test_de {glmGamPoi} | R Documentation

## Test for Differential Expression

### Description

Conduct a quasi-likelihood ratio test for a Gamma-Poisson fit.

### Usage

    test_de(
      fit,
      contrast,
      reduced_design = NULL,
      full_design = fit$model_matrix,
      subset_to = NULL,
      pseudobulk_by = NULL,
      pval_adjust_method = "BH",
      sort_by = NULL,
      decreasing = FALSE,
      n_max = Inf,
      verbose = FALSE
    )

### Arguments

`fit` | object of class `glmGamPoi`. Usually the result of calling `glm_gp(data, ...)`
`contrast` | The contrast to test. Can be a single column name (quoted or as a string) that is removed from the full model matrix of `fit`. Or a complex contrast comparing two or more columns: e.g. `A - B`, `"A - 3 * B"`, `(A + B) / 2 - C` etc. Only one of `contrast` or `reduced_design` must be specified.
`reduced_design` | a specification of the reduced design used as a comparison to see what how much better `fit` describes the data. Analogous to the `design` parameter in `glm_gp()`, it can be either a `formula`, a `model.matrix()`, or a `vector`. Only one of `contrast` or `reduced_design` must be specified.
`full_design` | option to specify an alternative `full_design` that can differ from `fit$model_matrix`. Can be a `formula` or a `matrix`. Default: `fit$model_matrix`
`subset_to` | a vector with the same length as `ncol(fit$data)` or an expression that evaluates to such a vector. The expression can reference columns from `colData(fit$data)`. A typical use case in single cell analysis would be to subset to a specific cell type (e.g. `subset_to = cell_type == "T-cells"`). Note that if this argument is set a new the model for the `full_design` is re-fit. Default: `NULL` which means that the data is not subset.
`pseudobulk_by` | a vector with the same length as `ncol(fit$data)` that is used to split the columns into different groups (calls `split()`). `pseudobulk_by` can also be an expression that evaluates to a vector. The expression can reference columns from `colData(fit$data)`. The counts are summed across the groups to create "pseudobulk" samples. This is typically used in single cell analysis if the cells come from different samples to get a proper estimate of the differences. This is particularly powerful in combination with the `subset_to` parameter to analyze differences between samples for subgroups of cells. Note that this does a fresh fit for both the full and the reduced design. Default: `NULL` which means that the data is not aggregated.
`pval_adjust_method` | one of the p-value adjustment method from p.adjust.methods. Default: `"BH"`.
`sort_by` | the name of the column or an expression used to sort the result. If `sort_by` is `NULL` the table is not sorted. Default: `NULL`
`decreasing` | boolean to decide if the result is sorted increasing or decreasing order. Default: `FALSE`.
`n_max` | the maximum number of rows to return. Default: `Inf` which means that all rows are returned
`verbose` | a boolean that indicates if information about the individual steps are printed while fitting the GLM. Default: `FALSE`.

### Value

a `data.frame` with the following columns

name

  the rownames of the input data

pval

  the p-values of the quasi-likelihood ratio test

adj_pval

  the adjusted p-values returned from `p.adjust()`

f_statistic

  the F-statistic: F = (Dev_full - Dev_red) / (df_1 * disp_ql-shrunken)

df1

  the degrees of freedom of the test: `ncol(design) - ncol(reduced_design)`

df2

  the degrees of freedom of the fit: `ncol(data) - ncol(design) + df_0`

lfc

  the log2-fold change. If the alternative model is specified by `reduced_design` will be `NA`.

### References

- Lund, S. P., Nettleton, D., McCarthy, D. J., & Smyth, G. K. (2012). Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Statistical Applications in Genetics and Molecular Biology, 11(5). https://doi.org/10.1515/1544-6115.1826.

### See Also

`glm_gp()`

### Examples

      Y <- matrix(rnbinom(n = 30 * 100, mu = 4, size = 0.3), nrow = 30, ncol = 100)
      annot <- data.frame(sample = sample(LETTERS[1:6], size = 100, replace = TRUE),
                          cont1 = rnorm(100), cont2 = rnorm(100, mean = 30))
      annot$condition <- ifelse(annot$sample %in% c("A", "B", "C"), "ctrl", "treated")
      head(annot)
      se <- SummarizedExperiment::SummarizedExperiment(Y, colData = annot)
      fit <- glm_gp(se, design = ~ condition + cont1 + cont2)

      # Test with reduced design
      res <- test_de(fit, reduced_design = ~ condition + cont1)
      head(res)

      # Test with contrast argument, the results are identical
      res2 <- test_de(fit, contrast = cont2)
      head(res2)

      # The column names of fit$Beta are valid variables in the contrast argument
      colnames(fit$Beta)

      # You can also have more complex contrasts:
      # the following compares cont1 vs cont2:
      test_de(fit, cont1 - cont2, n_max = 4)

      # You can also sort the output
      test_de(fit, cont1 - cont2, n_max = 4,
              sort_by = "pval")

      test_de(fit, cont1 - cont2, n_max = 4,
              sort_by = - abs(f_statistic))

      # If the data has multiple samples, it is a good
      # idea to aggregate the cell counts by samples.
      # This is called "pseudobulk".
      test_de(fit, contrast = "conditiontreated", n_max = 4,
              pseudobulk_by = sample)

      # You can also do the pseudobulk only on a subset of cells:
      cell_types <- sample(c("Tcell", "Bcell", "Makrophages"), size = 100, replace = TRUE)
      test_de(fit, contrast = "conditiontreated", n_max = 4,
              pseudobulk_by = sample,
              subset_to = cell_types == "Bcell")

      # Be care full, if you included the cell type information in
      # the original fit, after subsetting the design matrix would
      # be degenerate. To fix this, specify the full_design in 'test_de()'
      SummarizedExperiment::colData(se)$ct <- cell_types
      fit_with_celltype <- glm_gp(se, design = ~ condition + cont1 + cont2 + ct)
      test_de(fit_with_celltype, contrast = cont1, n_max = 4,
              full_design =  ~ condition + cont1 + cont2,
              pseudobulk_by = sample,
              subset_to = ct == "Bcell")

[Package glmGamPoi version 1.4.0 Index]