Generalized Reporter Score-Based Enrichment Analysis for Omics Data
Test the proper clusters k for c_means
Combine the results of 'step by step GRSA'
Custom modulelist from a specific organism
Build a custom modulelist
Export report score result tables
Transfer gene symbol table to KO table
get features in a modulelist
Calculate reporter score
The GOlist used for enrichment.
The KOs abundance table and group table.
Perform enrichment analysis
Perform fisher's exact enrichment analysis
Perform gene set analysis
Perform gene set enrichment analysis
Perform Gene Set Variation Analysis
Perform Pathway Analysis with Down-weighting of Overlapping Genes (PAD...
Perform Significance Analysis of Function and Expression
Perform Simultaneous Enrichment Analysis
Differential analysis or Correlation analysis for KO-abundance table
Load the CARDinfo (from CARD database)
Load the GOlist (from 'GO' database)
Load the specific table (from 'KEGG')
Modify the pathway description before plotting
Pipe operator
Plot enrich_res
Plot features boxplot
plot the Z-score of features distribution
Plot features heatmap
Plot features trend in one pathway or module
Plot features network
Plot htable levels
plot_KEGG_map
Plot the reporter_res as circle_packing
Plot the reporter_res
Plot the significance of pathway
Plot c_means result
Print reporter_score
Print rs_by_cm
Transfer p-value of KOs to Z-score
One step to get the reporter score of your KO abundance table.
ReporterScore: Generalized Reporter Score-Based Enrichment Analysis fo...
Reporter score analysis after C-means clustering
Upgrade the KO level
update CARDinfo from (from 'CARD' database)
Update the GO2gene files (from 'GO' database)
Update files from 'KEGG'
Inspired by the classic 'RSA', we developed the improved 'Generalized Reporter Score-based Analysis (GRSA)' method, implemented in the R package 'ReporterScore', along with comprehensive visualization methods and pathway databases. 'GRSA' is a threshold-free method that works well with all types of biomedical features, such as genes, chemical compounds, and microbial species. Importantly, the 'GRSA' supports multi-group and longitudinal experimental designs, because of the included multi-group-compatible statistical methods.
Useful links