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DE between time points

Source: R/DE_between_time.R
DE_between_time.Rd

Differential expression analysis between time points within each group by limma

Usage

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

Arguments

se_obj

A SummarizedExperiment object created by create_input

group

(Optional) A character vector specifying which groups to analyse. If NULL, all groups in the 'Group' column will be used. (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 and time comparison, all features); de_list (nested list of significant features only); fit_list (nested list of limma MArrayLM fit objects, for use with limma::plotSA()); time_series (vector of all available time points). The output can be passed to plot_DE_between_time() for visualisation.

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_time_out <- DE_between_time(example_obj, assay = 1)
#> Non-NA replicate number filter not specified. Using minimum number of replicates across all groups and time points: 0
#> Comparing group IFNbeta time 2 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 4 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 6 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 8 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 24 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 4 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 6 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 8 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 24 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 6 to 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 8 to 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 24 to 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 8 to 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 24 to 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta time 24 to 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 2 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 4 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 6 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 8 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 24 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 4 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 6 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 8 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 24 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 6 to 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 8 to 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 24 to 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 8 to 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 24 to 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma time 24 to 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 2 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 4 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 6 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 8 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 24 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 4 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 6 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 8 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 24 to 2: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 6 to 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 8 to 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 24 to 4: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 8 to 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 24 to 6: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS time 24 to 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated time 8 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated time 24 to 0: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated time 24 to 8: keeping 100 of 100 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero