Dahlquist:Notebook/Microarray Data Analysis/2008/10/14: Difference between revisions

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** No significant genes under these settings.
** No significant genes under these settings.
** Choose Q-Value cutoff as 1, recalculate
** Choose Q-Value cutoff as 1, recalculate
*** Saved total list of genes as: "20081014_wt_dCIN5_comparison_results_allgenes" in "Data analysis 2008-10-02"
*** Saved total list of genes as: "GeneList_20081014_wt-vs-dCIN5"
** To save the plots, do the following command in the R console window.
** 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())
  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"
* 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_20081014_wt-vs-dCIN5"
** Saved Q-Plot as "QPlot_20081014_wt-vs-dCIN5"
** Saved Histograms as "PvalHistogram_20081014_wt-vs-dCIN5




Revision as of 12:44, 14 October 2008

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Microarray Data Analysis <html><img src="/images/9/94/Report.png" border="0" /></html> Main project page
<html><img src="/images/c/c3/Resultset_previous.png" border="0" /></html>Previous entry<html>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</html>Next entry<html><img src="/images/5/5c/Resultset_next.png" border="0" /></html>

Today's Workflow

  • First compared dCIN5 and wt data following the same procedure as outlined on 10/02/2008:
    • 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.
  • Results: (Saved in 2008-10-14 Results)
    • No significant genes under these settings.
    • Choose Q-Value cutoff as 1, recalculate
      • Saved total list of genes as: "GeneList_20081014_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_20081014_wt-vs-dCIN5"
    • Saved Histograms as "PvalHistogram_20081014_wt-vs-dCIN5



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