runDS: Test for differential state using edgeR
In fionarhuang/treeclimbR: An algorithm to find optimal signal levels in a tree

runDS

R Documentation

Test for differential state using edgeR

Description

Test for differential state of entities using functions from the edgeR package. This adapts edgerWrp to accept input as a SummarizedExperiment (SE) object instead of matrix. Each assay should correspond to data for one node of the tree. Samples are in columns and features are in rows. The sample information is in colData. The tree that stores the hierarchical relation between the assays is provided via the argument tree.

Usage

runDS(
  SE,
  tree,
  option = c("glm", "glmQL"),
  design = NULL,
  contrast = NULL,
  filter_min_count = 1,
  filter_min_total_count = 15,
  filter_large_n = 10,
  filter_min_prop = 1,
  min_cells = 10,
  normalize = TRUE,
  normalize_method = "TMM",
  group_column = "group_id",
  design_terms = "group_id",
  message = TRUE,
  ...
)

Arguments

`SE`	A `SummarizedExperiment` object, typically generated by `aggDS`.
`tree`	A `phylo` object. Each assay of `SE` stores data for one node of the tree.
`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`.
`min_cells`	A numeric scalar specifying the minimal number of cells in a node required to include a node in the analysis. The information about the number of cells per node and sample should be available in `metadata(SE)$n_cells`. A node is retained if at least half of the samples have at least `min_cells` cells belonging to the node.
`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. This annotation is used for filtering.
`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.
`message`	A logical scalar, indicating whether progress messages should be printed.
`...`	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: A list. Each of the elements contains the output of glmQLFTest or glmLRT for one node, depending on the specified option.
tree: The hierarchical structure of entities that was stored in the input SE.
nodes_drop: A vector storing the alias node labels of entities that are filtered before analysis due to low counts.

Author(s)

Ruizhu Huang

Examples

suppressPackageStartupMessages({
    library(TreeSummarizedExperiment)
})
## Load example data
ds_tse <- readRDS(system.file("extdata", "ds_sim_20_500_8de.rds",
                              package = "treeclimbR"))
ds_se <- aggDS(TSE = ds_tse, assay = "counts", sample_id = "sample_id",
               group_id = "group", cluster_id = "cluster_id", FUN = sum)
## Information about the number of cells is provided in the metadata
S4Vectors::metadata(ds_se)$n_cells

ds_res <- runDS(SE = ds_se, tree = colTree(ds_tse), option = "glmQL",
                group_column = "group", contrast = c(0, 1),
                filter_min_count = 0, filter_min_total_count = 1,
                design = model.matrix(~ group, data = colData(ds_se)),
                filter_min_prop = 0, min_cells = 5, message = FALSE)
## Top differential features (across nodes)
nodeResult(ds_res, type = "DS")

fionarhuang/treeclimbR documentation built on Nov. 7, 2024, 4:17 a.m.