res {distinct} | R Documentation

## Results from `distinct_test` function

### Description

Results from `distinct_test` function

### Arguments

`res` | contains a `data.frame` object, with the results obtained applying `distinct_test` function to `Kang_subset` dataset. Below the code used to obtain 'res'.

### Author(s)

Simone Tiberi simone.tiberi@uzh.ch

### See Also

`distinct_test`

### Examples

    # load the input data:
    # data("Kang_subset", package = "distinct")
    # Kang_subset
    #
    # create the design of the study:
    # samples = Kang_subset@metadata$experiment_info$sample_id
    # group = Kang_subset@metadata$experiment_info$stim
    # design = model.matrix(~group)
    # rownames of the design must indicate sample ids:
    # rownames(design) = samples
    # design
    #
    # Note that the sample names in `colData(x)$name_sample` have to be the same ones as those in `rownames(design)`.
    # rownames(design)
    # unique(SingleCellExperiment::colData(Kang_subset)$sample_id)
    #
    # In order to obtain a finer ranking for the most significant genes, if computational resources are available, we encourage users to increase P_4 (i.e., the number of permutations when a raw p-value is < 0.001) and set P_4 = 20,000 (by default P_4 = 10,000).
    #
    # The group we would like to test for is in the second column of the design, therefore we will specify: column_to_test = 2
    #
    # set.seed(61217)
    # res = distinct_test(
    #   x = Kang_subset,
    #   name_assays_expression = "logcounts",
    #   name_cluster = "cell",
    #   design = design,
    #   column_to_test = 2,
    #   min_non_zero_cells = 20,
    #   n_cores = 2)
    #
    #   save(res, file = "res.RData")
    #   saveRDS(res, file = "res.rds")

[Package distinct version 1.4.1 Index]