# Brings transcriptomics to the tidyverse

## Documentation for package ‘tidybulk’ version 2.3.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

.describe_transcript_SE | Get DESCRIPTION from gene SYMBOL for Human and Mouse
adjust_abundance | Adjust transcript abundance for unwanted variation
adjust_abundance-method | 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
aggregate_duplicates-method | Aggregates multiple counts from the same samples (e.g., from isoforms), concatenates other character columns, and averages other numeric columns
as_matrix | Get matrix from tibble
as_SummarizedExperiment | as_SummarizedExperiment
as_SummarizedExperiment-method | as_SummarizedExperiment
cluster_elements | Get clusters of elements (e.g., samples or transcripts)
cluster_elements-method | Get clusters of elements (e.g., samples or transcripts)
deconvolve_cellularity | Get cell type proportions from samples
deconvolve_cellularity-method | Get cell type proportions from samples
describe_transcript | Get DESCRIPTION from gene SYMBOL for Human and Mouse
describe_transcript-method | 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
get_bibliography-method | Produces the bibliography list of your workflow
get_X_cibersort | Get Cibersort reference data
identify_abundant | Identify abundant transcripts/genes
identify_abundant-method | Identify abundant transcripts/genes
impute_missing_abundance | impute transcript abundance if missing from sample-transcript pairs
impute_missing_abundance-method | impute transcript abundance if missing from sample-transcript pairs
keep_abundant | Filter to keep only abundant transcripts/genes
keep_abundant-method | keep_abundant
keep_abundant-method | keep_abundant
keep_variable | Keep variable transcripts
keep_variable-method | Keep variable transcripts
log10_reverse_trans | Log10 reverse transformation for ggplot2
logit_trans | logit scale
pivot_sample | Extract sample-wise information
pivot_sample-method | Extract sample-wise information
pivot_transcript | Extract transcript-wise information
pivot_transcript-method | Extract transcript-wise information
quantile_normalise_abundance | Normalise by quantiles the counts of transcripts/genes
quantile_normalise_abundance-method | Normalise by quantiles the counts of transcripts/genes
reduce_dimensions | Dimension reduction of the transcript abundance data
reduce_dimensions-method | 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
remove_redundancy-method | Drop redundant elements (e.g., samples) for which feature (e.g., transcript/gene) abundances are correlated
resolve_complete_confounders_of_non_interest | Resolve Complete Confounders of Non-Interest
resolve_complete_confounders_of_non_interest-method | resolve_complete_confounders_of_non_interest
rotate_dimensions | Rotate two dimensions (e.g., principal components) of an arbitrary angle
rotate_dimensions-method | Rotate two dimensions (e.g., principal components) of an arbitrary angle
scale_abundance | Scale the counts of transcripts/genes
scale_abundance-method | Scale the counts of transcripts/genes
scale_x_log10_reverse | scale_x_log10_reverse
scale_y_log10_reverse | scale_y_log10_reverse
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_abundance-method | 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_differential_expression-method | 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_enrichment-method | analyse gene enrichment with EGSEA
test_gene_overrepresentation | analyse gene over-representation with GSEA
test_gene_overrepresentation-method | analyse gene over-representation with GSEA
test_gene_rank | analyse gene rank with GSEA
test_gene_rank-method | analyse gene rank with GSEA
test_stratification_cellularity-method | 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
X_cibersort | Cibersort reference