Brings transcriptomics to the tidyverse


[Up] [Top]

Documentation for package ‘tidybulk’ version 1.99.4

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