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Separate transcripts by type (app: Numeric Evaluation of a Data Column)

Description: This App identifies rows in a tab-delimited file where a specified column has a value that fits a specific criterion. Used to make separate lists of transcripts that have a fold-change value greater than or less than 1. Documentation: https://pods.iplantcollaborative.org/wiki/display/DEapps/Numeric%20Evaluation%20of%20a%20Data%20Column.

  1. Log into the Discovery Environment: https://de.iplantcollaborative.org/de/.
  2. Open the Numeric Evaluation of a Data Column app (Public Applications > General Utilities > Text and Tabular Data > Numeric Evaluation of a Data Column).
    1. Change 'Analysis Name' to Collect_Up-regulated_Transcripts, add a 'Description' (optional), and use the default 'output folder'.
  3. Click on the Select input data tab.
    1. Under the 'Select an input file' field enter the DESeq_results_significant.txt file from the previous section (Determine differential expression) (Sample data: Community Data > iplant_training > rna-seq_without_genome > Q_separate_transcripts_by_type).
  4. Click on the Options tab.
    1. Select the 'Indicate the test column (c1 = column 1)' field. Set the column to c6.
    2. Select the 'Enter either another column or a number' field. Enter 1.
    3. Select the 'Select a comparison operator' field. Choose "Greater than".
    4. Under the 'Lines of header text' field. Enter 1.
  5. Click on "Launch Analysis".
  6. Repeat steps 3-8, but set the comparison operator to “Less than” to determine which transcripts were down-regulated, changing the 'Analysis Name' to Collect_Down-regulated_Transcripts.
  7. Click on 'Analyses' from the DE workspace and monitor the 'Status' of the analysis (e.g., Idle, Submitted, Pending, Running, Completed, Failed).
    1. Once launched, an analysis will continue whether the user remains logged in or not.
    2. Email notifications update on the analysis progress; they can be switched off under 'Preferences'.
    3. If the analysis fails or does not proceed in the anticipated timeline, check these tips for troubleshooting. (Using the sample data, the analysis should be complete in less than 5 minutes.)
    4. To re-run an analysis, click the analysis "App" in the 'Analyses' window.
  8. Access analysis results in one of two ways:
    1. In the 'Analyses' window click on the analysis "Name" to open the output folder.
    2. In the 'Data' window, click on user name, then navigate to the folder that holds the output of the analysis. (Find the output for the sample at Community Data > iplant_training > rna-seq_without_genome > Q_separate_transcripts_by_type > output_from_sample_data.)
  9. The output file, numeric_filter_out.txt, will be similar to DESeq_results_significant.txt from the previous section (Determine differential expression), but include only the lines corresponding to transcripts that DESeq determined were significantly up-regulated or down-regulated.

Note: Rename the output files for the next section in order to differentiate them.

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