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Test for differential abundance using edgeR

Source: R/runDA.R
runDA.Rd

Test for differential abundance of entities using functions from the edgeR package. This adapts edgerWrp to accept input as a TreeSummarizedExperiment (TSE) object instead of a matrix. Features could be represented in either rows or columns. By default, features are in the rows. Then, samples are in columns and the sample information is in colData. The tree that stores the hierarchical information about features is in rowTree. Each row of the assays can be mapped to a node of the tree. Data on rows that are mapped to internal nodes is generated from data on leaf nodes. Normalization for samples is automatically performed by edgeR and the library size is calculated using features that are mapped to leaf nodes.

Usage

runDA(
  TSE,
  feature_on_row = TRUE,
  assay = NULL,
  option = c("glm", "glmQL"),
  design = NULL,
  contrast = NULL,
  filter_min_count = 10,
  filter_min_total_count = 15,
  filter_large_n = 10,
  filter_min_prop = 0.7,
  normalize = TRUE,
  normalize_method = "TMM",
  group_column = "group",
  design_terms = "group",
  ...
)

Arguments

TSE

A TreeSummarizedExperiment object.

feature_on_row

A logical scalar. If TRUE (default), features or entities (e.g. genes, OTUs) are in rows of the assays tables, and samples are in columns; otherwise, it's the other way around.

assay

A numeric index or assay name to specify which assay from assays is used for analysis.

option

Either "glm" or "glmQL". If "glm", glmFit and glmLRT are used; otherwise, glmQLFit and glmQLFTest are used. Details about the difference between two options are in the help page of glmQLFit.

design

A numeric design matrix. If NULL, all columns of the sample annotation will be used to create the design matrix.

contrast

A numeric vector specifying one contrast of the linear model coefficients to be tested equal to zero. Its length must equal to the number of columns of design. If NULL, the last coefficient will be tested equal to zero.

filter_min_count

A numeric value, passed to min.count of filterByExpr.

filter_min_total_count

A numeric value, passed to min.total.count of filterByExpr.

filter_large_n

A numeric value, passed to large.n of filterByExpr.

filter_min_prop

A numeric value, passed to min.prop of filterByExpr.

normalize

A logical scalar indicating whether to estimate normalization factors (using calcNormFactors).

normalize_method

Normalization method to be used. See calcNormFactors for more details.

group_column

The name of the column in the sample annotation providing group labels for samples (currently not used).

design_terms

The names of columns from the sample annotation that will be used to generate the design matrix. This is ignored if design is provided.

...

More arguments to pass to glmFit (option = "glm" or glmQLFit (option = "glmQL").

Value

A list with entries edgeR_results, tree, and nodes_drop.

edgeR_results

The output of glmQLFTest or glmLRT depending on the specified option.

tree

The hierarchical structure of entities that was stored in the input TSE.

nodes_drop

A vector storing the alias node labels of entities that are filtered before analysis due to low counts.

Details

The experimental design is specified by a design matrix and provided via the argument design. More details about the calculation of normalization factor could be found from calcNormFactors.

Author

Ruizhu Huang

Examples

suppressPackageStartupMessages({
    library(TreeSummarizedExperiment)
})

## Load example data set
lse <- readRDS(system.file("extdata", "da_sim_100_30_18de.rds",
                           package = "treeclimbR"))

## Aggregate counts on internal nodes
nodes <- showNode(tree = tinyTree, only.leaf = FALSE)
tse <- aggTSE(x = lse, rowLevel = nodes)

dd <- model.matrix(~ group, data = colData(tse))
out <- runDA(TSE = tse, feature_on_row = TRUE,
             assay = 1, option = "glmQL",
             design = dd, contrast = NULL,
             normalize = TRUE, filter_min_count = 2)
names(out)
#> [1] "edgeR_results" "nodes_drop"    "tree"         
out$nodes_drop
#> [1] "alias_1"  "alias_8"  "alias_12" "alias_18"
edgeR::topTags(out$edgeR_results, sort.by = "PValue")
#> Coefficient:  groupB 
#>               logFC   logCPM          F       PValue          FDR
#> alias_16  1.0468351 16.09379 25.4239062 1.541511e-06 2.312267e-05
#> alias_5   0.7600070 15.55706  8.4473672 4.308004e-03 3.231003e-02
#> alias_6   0.6596231 15.67131  7.3778576 7.517163e-03 3.758582e-02
#> alias_9  -0.1652670 16.97145  0.8615460 3.550504e-01 6.088225e-01
#> alias_10 -0.1684029 16.75636  0.8066779 3.707893e-01 6.088225e-01
#> alias_15 -0.1539229 16.64917  0.7248203 3.961568e-01 6.088225e-01
#> alias_17 -0.1598991 16.60601  0.6872196 4.086549e-01 6.088225e-01
#> alias_7  -0.1492673 16.69156  0.6354511 4.268383e-01 6.088225e-01
#> alias_4  -0.1490846 16.41854  0.5726784 4.505882e-01 6.088225e-01
#> alias_19 -0.1549905 16.43653  0.5647012 4.537526e-01 6.088225e-01