Compare difference in estimates between two trees

Compare difference in cofficient estimates between two trees. For node i, the test evaluates tree1[i] - tree2[i] = 0.

Usage

diffTree(tree1, tree2)

Arguments

tree1: object of type treedata from treeTest()
tree2: object of type treedata from treeTest()

Details

When a fixed effect test is performed at each node using treeTest() with method = "FE.empirical" or method = "FE", a coefficient estimate and standard error are estimated for each node based on the children. This function performs a two-sample z-test to test if a given coefficient from tree1 is significantly different from the corresponding coefficient in tree2.

Examples

library(variancePartition)

# Load cell counts, clustering and metadata
# from Kang, et al. (2018) https://doi.org/10.1038/nbt.4042
data(IFNCellCounts)

# Simulate a factor with 2 levels called DiseaseRand
set.seed(123)
info$DiseaseRand = sample(LETTERS[seq(2)], nrow(info), replace=TRUE)
info$DiseaseRand = factor(info$DiseaseRand, LETTERS[seq(2)])

# Apply crumblr transformation
cobj <- crumblr(df_cellCounts)

# Use dream workflow to analyze each cell separately
fit <- dream(cobj, ~ StimStatus + ind, info)
fit <- eBayes(fit)

# Perform multivariate test across the hierarchy
res1 <- treeTest(fit, cobj, hcl, coef = "StimStatusstim")

# Perform same test, but on DiseaseRand
fit2 <- dream(cobj, ~ DiseaseRand, info)
fit2 <- eBayes(fit2)
res2 <- treeTest(fit2, cobj, hcl, coef = "DiseaseRandB")

# Compare the coefficient estimates at each node
# Test if res1 - res2 is significantly different from zero
resDiff = diffTree(res1, res2)

resDiff
#> 'treedata' S4 object'.
#> 
#> ...@ phylo:
#> 
#> Phylogenetic tree with 8 tips and 7 internal nodes.
#> 
#> Tip labels:
#>   B cells, CD14+ Monocytes, CD4 T cells, CD8 T cells, Dendritic cells, FCGR3A+
#> Monocytes, ...
#> Node labels:
#>   B cells/CD14+ Monocytes/CD4 T cells/CD8 T cells/Dendritic cells/FCGR3A+
#> Monocytes/Megakaryocytes/NK cells, B cells/CD4 T cells/CD8 T
#> cells/Megakaryocytes/NK cells, CD14+ Monocytes/Dendritic cells/FCGR3A+
#> Monocytes, CD14+ Monocytes/FCGR3A+ Monocytes, B cells/CD4 T cells/CD8 T
#> cells/NK cells, CD4 T cells/CD8 T cells/NK cells, ...
#> 
#> Rooted; includes branch lengths.
#> 
#> with the following features available:
#>   'beta', 'se', 'stat', 'pvalue', 'n_features', 'lambda', 'method', 'FDR'.
#> 
#> # The associated data tibble abstraction: 15 × 11
#> # The 'node', 'label' and 'isTip' are from the phylo tree.
#>     node label      isTip    beta     se   stat  pvalue n_features lambda method
#>    <int> <chr>      <lgl>   <dbl>  <dbl>  <dbl>   <dbl>      <dbl>  <dbl> <chr> 
#>  1     1 B cells    TRUE   0.0625 0.151   0.414 6.79e-1          1   0.01 FE.em…
#>  2     2 CD14+ Mon… TRUE  -0.0493 0.152  -0.324 7.46e-1          1   0.01 FE.em…
#>  3     3 CD4 T cel… TRUE  -0.327  0.208  -1.57  1.16e-1          1   0.01 FE.em…
#>  4     4 CD8 T cel… TRUE  -0.229  0.372  -0.615 5.38e-1          1   0.01 FE.em…
#>  5     5 Dendritic… TRUE   0.367  0.281   1.30  1.93e-1          1   0.01 FE.em…
#>  6     6 FCGR3A+ M… TRUE   0.528  0.181   2.92  3.45e-3          1   0.01 FE.em…
#>  7     7 Megakaryo… TRUE  -0.152  0.191  -0.798 4.25e-1          1   0.01 FE.em…
#>  8     8 NK cells   TRUE  -0.0955 0.157  -0.606 5.44e-1          1   0.01 FE.em…
#>  9     9 B cells/C… FALSE -0.0459 0.0242 -1.90  5.74e-2          8   0.01 FE.em…
#> 10    10 B cells/C… FALSE -0.197  0.0520 -3.79  1.51e-4          5   0.01 FE.em…
#> # ℹ 5 more rows
#> # ℹ 1 more variable: FDR <dbl>

plotTreeTest(resDiff)


plotTreeTestBeta(resDiff)