Today's Workflow
The results generated on 10/14/2008 were downloaded and placed on the Desktop in "Edge Analysis" in Kevin's profile. Significant gene results were saved as tab-delimited files and the Pvalue Histograms and QPlots were saved into a powerpoint and printed.
- Only the wt-only results should be used, the other results are useless, see below for explanation.
Previous run (10/14/2008) on dCIN5-only dataset gave interesting results. While the wt-only dataset produced about 1000 significant genes, the dCIN5-only one gave about 150 significant genes. To verify this result:
- First the covariates and genelist files were uploaded to lion share. They will be opened with excel and checked for errors.
- IMPORTANT: It was found that the flask numbers were wrong for covariates files for dCIN5-only and wt-vs-dCIN5. They were changed and new runs were completed.
- The new files were saved on the desktop in the Edge Analysis folder as:
- dCIN5-only_Edge_covariates_20081021.txt and
- wt-vs-dCIN5_Edge_covariates_20081021.txt
Reran the dCIN5-vs-wt data with the updated covariate file:
- Gene file in Desktop "Data analysis 2008-10-02", Covariate file on Desktop
- Used gene file "wt-dCIN5_consolidated_Edge_genes-indexonly_20080715.txt"
- Used covariate file "wt-dCIN5_consolidated_Edge_covariates_20081021.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"
- 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 9 minutes.
- Results: (Saved in 2008-10-14 Results)
- 2 significant genes under these settings.(ID 1068 and 1798) with Q Value Cutoff of 0.1
- Choose show all
- Saved total list of genes as: "GeneList_20081021_wt-vs-dCIN5"
- To save the plots, do the following command in the R console window.
savePlot(filename = "PvalHistogram_wt-vs-dCIN5", type = c("png"), device = dev.cur())
- This will save the active plot window under a file name you choose. Saves in folder "edge_1.1.290"
- Saved Q-Plot as "QPlot_20081021_wt-vs-dCIN5"
- Saved Histograms as "PvalHistogram_20081021_wt-vs-dCIN5
Then dCIN5 dataset was ran on its own:
- Gene file in "Edge_data_20080710" and covariate file on Desktop
- Used gene file "dCIN5-only_Edge_genes-indexonly_20080715.txt"
- Used covariate file "dCIN5-only_Edge_covariates_20081021.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: 1.32%
- Percent of arrays missing data: 90%
- Overall percent of missing data: 0.09%
- 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"
- Class Variable is: None (within class differential expression)
- 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 2 minutes.
- Results: (Saved on Desktop)
- 1000 Genes Called Significant (Cutoff Q Value 0.0114)!!!!
- Saved total list of genes as "GeneList_20081021_dCIN5-only"
- Saved Q-Plot as "QPlot_20081021_dCIN5-only"
- Saved Histograms as "PvalHistogram_20081021_dCIN5-only"
All Q-Plots and Pvalue Histograms were combined into a powerpoint. All significant gene lists were exported and saved into text files in Edge Analysis on the Desktop in Dahlquist's Lab.
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Microarray Data Analysis
