Update README.md

README.md

@@ -5,18 +5,10 @@
 
 > GAM-clustering provides metabolic variability within dataset using a novel network-based computational approach that utilizes cellular transcriptional profiles as proxies. The metabolic network of reactions from KEGG database is presented as a graph that has vertices corresponding to metabolites and the edges corresponding to the reactions with the expressed genes. In the graph the method tries to find a set of connected subgraphs, with each corresponding well to a certain gene expression pattern. Curret analysis reveals the major metabolic features associated with different subpopulations and highlights a number of metabolic modules that are specific to individual cell types, tissues of residence, or developmental stages.
 
-To explore data visit the following links:
-- [Gene expression heatmap](https://artyomovlab.wustl.edu/phantasus/?preloaded=ImmGen_total_Eduw0mei4)
-- [PCA with samples annotation](http://artyomovlab.wustl.edu/publications/supp_materials/Immgen/PCADatasetOverview.html)
-- [Heatmap of metabolic modules](https://artyomovlab.wustl.edu/phantasus/?session=x039baa087a35e7) 
-
-## Requirments
-- [R](https://www.r-project.org/)
-- [sgmwcs-solver](https://github.com/ctlab/sgmwcs-solver) 	
-- [KEGG mouse metabolic network](GAM) 
+To explore data visit the following [interactive browser](https://artyomovlab.wustl.edu/immgen-met/).
 
 ## Input data
-[Raw counts](Data/OSMNP_unnormalized_genes_count_10_3_18.count_table) are processed by [rawDataProcessing.R]() script and the output object `es.top12k` has the following structure:
+[Raw counts](Data/OSMNP_unnormalized_genes_count_10_3_18.count_table) are processed by [`rawDataProcessing.R`](rawDataProcessing.R) script and the output object `es.top12k` has the following structure:
 
 ``` r
 > load("Data/337_es.top12k.Rda")
@@ -33,15 +25,15 @@ To explore data visit the following links:
 ```
 
 ## Modules deriving
-The initial patterns are defined using k-means clustering on gene expression matrix and then are refined in an iterative process using the network connections ([modulesDeriving.R]()).
+The initial patterns are defined using k-means clustering on gene expression matrix and then are refined in an iterative process using the network connections ([`modulesDeriving.R`](modulesDeriving.R)).
 The final output presents a set of specific subnetworks (also called metabolic modules) that reflect metabolic variability within a given transcriptional dataset. 
 Each metabolic module is a piece of metabolic network whose gene expression has correlated expression pattern across all dataset. The following graph and heatmap represent network and constituting genes' expression for module 5, correspondingly:
 ![module5](/readmePics/github.pic.m5.png "network and gene expression heatmap for module 5")
 Averaged gene expression of all modules is represented at the following summary heatmap:
 ![centers](/readmePics/github.m.centers.png "centers heatmap")
 
 ## Modules annotation
-Functional annotation of obtained modules is based on KEGG and Reactome canonical pathways ([modulesAnnotation.R]()).
+Functional annotation of obtained modules is based on KEGG and Reactome canonical pathways ([`modulesAnnotation.R`](modulesAnnotation.R)).
 The following example is devoted to module 5 (k - number of module genes in a particular pathway, K - number of genen in a particular pathway):
 ``` r
 > paths <- data.table::fread("Data/m.5.pathways_mod.tsv")
@@ -50,4 +42,4 @@ The following example is devoted to module 5 (k - number of module genes in a pa
 ## 1:  R-MMU-191273 1.009604e-48 17  24 1.237774e-45 Cholesterol biosynthesis	Hmgcs1 Hmgcr Msmo1 Cyp51 Mvd ...
 ## 2: R-MMU-8957322 9.801101e-39 17  67 6.008075e-36   Metabolism of steroids	Hmgcs1 Hmgcr Msmo1 Cyp51 Mvd ...
 ## 3:  R-MMU-556833 1.406903e-27 18 395 5.749543e-25     Metabolism of lipids	Hmgcs1 Hmgcr Msmo1 Cyp51 Aacs ...
-```
+```