conditionTest {tradeSeq} | R Documentation

## Assess differential expression patterns between conditions within a lineage.

### Description

Assess differential expression patterns between conditions within a lineage.

Assess differential expression patterns between conditions within a lineage.

### Usage

    conditionTest(models, ...)

    ## S4 method for signature 'SingleCellExperiment'
    conditionTest(
      models,
      global = TRUE,
      pairwise = FALSE,
      lineages = FALSE,
      knots = NULL,
      l2fc = 0,
      eigenThresh = 0.01
    )

### Arguments

`models` | The fitted GAMs, typically the output from `fitGAM`. For `conditionTest`, these are required to be a `singleCellExperiment` object.
`...` | parameters including:
`global` | If TRUE, test for all pairwise comparisons simultaneously, i.e. test for DE between all conditions in all lineages.
`pairwise` | If TRUE, return output for all comparisons between pairs of conditions. Both `global` and `pairwise` can be TRUE.
`lineages` | If TRUE, return output for all comparisons within each lineage. Both `global` and `lineages` can be TRUE. If both `lineages` and `pairwise` are TRUE, the function returns output for all pairs of conditions within each lineage.
`knots` | Default to NULL. Otherwise, a vector of length 2 specifying the smallest and largest knots that are contrasted between conditions.
`l2fc` | The log2 fold change threshold to test against. Note, that this will affect both the global test and the pairwise comparisons.
`eigenThresh` | Eigenvalue threshold for inverting the variance-covariance matrix of the coefficients to use for calculating the Wald test statistics. Lower values are more lenient to adding more information but also decrease computational stability. This argument should in general not be changed by the user but is provided for back-compatability. Set to `1e-8` to reproduce results of older version of `tradeSeq`.

### Value

A matrix with the wald statistic, the number of degrees of freedom and the p-value associated with each gene for all the tests performed.

### Examples

    ## artificial example
    data(crv, package = "tradeSeq")
    data("countMatrix", package = "tradeSeq")
    conditions <- factor(sample(1:2, size = ncol(countMatrix), replace = TRUE))
    sce <- fitGAM(as.matrix(countMatrix), sds = crv, conditions = conditions)
    res <- conditionTest(sce)

[Package tradeSeq version 1.8.0 Index]