R/AllGenerics.R
, R/earlyDETest.R
earlyDETest.Rd
Perform test of differential expression patterns between lineages in a user-defined region based on the knots of the smoothers.
earlyDETest(models, ...) # S4 method for SingleCellExperiment earlyDETest( models, global = TRUE, pairwise = FALSE, knots = NULL, nPoints = 2 * nknots(models), l2fc = 0, eigenThresh = 0.01 ) # S4 method for list earlyDETest( models, global = TRUE, pairwise = FALSE, knots = NULL, nPoints = 2 * nknots(models), l2fc = 0, eigenThresh = 0.01 )
models | The fitted GAMs, typically the output from
|
---|---|
... | parameters including: |
global | If TRUE, test for all pairwise comparisons simultaneously. |
pairwise | If TRUE, test for all pairwise comparisons independently. |
knots | A vector of length 2 specifying the knots at the start and end of the region of interest. |
nPoints | The number of points to be compared between lineages. Defaults to twice the number of knots |
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 |
A matrix with the wald statistic, the number of df and the p-value associated with each gene for all the tests performed. Also, for each possible pairwise comparision, the observed log fold changes. If the testing procedure was unsuccessful, the procedure will return NA test statistics, fold changes and p-values.
To help the user in choosing which knots to use when defining the
branching, the plotGeneCount
function has a models optional
parameter that can be used to visualize where the knots are.
data(gamList, package = "tradeSeq") earlyDETest(gamList, knots = c(1, 2), global = TRUE, pairwise = TRUE)#>#> waldStat df pvalue fcMedian #> 1700012B09Rik 1.071612e-16 2 1.0000000 0.4901689 #> 1700017B05Rik 3.960814e+00 2 0.1380130 1.4803166 #> 1700020L24Rik 1.071612e-16 2 1.0000000 0.4901689 #> 1810009A15Rik 1.611391e+00 2 0.4467770 1.7931856 #> 1810011H11Rik 1.071612e-16 2 1.0000000 0.4901689 #> 1810027O10Rik 1.985099e-01 2 0.9055118 0.2846985 #> 1810031K17Rik 1.027339e+00 2 0.5982960 1.1570870 #> 1810033B17Rik 1.071612e-16 2 1.0000000 0.4901689 #> 1810037I17Rik 1.484424e+00 2 0.4760596 0.2644434 #> 2010002N04Rik 1.071612e-16 2 1.0000000 0.4901689 #> 2010011I20Rik 1.810283e-01 2 0.9134614 55.4715290