export_for_enrichment: Export to clusterProfiler

In zentnerlab/TSRexploreR: TSS and TSR Analysis

Export to clusterProfiler

Description

Export differential features for use in clusterProfiler term enrichment.

Usage

export_for_enrichment(
  experiment,
  data_type = c("tss", "tsr"),
  de_comparisons = "all",
  log2fc_cutoff = 1,
  fdr_cutoff = 0.05,
  keep_unchanged = FALSE,
  anno_categories = NULL
)

Arguments

experiment	TSRexploreR object.
data_type	Whether to export genes associated with differential TSSs ('tss') or TSRs ('tsr').
de_comparisons	Character vector of differential expression comparisons to export.
log2fc_cutoff	Differential features not meeting this |log2(fold change)| threshold will not be considered.
fdr_cutoff	Differential features not meeting this significance threshold will not be considered.
keep_unchanged	Logical for inclusion of genes not significantly changed in the exported list.
anno_categories	Vector of annotation categories to keep. If NULL no filtering by annotation type occurs.

Details

This function outputs a data.frame that is formatted for use with the 'compareCluster' function of the clusterProfiler library. The 'geneId', 'sample', and 'de_status' columns can be used in the formula 'geneId ~ sample + de_status'.

'de_comparisons' are the names given to the comparisons from the 'comparison_name' argument of the 'differential_expression' function. 'log2fc_cutoff' and 'fdr_cutoff' are the log2(fold change) and FDR cutoffs used for determination of significance.

'keep_unchanged' controls whether genes with the category of 'unchanged' (not differentially expressed) are returned in the table. Additionally, genes can be returned based on whether they have differential features within a certain relative genomic location, such as promoter. This is controlled by providing a vector of annotation types to 'anno_types'.

Value

data.frame of genes and differential expression status of TSSs or TSRs.

See Also

fit_de_model to fit a differential expression model. differential_expression to find differential TSSs or TSRs.

Examples

data(TSSs)
annotation <- system.file("extdata", "S288C_Annotation.gtf", package="TSRexploreR")
sample_sheet <- data.frame(
  sample_name=c(
    sprintf("S288C_D_%s", seq_len(3)),
    sprintf("S288C_WT_%s", seq_len(3))
  ),
  file_1=rep(NA, 6), file_2=rep(NA, 6),
  condition=c(rep("Diamide", 3), rep("Untreated", 3))
)

exp <- TSSs %>%
  tsr_explorer(sample_sheet=sample_sheet, genome_annotation=annotation) %>%
  format_counts(data_type="tss") %>%
  annotate_features(data_type="tss")

diff_tss <- exp %>%
  fit_de_model(data_type="tss", formula=~condition, method="edgeR") %>%
  differential_expression(
  data_type="tss", 
  comparison_name="Diamide_vs_Untreated",
  comparison_type="coef",
  comparison=2)
  
diff_tss <- export_for_enrichment(diff_tss, data_type="tss")

zentnerlab/TSRexploreR documentation built on Dec. 30, 2022, 10:27 p.m.