Dahlquist:Notebook/Microarray Data Analysis/2008/10/21

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 * style="background-color: #EEE"|[[Image:owwnotebook_icon.png|128px]] Microarray Data Analysis
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 * style="background-color: #F2F2F2" align="center"|  |Main project page


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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: savePlot(filename = "PvalHistogram_wt-vs-dCIN5", type = c("png"), device = dev.cur)
 * 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.
 * 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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