Package index • tidybulk

All functions
`X_cibersort`	Cibersort reference
`adjust_abundance()`	Adjust transcript abundance for unwanted variation
`aggregate_duplicates()`	Aggregates multiple counts from the same samples (e.g., from isoforms), concatenates other character columns, and averages other numeric columns
`as_SummarizedExperiment()`	as_SummarizedExperiment
`as_matrix()`	Get matrix from tibble
`cluster_elements()`	Get clusters of elements (e.g., samples or transcripts)
`deconvolve_cellularity()`	Get cell type proportions from samples
`describe_transcript()` `.describe_transcript_SE()`	Get DESCRIPTION from gene SYMBOL for Human and Mouse
`fill_missing_abundance()`	Fill transcript abundance if missing from sample-transcript pairs
`get_bibliography()`	Produces the bibliography list of your workflow
`identify_abundant()`	Identify abundant transcripts/genes
`impute_missing_abundance()`	impute transcript abundance if missing from sample-transcript pairs
`keep_abundant(<RangedSummarizedExperiment>)`	keep_abundant
`keep_abundant(<SummarizedExperiment>)`	keep_abundant
`keep_abundant()`	Filter to keep only abundant transcripts/genes
`keep_variable()`	Keep variable transcripts
`log10_reverse_trans()`	log10_reverse_trans
`logit_trans()`	logit scale
`pivot_sample()`	Extract sample-wise information
`pivot_transcript()`	Extract transcript-wise information
`quantile_normalise_abundance()`	Normalise by quantiles the counts of transcripts/genes
`reduce_dimensions()`	Dimension reduction of the transcript abundance data
`remove_redundancy()`	Drop redundant elements (e.g., samples) for which feature (e.g., transcript/gene) abundances are correlated
`resolve_complete_confounders_of_non_interest(<SummarizedExperiment>)` `resolve_complete_confounders_of_non_interest(<RangedSummarizedExperiment>)`	resolve_complete_confounders_of_non_interest
`resolve_complete_confounders_of_non_interest()`	Resolve Complete Confounders of Non-Interest
`rotate_dimensions()`	Rotate two dimensions (e.g., principal components) of an arbitrary angle
`scale_abundance()`	Scale the counts of transcripts/genes
`test_differential_abundance()`	Perform differential transcription testing using edgeR quasi-likelihood (QLT), edgeR likelihood-ratio (LR), limma-voom, limma-voom-with-quality-weights or DESeq2
`test_differential_expression()`	Perform differential expression testing using edgeR quasi-likelihood (QLT), edgeR likelihood-ratio (LR), limma-voom, limma-voom-with-quality-weights or DESeq2
`test_gene_enrichment()`	analyse gene enrichment with EGSEA
`test_gene_overrepresentation()`	analyse gene over-representation with GSEA
`test_gene_rank()`	analyse gene rank with GSEA
`test_stratification_cellularity(<SummarizedExperiment>)` `test_stratification_cellularity(<RangedSummarizedExperiment>)`	test_stratification_cellularity
`tximeta_summarizeToGene_object`	Needed for tests tximeta_summarizeToGene_object, It is SummarizedExperiment from tximeta
`vignette_manuscript_signature_boxplot`	Needed for vignette vignette_manuscript_signature_boxplot
`vignette_manuscript_signature_tsne`	Needed for vignette vignette_manuscript_signature_tsne
`vignette_manuscript_signature_tsne2`	Needed for vignette vignette_manuscript_signature_tsne2

Reference

All functions