To identify a relevant network, we will use the STRING database to find a network relevant to the list of up-regulated genes. With the filter active, select and copy all entries in the Gene Name column.But in this case, all genes with a fold change greater than 2 already meet that cutoff. Next, one would normally filter out non-significant changes by filtering on the p-value as well, for example setting p-value less than 0.05.In the drop-down for the fold change column, set a filter for fold change greater than 2.Select the row containing data value headers (row 4) and select Data → Filter.We are going to define a set of up-regulated genes from the full dataset by filtering for fold change and p-value. You will have to scroll to the right to see the second column. Make sure to change the format for the second column, Gene Name, to Text. The file has 4 columns of data: Gene ID, Gene Name, fold change and p-value. In the third step, you can select the Data Format for every column.In the import wizard, select Delimited and in the next step select Tab.Next, go to Data → Get External Data → Import Text File. To open the tsv datafile in Excel, first launch Excel and open a blank workbook.Download the data: Transcriptomic analysis of autistic brain reveals convergent molecular pathology.The study has been published in Voineagu et al., and we will get a summarized dataset with fold change and p-value from the EBI Gene Expression Atlas. Install the stringApp from the Cytoscape App Store, or via Apps → App Store → Show App Store.įor this tutorial, we will use a dataset comparing transcriptomic differences between autistic and normal brain.
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