Table 2.

Grammar of the functions and packages integrated in the current version 1.1.7 [32]

Name	Description
Analysis
Function name	Description	Integrated packages
adjust_abundance	Remove known unwanted variation	ComBat [33]
aggregate_duplicates	Summarize the abundance of duplicated transcripts (e.g., isoforms)
cluster_elements	Identify sample or transcript clusters	Kmeans [34], SNN [20]
deconvolve_cellularity	Identify cell type fraction within each sample	Cibersort [23], EPIC [24], lsfit [35]
identify_abundant	Identify abundant transcripts to be used in subsequent analyses	edgeR [13]
keep_abundant	Filter out rare transcripts
keep_variable	Filter out non-variable transcripts	limma [31]
reduce_dimensions	Calculate reduced dimensions of transcript abundance	limma [31], PCA [35], Rtsne [21]
remove_redundancy	Filter out redundant samples or transcripts
scale_abundance	Scale (i.e., normalize) the transcript abundance to compensate for diverse sequencing depth across samples	TMM [14]
test_differential_abundance	Test the hypothesis of differential abundance of transcripts across biological/experimental conditions	edgeR [13], DESeq2 [16], limma-voom [29]
test_gene_enrichment	Test the hypothesis of rank-based enrichment of transcript signatures	EGSEA [36]
test_gene_overrepresentation	Test the hypothesis of gene set enrichment for an unranked gene list	clusterProfiler [26]
test_differential_cellularity	Test the hypothesis of differential tissue composition	lm [35], coxph [17, 37]
Main utilities
get_bibliography	Extract the bibliography for your workflow from any tidybulk object
impute_missing_abundance	Impute abundance for missing data points using sample groupings
pivot_sample	Extract non-redundant sample-related information from the data frame
pivot_transcript	Extract non-redundant transcript-related information from the data frame
tidybulk	Create a tidybulk data frame from a standard data frame
tidybulk_SAM_BAM	Infer transcript abundance from mapped reads and create a tidybulk data frame	featureCounts [12]