Kevin's Edge Analysis from 10/2/2008
- Login with your Keck lab username to mason (the names of the machines are on the lower-left corner of the login screens).
- Right-click on the green tabula rasa.
- Choose Terminal.
- At this point, the R prompt shows up. Type:
- The Edge GUI should now appear.
- Create two tab-delimited text files for "genes" and "covariates".
- Files in Desktop "Data analysis 2008-10-02"
- Used gene file "wt-dCIN5_consolidated_Edge_genes-indexonly_20080715.txt"
- Used covariate file "wt-dCIN5_consolidated_Edge_covariates_20080710.txt
- Load both into an Edge session.
- Select "Impute Missing Data" from the menu. Calculate Percent Missing Data by clicking on the button. The results are:
- Percent of genes missing data: 7.63%
- Percent of arrays missing data: 95.35%
- Overall percent of missing data: 3.15%
- For KNN Parameters, set:
- Percent of missing values to tolerate in a gene: 100 (so all genes included)
- Number of nearest neighbors to use (maximum of 15): 15
- clicked GO to impute missing data.
- Selected "Identify Differentially Expressed Genes"
- Note: this is to compare between the wt and dCIN5 strains. Different parameters and gene/covariate files will need to be used to analyze individual strains.
- Class Variable is: Strain
- Differential Expression Type is: Time Course
- Number of null iterations, set to 1000
- Choose a seed for reproducible results, set to 47
- Choose Time Course Settings
- Covariate giving time points is: Timepoint
- Covariate corresponding to individuals is: Flask
- Choose spline type, accepted default of Natural Cubic Spline, dimension 4
- Click "Apply" and then click "Go"
- 1000 permutations looks like it will take about 10 minutes.
- Save results as:
- Choose Q-Value cutoff as 1, recalculate
- Saved total list of genes as: "20081002_wt_dCIN5_comparison_results_genelist" in "Data analysis 2008-10-02"
- Can cluster significant genes, did not do