Package index • bulkMAE

Plotting

Standard ggplot2 views of QC, embeddings, differential expression, enrichment, and assays.

plot_assay_heatmap(): Plot selected assay features as a heatmap
plot_de_ma(): Plot a differential-expression MA plot
plot_de_volcano(): Plot a differential-expression volcano plot
plot_embedding(): Plot a sample embedding
plot_gsea_classic(): Plot one classic GSEA running-score profile
plot_gsea_ridge(): Plot GSEA rank-metric distributions as ridges
plot_ora_bubble(): Plot explicitly selected ORA terms as bubbles
plot_ora_network(): Plot an ORA term-feature community network
plot_ora_radial(): Plot an ORA radial term-feature network
plot_qc_correlation(): Plot a sample correlation matrix
plot_qc_library(): Plot sample library QC metrics
plot_qc_outliers(): Plot robust sample outlier distances
plot_save(): Save a bulkMAE plot at its recommended physical size
theme_bulkmae(): bulkMAE plot theme

Functions

activity_decouple(): Run one or more decoupleR methods
activity_matrix(): Convert long-format activity results to a source-by-sample matrix
activity_methods(): List methods available through decoupleR
activity_progeny(): Infer pathway activity with PROGENy and decoupleR
activity_resource(): Retrieve a network or gene-set resource through decoupleR
activity_resources(): List molecular resources available through decoupleR
activity_tf(): Infer transcription-factor activity with decoupleR
adjust_batch(): Remove batch effects from continuous expression data with limma
adjust_combat(): Adjust a continuous assay with ComBat
adjust_combatseq(): Adjust integer counts with ComBat-seq
adjust_covariates(): Extract aligned adjustment covariates
adjust_ruv(): Estimate unwanted factors with RUVg
adjust_sva(): Estimate surrogate variables with sva
annotate_biomart(): Query Ensembl through biomaRt
annotate_ensembl(): Remove Ensembl version suffixes
annotate_gene_lengths(): Retrieve representative transcript lengths from Ensembl
annotate_ids(): Map feature identifiers with an AnnotationDbi database
annotate_rekey(): Apply an identifier mapping to common gene-level values
annotation_orgdb(): Resolve a local organism annotation database
bulkmae_rename(): Look up the 0.4 name for a pre-0.4 function
cluster_consensus(): Run consensus clustering
cluster_consensus_classes(): Extract sample classes from a consensus-clustering solution
cluster_consensus_diagnostics(): Calculate consensus-clustering stability diagnostics
cluster_nmf(): Run non-negative matrix factorization
cluster_nmf_classes(): Extract sample or feature classes from an NMF fit
coexpr_differential(): Test differential co-expression between two groups
coexpr_modules(): Extract named WGCNA module labels
coexpr_pick_power(): Evaluate candidate WGCNA soft-thresholding powers
coexpr_preservation(): Test preservation of reference WGCNA modules in another cohort
coexpr_wgcna(): Detect co-expression modules with WGCNA
de_contrast(): Construct limma-compatible contrasts from MAE sample metadata
de_deseq2(): Fit a DESeq2 differential-expression model
de_deseq2_results(): Extract DESeq2 results
de_design(): Construct a model design matrix from MAE sample metadata
de_dream(): Fit a repeated-measures model with dream
de_edger(): Fit an edgeR quasi-likelihood model
de_limma(): Fit a limma model to continuous expression or score data
de_masigpro(): Fit a maSigPro time-course or dose-response model
de_masigpro_design(): Build a maSigPro experimental-design table from sample metadata
de_ranks(): Extract finite named ranks from differential-expression results
de_selected(): Select differential features as a named logical vector
de_table(): Convert native differential-expression results to a stable table
de_variance(): Partition expression variance among model terms
de_voom(): Fit a limma-voom differential-expression model
deconv(): Deconvolve bulk expression with immunedeconv
deconv_bayesprism(): Deconvolve bulk counts with BayesPrism
