DE between groups

Differential expression analysis between groups at each time point by limma. The function compares each pair of groups at each time point, and returns a nested list of DE analysis results for each pair of groups and each time point including all features, as well as a list of filtered DE results for each pair of groups based on the specified thresholds including only significant features. Use plot_DE_between_group() to visualise the number of DE features between groups over time.

Usage

DE_between_group(
  se_obj,
  group = NULL,
  filter = NULL,
  assay,
  adjP_thres = 0.05,
  logFC_thres = 1,
  trend = FALSE
)

Arguments

se_obj

A SummarizedExperiment object

group

(Optional) A character vector specifying which group to be compared to. If NULL, all groups in the 'Group' column will be compared to. (default is NULL)

filter

(Optional) Minimum number of replicates required in both conditions for a feature to be tested. If NULL, the minimum number of replicates across all groups and time points will be used. (default is NULL)

assay

Assay to use: "orig" for original data, "norm" for normalised-to-start data. Numeric indices (1, 2) are also accepted. No default, must be specified explicitly. The selected assay should contain log-transformed, normalised values (e.g. log2-CPM for RNA-seq, log2-intensity for proteomics). A warning is issued if the data appears to be un-logged raw counts.

adjP_thres

(Optional) Threshold for adjusted p-value to consider a feature as differentially expressed (default is 0.05)

logFC_thres

(Optional) Threshold for log2 fold change to consider a feature as differentially expressed (default is 1)

trend

(Optional) Logical, passed to limma::eBayes(). Set to TRUE for RNA-seq count-derived data to model the mean-variance trend. Leave as FALSE (default) for microarray, proteomics, metabolomics, or other log-intensity data where the mean-variance relationship is typically flat.

Value

A list with: all_list (nested list of DE results per group pair and time point, all features); de_list (significant features only); fit_list (nested list of limma MArrayLM fit objects, for use with limma::plotSA()); ref_groups (group to be compared to); all_groups (all groups). The output can be passed to plot_DE_between_group() for visualisation.

Details

Only time points present in both groups are compared. No multiple-testing correction is applied across the pairwise group-by-time comparisons; each comparison is independent. Apply your own correction (e.g., stats::p.adjust()) across combined results if needed.

Examples

data(example_obj)
example_obj <- normalise_to_start(example_obj)
#> Normalising to group baseline at each feature's first non-NA time point.

DE_between_group_out <- DE_between_group(example_obj, assay = 2)
#> Non-NA replicate number filter not specified. Using minimum number of replicates across all groups and time points: 0
#> Comparing group IFNgamma to IFNbeta at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to IFNbeta at Time 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to IFNbeta at Time 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to IFNbeta at Time 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to IFNbeta at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to IFNbeta at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing untreated vs IFNbeta: time points only in IFNbeta: 2, 4, 6; only in untreated: none
#> Comparing group untreated to IFNbeta at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to IFNbeta at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to IFNbeta at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing untreated vs IFNgamma: time points only in IFNgamma: 2, 4, 6; only in untreated: none
#> Comparing group untreated to IFNgamma at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to IFNgamma at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to IFNgamma at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing untreated vs LPS: time points only in LPS: 2, 4, 6; only in untreated: none
#> Comparing group untreated to LPS at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to LPS at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to LPS at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing IFNbeta vs untreated: time points only in untreated: none; only in IFNbeta: 2, 4, 6
#> Comparing group IFNbeta to untreated at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to untreated at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to untreated at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing IFNgamma vs untreated: time points only in untreated: none; only in IFNgamma: 2, 4, 6
#> Comparing group IFNgamma to untreated at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to untreated at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to untreated at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing LPS vs untreated: time points only in untreated: none; only in LPS: 2, 4, 6
#> Comparing group LPS to untreated at Time 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to untreated at Time 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to untreated at Time 24: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero