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--- workvafin2 [2020/12/09 13:33] – [Example 1: Diabetes study] mestebanm
+++ workvafin2 [2020/12/30 21:14] (current) – [Results and interpretation] mestebanm
@@ Line 34: / Line 34: @@
 When the analysis is finished at Hipathia web, the report/results will be available in “My studies” section once the study is done.\\  You can download any table or image showed in the report page by clicking on the name right before it.\\ You can also download the circuit activity values and function matrices by clicking on //Circuit values//, //GO terms values// and //Uniprot keywords values// respectively.
-As we can see in the figure below, the results after simulation the KO of gene RAP1GAP over the 54 non diabetic samples only show deregulation on the pathways where the gene RAP1GAP can be found. Since RAP1GAP is an inhibitor of the Rap1 pathway, mostly all the functions associated to the circuits that compose the pathway are UP-regulated. The functional consequences of the KO of RAP1GAP vary from UP-regulation of Inflammatory processes, Cell adhesion, Stress response among others. The results shown seem to indicate that RAP1GAP plays an important role in the control of these processes.
+As we can see in the figure below, the results after the simulating the KO of gene RAP1GAP over the 54 non diabetic samples only show deregulation on the pathways where the gene RAP1GAP can be found. Since RAP1GAP is an inhibitor of the Rap1 pathway, mostly all the functions associated to the circuits that compose the pathway are UP-regulated. The functional consequences of the KO of RAP1GAP vary from UP-regulation of Inflammatory processes, Cell adhesion and Stress response among others. The results shown seem to indicate that RAP1GAP plays an important role in the control of these processes highly related to diabetes. Therefore, loss of function mutations in those genes inactivating RAP1 protein would most likely result in an over-activation of inflammatory response and thus, an increased risk of developing immune-mediated diabetes ([[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6908734/|Peña-Chilet et al., 2019]]).
 {{ ::rap1_signaling_pathway_hsa04015_1_.svg.png?nolink |}}
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 **10-** Press the //Run analysis// button. A study will be created and listed in the studies panel. You can access this panel by clicking on the //My studies// button.
-===== Example 3: Diabetes study =====
-**1-** Log in into HiPathia. For further information on this step visit [[logging_in|Logging in]].
-**2-** We will work with expression data from custom tissue related to Diabetes. You can download the raw expression matrix used for this example from GEO repository, accession number [[https://www.ncbi.nlm.nih.gov/pubmed/22768844|GSE38642]] You can read the original study here: https://www.ncbi.nlm.nih.gov/pubmed/22768844
-Hipathia only takes as input **normalized data**, so you will have to correctly normalize the raw expression matrix to be uploaded.
-  * Custom tissue expression matrix after normalization: {{:onlyCexpresetDiabetes.txt|Diabetes expression matrix}}
-**3-** Upload the data to HiPathia in the data panel by clicking on //My data//. For further information on this step visit [[upload_your_data|Upload your data]].
-**4-** We will evaluate the impact of disrupting variants in each gene **independently** of these gene lists. Each gene list correspond to all the genes that compose a single circuit from related and non related pathways to the disease, circuits  are named after their effector node:
-  * {{ :listagenesKO_diabetesRap1.txt | Gene list for example 1: Rap1 signaling pathway: PRKCI PARD6A PARD3}}
-  * {{ :listagenesKO_diabetesChemokine.txt | Gene list for example 2: Chemokynes signaling pathway: PARD3-PRKCZ-TIAM1}}
-  * {{ :listagenesKO_diabetesTNF.txt | Gene list for example 3: TNF signaling pathway:CREB3}}
-After choosing one or more from the studied example and prepare a list of gene(s) in txt format as described [[gene_list_file_format|here]]. We need to upload it the same way we did to upload expression data. Both gene names and entrez ids are accepted. Select Gene list as data type.
-**5-** Press the //Variant Interpreter// button.
-{{ :variantinterbtn.png |}}
-**6-** In the //Input data panel//. Press the //File browser// and select the desired file for gene list and custom tissue expression file (OPTIONAL).
-{{ :uploadgenelist.png |}}
-**7-** (OPTIONAL) Select a subset of signaling pathways to evaluate, all pathways are selected by default.
-{{ :pathwayslist.png |}}
-**8-** In the //Study information// panel, press the //File browser// button and select the desired output folder. In this case we will use //Diabetes_KO_analysis//. Give a name to the study, for example "Diabetes KO analysis".
-**9-** Press the //Run analysis// button. A study will be created and listed in the studies panel. You can access this panel by clicking on the //My studies// button.