deconv_cibersortx_input(): Prepare a CIBERSORTx mixture table
deconv_music(): Deconvolve bulk counts with MuSiC
deconv_reference(): Construct a single-cell deconvolution reference
drug_lincs(): Search a LINCS reference database for connected signatures
drug_lincs_databases(): List supported signatureSearch reference databases
drug_query(): Prepare an up/down query for LINCS or CMap
dtu_diffsplice(): Test differential exon or transcript usage from a fitted limma/edgeR model
enrich_camera(): Run CAMERA competitive gene-set testing
enrich_fgsea(): Run preranked gene-set enrichment with fgsea
enrich_fry(): Run FRY rotation gene-set testing
enrich_go(): Run Gene Ontology enrichment
enrich_goseq(): Run selection-bias-aware enrichment with goseq
enrich_gsea(): Run preranked enrichment against arbitrary gene sets with clusterProfiler
enrich_kegg(): Run KEGG enrichment
enrich_ora(): Run over-representation analysis against arbitrary gene sets
enrich_reactome(): Run Reactome enrichment
enrich_roast(): Run mroast rotation gene-set testing
filter_expr(): Filter lowly expressed features with edgeR
gene_sets_msigdb(): Retrieve MSigDB gene sets through msigdbr
gene_sets_prepare(): Standardize gene sets as a named list
gene_sets_read_gmt(): Read gene sets from a GMT file
import_tximport(): Import transcript abundance estimates with tximport
mae_add_assay(): Add an aligned assay to one experiment
mae_add_experiment(): Add an experiment while preserving the MAE sample map
mae_add_feature_data(): Add aligned feature metadata to one experiment
mae_add_sample_data(): Add aligned sample metadata to one experiment
mae_assays(): List assays in one experiment
mae_create(): Construct a MultiAssayExperiment
mae_create_experiment(): Construct a SummarizedExperiment leaf
mae_experiments(): List experiments in a MultiAssayExperiment
mae_feature_data(): Extract feature metadata from one experiment
mae_from_matrix(): Construct a one-experiment MAE from a matrix
mae_pull_assay(): Extract an assay matrix
mae_pull_experiment(): Extract an experiment aligned to primary sample data
mae_samples(): Extract aligned sample metadata
mae_simulate(): Simulate a self-contained bulk-transcriptomics MAE
mae_subset_features(): Subset features in one experiment
mae_validate(): Validate the package's MultiAssayExperiment contract
meta_collect(): Collect one feature's effect estimates across differential analyses
meta_effect(): Fit a univariate or meta-regression model
ml_glmnet(): Fit a cross-validated glmnet prediction model
network_genie3(): Infer a gene regulatory network with GENIE3
network_genie3_links(): Convert GENIE3 weights to a ranked edge list
network_string(): Retrieve STRING interactions for selected assay features
normalize_deseq(): Estimate DESeq2 size factors
normalize_tmm(): Calculate TMM normalization factors
normalize_tpm(): Convert counts to transcripts per million
qc_correlation(): Calculate sample-to-sample correlations
qc_library(): Calculate sample-level library metrics
qc_outliers(): Flag samples with unusually large robust PCA distances
reduce_mds(): Run sample multidimensional scaling
reduce_pca(): Run sample principal component analysis
reduce_tsne(): Run t-SNE on samples
reduce_umap(): Run UMAP on samples
score_gsva(): Calculate GSVA scores
score_signature(): Score a gene-expression signature
score_singscore(): Score samples with rank-based singscore
score_ssgsea(): Calculate single-sample GSEA scores
surv_cox(): Fit a Cox proportional-hazards model
surv_formula(): Construct a namespace-safe survival formula
surv_km(): Fit Kaplan-Meier survival curves
surv_penalized(): Fit a cross-validated penalized Cox model
surv_roc(): Calculate time-dependent ROC curves
transform_rlog(): Apply the DESeq2 regularized-log transformation
transform_voom(): Apply limma-voom transformation and precision weighting
transform_vst(): Apply the DESeq2 variance-stabilizing transformation