View source: R/diff_exp_plots.R
export_for_enrichment | R Documentation |
Export differential features for use in clusterProfiler term enrichment.
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 )
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. |
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'.
data.frame of genes and differential expression status of TSSs or TSRs.
fit_de_model
to fit a differential expression model.
differential_expression
to find differential TSSs or TSRs.
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")
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