diff --git a/docs/faq.rst b/docs/faq.rst
index 1187892..6af13c5 100644
--- a/docs/faq.rst
+++ b/docs/faq.rst
@@ -30,6 +30,17 @@ When observed ES is not within the null ESs, you'll get 0s. if you don't want 0,
 - set the smallest pvalue to 1 / ( number of permutations)
 - increase the permutation number (but more running time needed)
 
+**Q:** What are ``gene %``, and ``tag %`` mean in the output?
+-----------------------------------------------------------------------
+
+.. math::
+
+    \text{gene \\%} = \text{(the position of the gene coresponding to enrichment score peak)} \div \text{(number of all genes in ranking list)}
+
+
+.. math::
+
+    \text{tag \\%}  = \text{( number of leading genes)} \div \text{(number of genes in certain pathway that overalpped with the input ranking list)}
 
 
 
diff --git a/docs/index.rst b/docs/index.rst
index 490fbc5..7710941 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -62,7 +62,7 @@ Installation
 GSEApy is a Python/Rust implementation of **GSEA** and wrapper for **Enrichr**. 
 --------------------------------------------------------------------------------------------
 
-GSEApy has six subcommands: ``gsea``, ``prerank``, ``ssgsea``, ``replot`` ``enrichr``, ``biomart``.
+GSEApy has multiple subcommands: ``gsea``, ``prerank``, ``ssgsea``, ``gsva``, ``replot`` ``enrichr``, ``biomart``.
 
 1. The ``gsea`` module produces **GSEA** results.    
 The input requries a txt file(FPKM, Expected Counts, TPM, et.al), a cls file, and gene_sets file in gmt format. 
@@ -73,13 +73,15 @@ The input expects a pre-ranked gene list dataset with correlation values, which
 3. The ``ssgsea`` module performs **single sample GSEA(ssGSEA)** analysis.  
 The input expects a gene list with expression values(same with ``.rnk`` file, and gene_sets file in gmt format. ssGSEA enrichment score for the gene set as described by `D. Barbie et al 2009 <http://www.nature.com/nature/journal/v462/n7269/abs/nature08460.html>`_.
 
-4. The ``replot`` module reproduces GSEA desktop version results.  
+4. The ``gsva`` module performs **GSVA** analysis, which described by `Hänzelmann et al <https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-7>`_.
+
+5. The ``replot`` module reproduces GSEA desktop version results.  
 The only input for GSEAPY is the location to GSEA Desktop output results.
 
-5. The ``enrichr`` module enables you to perform gene set enrichment analysis using ``Enrichr`` API.
+6. The ``enrichr`` module enables you to perform gene set enrichment analysis using ``Enrichr`` API.
 Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr . It runs very fast and generates results in txt format.
 
-6. The ``biomart`` module helps you convert gene ids using BioMart API.
+7. The ``biomart`` module helps you convert gene ids using BioMart API.
 
 
 GSEApy could be used for **RNA-seq, ChIP-seq, Microarry** data. It's used for convenient GO enrichments and produce **publishable quality figures** in python.