Make 'PICRUSt2' Output Analysis and Visualization Easier


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Documentation for package ‘ggpicrust2’ version 2.5.2

Help Pages

calculate_smart_text_size Smart Text Size Calculator
color_themes Color Theme System for ggpicrust2
compare_daa_results Compare the Consistency of Statistically Significant Features
compare_gsea_daa Compare GSEA and DAA results
compare_metagenome_results Compare Metagenome Results
create_gradient_colors Create Gradient Colors
create_legend_theme Create Enhanced Legend Theme
create_pathway_class_theme Create Pathway Class Annotation Theme
daa_annotated_results_df Differentially Abundant Analysis Results with Annotation
daa_results_df DAA Results Dataset
format_pvalue_smart Smart P-value Formatting
get_available_themes Get Available Color Themes
get_color_theme Get Color Theme
get_significance_colors Get Significance Colors
get_significance_stars Get Significance Stars
ggpicrust2 This function integrates pathway name/description annotations, ten of the most advanced differential abundance (DA) methods, and visualization of DA results.
ggpicrust2_extended Integrated analysis with ggpicrust2 including GSEA
gsea_pathway_annotation Annotate GSEA results with pathway information
import_MicrobiomeAnalyst_daa_results Import Differential Abundance Analysis (DAA) results from MicrobiomeAnalyst
kegg_abundance KEGG Abundance Dataset
ko2kegg_abundance Convert KO abundance in picrust2 export files to KEGG pathway abundance
ko_abundance KO Abundance Dataset
legend_annotation_utils Legend and Annotation Utilities for ggpicrust2
metacyc_abundance MetaCyc Abundance Dataset
metadata Metadata for ggpicrust2 Demonstration
pathway_annotation Pathway information annotation
pathway_errorbar The function pathway_errorbar() is used to visualize the results of functional pathway differential abundance analysis as error bar plots.
pathway_errorbar_table Generate Abundance Statistics Table for Pathway Analysis
pathway_gsea Gene Set Enrichment Analysis for PICRUSt2 output
pathway_heatmap Create pathway heatmap with support for multiple grouping variables
pathway_pca Perform Principal Component Analysis (PCA) on functional pathway abundance data
prepare_gene_sets Prepare gene sets for GSEA
preview_color_theme Preview Color Theme
resolve_annotation_overlaps Detect and Resolve Annotation Overlaps
safe_extract Safely Extract Elements from a List
smart_color_selection Smart Color Selection
visualize_gsea Visualize GSEA results