hiPathia

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====== Differential signaling exercises ======

In this page you will find different exercises for further practising in the use of the HiPathia Differentail Signaling tool. Try to answer the questions using the results given by HiPathia for each case.

To see how to use the application please visit the [[Differential signaling|documentation of the differential signaling tool]] or the [[Worked example Differential example|Differential Signaling worked example]].

==== Exercise 1 ====

We will analyze a dataset with samples of healthy people (22-30 years old) before and after performing moderate exercise. This dataset has been download from the GEO database. Further information on the study can be found at [[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GDE51835|this site]].

You can download the expression matrix and the experimental design from the following links:

  * Expression matrix: {{:gse51835-exercise_genes_vals.txt|}} 
  * Experimental design: {{:GSE51835-Exercise_ED.txt|}}

**1.1.-** Run the Differential signaling tool with these files. Take a look to the results. Which pathways are differentially activated?

**1.2.-** What is the role of the differentially expressed genes in the pathways?



==== Exercise 2 ====

For his exercise we will use a dataset of the Crohn's disease. This dataset has been download from the GEO database. Further information on the study can be found at [[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GDE36807]].

You can download the expression matrix and the experimental design from the following links:

  * Expression matrix: {{:GSE36807-Crohn_genes_vals.txt|}}
  * Experimental design: {{:GSE36807-Crohn_ED.txt|}}

Run the Differential signaling tool with these files, selecting the options of both functional analysis //Gene ontology// and //Uniprot keywords//.

**2.1.-** Try to identify representative functions of this experiment (related to the disease). Are there significative functions? Are they related with the disease?

**2.2.-** Look at the Heatmaps provided by the tool for the different analyses. Do you think that a predictor could be trained from these data? With which matrix do you think that it would work better? 


==== Exercise 3 ====

We will work with a kidney cancer dataset which has been download from The Cancer Genome Atlas database (TCGA). Tumor samples belong to Kidney Clear Cell Renal Cell Carcinoma (KIRC), the most common kind of kidney cancer. Healthy samples correspond to healthy kidney tissue from the same pacients. 

You can download the expression matrix and the experimental design from the following links:

  * Expression matrix: {{:kirc_demo_genes_vals.txt|}}
  * Experimental design: {{:kirc_demo_ED_class.txt|}}

**3.1.-** Run the Differential signaling tool with these files, selecting also the functional analyses. Find representative paths and functions of this disease.

**3.2.-** Look at the results. If you have done Exercises 1 or 2, can you find any difference in the results of this dataset?


==== Exercise 4 ====

We will work with a Breast Cancer dataset from the repository The Cancer Genome Atlas. 
There are different subtypes of Breast Cancer. In this exercise we will compare two kinds of breast cancer subtypes.

First we will analyze the basal subtype, also called triple negative, with healthy breast tissue.
You can download the normalized expression matrix and the experimental design from these links:

  * Expression matrix: {{:brca_genes_vals_bn.txt|}}
  * Experimental design: {{:brca_normal-basal_ed.txt|}}

Then we will analyze the luminal subtype. This subtype is divided in two classes, A and B, but we will not take into account this division. You can download the normalized expression matrix and the experimental design from these links:

  * Expression matrix: {{:brca_genes_vals_ln.txt|}}
  * Experimental design: {{:brca_luminal-normal_ed.txt|}}

Identify paths and functions which are significant for each type of breast cancer. 
Try to find differences between these two subtypes of breast cancer.

If you are familiar with R, you can compare both cancer subtypes by combining both datasets and run the resulting dataset in HiPathia.