pd_row_t_test: Row-wise tests of difference using the probabilistic dropout...
In proDA: Differential Abundance Analysis of Label-Free Mass Spectrometry Data

Description Usage Arguments Details Value See Also Examples

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

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
)

`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.

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.

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().

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

  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)