Differences

This shows you the differences between two versions of the page.

--- differential_signaling_exercises [2016/02/25 10:38] – mhidalgo
+++ differential_signaling_exercises [2018/12/26 21:08] (current) – krian
@@ Line 14: / Line 14: @@
   * Experimental design: {{:GSE51835-Exercise_ED.txt|}}
-**1.1.-** Run the Differential signaling tool with these files, selecting the option //Color nodes by differential expression//. Take a llok to the results. Which pathways are differentially activated?
+**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?
-**1.3.-** Run the Differential signaling tool with these files again, selecting the option //Decompose paths//. Which diferences can you find between the two methods?
@@ Line 33: / Line 31: @@
 Run the Differential signaling tool with these files, selecting the options of both functional analysis //Gene ontology// and //Uniprot keywords//.
-Try to identify representative functions of this experiment (related to the disease). Are there significative functions? Are they related with the disease?
+**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?
@@ Line 42: / Line 42: @@
 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. You can select the option //Color nodes by differentail expression// if desired. Look at the results. If you have done Exercises 1 or 2, can you find any difference in the results of this dataset?
+**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 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?
-Stages describe the natural evolution of any cancer. You can find further information on the matter in
-[[http://www.cancer.gov/about-cancer/diagnosis-staging/staging/staging-fact-sheet]].
-Now we will try to understand how the Kidney cancer evolves by comparing the initial (I) with the final (IV) stages. We will use a subset of the former matrix, with a new experimental design, that you can download from the following links:
-**3.3.-** Run the Differential signaling tool with the former files comparing stage I versus stage IV. Compare the results with those given in Exercise 3.1. Are there any differences? What do they mean?
+**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?
-**3.4.-** Look for some paths which are related with disease but not with its progression.
 ==== Exercise 4 ====
@@ Line 71: / Line 63: @@
 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_b.txt|}}
+  * Expression matrix: {{:brca_genes_vals_ln.txt|}}
-  * Experimental design: {{:brca_normal-luminal_ed.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 merging the two datasets and run the resulting dataset in HiPathia.
+If you are familiar with R, you can compare both cancer subtypes by combining both datasets and run the resulting dataset in HiPathia.