Table 2 Grammar of the functions and packages integrated in the current version 1.1.7 [32]

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]

 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]