Function reference • DIANE

DIANE User Interface
`run_app()`	Run the Shiny Application
Pre-processing
`normalize()`	Normalize raw count data
`filter_low_counts()`	Remove low count genes
`aggregate_splice_variants()`	Aggregate splice variants
`get_locus()`	Get gene IDs loci, without splicing information
Differential expression analysis
`estimateDEGs()`	Estimates Differentially Expression Genes
`estimateDEGs_legacy()`	Estimates Differentially Expression Genes. Legacy version. This function should no longer be used, unless for reproductibility purpose.
`estimateDispersion()`	Estimates gene dispersions prior to differential expression testing
Expression based clustering
`run_coseq()`	Mixture Model clustering
`get_genes_in_cluster()`	Get genes in a specific cluster
`fit_glm()`	Fits a Poisson generalized linear model to a set of genes
Gene ontologies and annotations
`enrich_go()`	Enriched ontologies for recognized organisms
`enrich_go_custom()`	Enriched ontologies for a custom organism
`convert_from_agi()`	For A. thaliana, converts TAIR IDs to entrez IDs, gene symbol or description
`convert_from_ensembl()`	For H. sapiens, converts ensembl IDs to entrez IDs, symbol or name
`convert_from_ensembl_ce()`	For C. elegans, converts ensembl IDs to entrez IDs, symbol or name
`convert_from_ensembl_dm()`	For D. melanogaster, converts ensembl IDs to entrez IDs, symbol or name
`convert_from_ensembl_eck12()`	For E coli, converts ensembl IDs to entrez IDs, symbol or name
`convert_from_ensembl_mus()`	For M. musculus, converts ensembl IDs to entrez IDs, symbol or name
`get_gene_information()`	Get genes information
Gene regulatory network inference
`network_data()`	Creates data describing a gene regulatory network
`network_from_tests()`	Create network from edges statistical tests
`network_inference()`	Regulatory importances estimation via Random Forests
`network_thresholding()`	Thresholds a non sparse adjascency matrix to return the strongest weights only
`test_edges()`	Statistical edges testing by permutations
`estimate_test_edges_time()`	Estimates time spent for statistical testing
`group_regressors()`	Group correlated regulators
`describe_node()`	Targets and regulators of a gene in a network
Visualization functions
`draw_DEGs()`	MA or volcano plot for differential expression results
`draw_PCA_legacy()`	Draw PCA results Legacy
`draw_coseq_run()`	Displays clustering results and quality
`draw_discarded_edges()`	Draw network with removed edges in red
`draw_distributions()`	Draw distributions of expression data
`draw_enrich_go()`	Plot the enriched go terms of an enrich_go result
`draw_enrich_go_map()`	Draw GO enrichment map
`draw_expression_levels()`	Draw gene expression levels
`draw_glm()`	Plots the coefficients value of a Poisson generalized linear model
`draw_heatmap()`	Draw expression heatmap
`draw_network()`	Interactive network view
`draw_network_degrees()`	Draw a network's degree distributions
`draw_pca_scree()`	draw_pca_scree
`draw_profiles()`	Draw expression profiles of a clustering
`draw_raw_pvalue_histogram()`	Draw raw pvalue histogram
`draw_specific_pca()`	draw_specific_pca
`draw_venn()`	Venn diagram
`compute_pca()`	compute_pca
`pca_plot_correlation()`	PCA plot correlation
`quick_pca()`	Quick PCA
Data
`regulators_per_organism`	List of regulator genes for a number of model organisms
`abiotic_stresses`	Combined abiotic stresses
`gene_annotations`	Gene annotations for supported organisms in DIANE

Reference

DIANE User Interface

Pre-processing

Differential expression analysis

Expression based clustering

Gene ontologies and annotations

Gene regulatory network inference

Visualization functions

Data