge to the hyper-parameter estimates. Default: 20
epsilon	if the remaining error is smaller than epsilon the model has converged. Default: 1e-3
return_fit	boolean that signals that in addition to the data.frame with the hypothesis test results, the fit from proDA() is returned. Default: FALSE
verbose	boolean that signals if the method prints messages during the fitting. Default: FALSE
groups	a factor or character vector with that assignes the columns of X to different conditions. This parameter is only applicable for the F-test and must be specified if only a single matrix is provided.

Details

The pd_row_t_test is not actually doing a t-test, but rather a Wald test. But, as the two are closely related and term t-test is more widely understood, we choose to use that name.

Value

If return_fit == FALSE a data.frame is returned with the content that is described in test_diff.

If return_fit == TRUE a list is returned with two elements: fit with a reference to the object returned from proDA() and a test_result() with the data.frame returned from test_diff().

See Also

proDA and test_diff for more flexible versions. The function was inspired by the rowFtests function in the genefilter package.

Examples

  data1 <- matrix(rnorm(10 * 3), nrow=10)
  data2 <- matrix(rnorm(10 * 4), nrow=10)
  data3 <- matrix(rnorm(10 * 2), nrow=10)

  # Comparing two datasets
  pd_row_t_test(data1, data2)

  # Comparing multiple datasets
  pd_row_f_test(data1, data2, data3)

  # Alternative
  data_comb <- cbind(data1, data2, data3)
  pd_row_f_test(data_comb,
     groups = c(rep("A",3), rep("B", 4), rep("C", 2)))

  # t.test, lm, pd_row_t_test, and pd_row_f_test are
  # approximately equivalent on fully observed data
  set.seed(1)
  x <- rnorm(5)
  y <- rnorm(5, mean=0.3)

  t.test(x, y)
  summary(lm(c(x, y) ~ cond,
             data = data.frame(cond = c(rep("x", 5),
                                        rep("y", 5)))))$coefficients[2,]
  pd_row_t_test(matrix(x, nrow=1), matrix(y, nrow=1),
                moderate_location = FALSE,
                moderate_variance = FALSE)
  pd_row_f_test(matrix(x, nrow=1), matrix(y, nrow=1),
                moderate_location = FALSE,
                moderate_variance = FALSE)

const-ae/proDA documentation built on Oct. 31, 2023, 9:39 p.m.