Dahlquist:Notebook/Microarray Data Analysis/2008/10/14

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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

Then dCIN5 dataset was ran on its own:

  • Files in "Edge_data_20080710"
    • Used gene file "dCIN5-only_Edge_genes-indexonly_20080715.txt"
    • Used covariate file "dCIN5-only_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: 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 in 2008-10-14 Results)
    • 157 Genes Called Significant (Cutoff Q Value 0.1)
    • Saved total list of genes as "GeneList_20081014_dCIN5-only"
    • Saved Q-Plot as "QPlot_20081014_dCIN5-only"
    • Saved Histograms as "PvalHistogram_20081014_dCIN5-only"

Then wildtype dataset was ran on its own:

  • Files in "Edge_data_20080710"
    • Used gene file "wt-only_Edge_genes-indexonly_20080715.txt"
    • Used covariate file "wt-only_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: 6.79%
    • Percent of arrays missing data: 91.3%
    • Overall percent of missing data: 2.5%
  • 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 in 2008-10-14 Results)
    • 1000 Genes Called Significant (Cutoff Q Value 0.0326)
    • Saved total list of genes as "GeneList_20081014_wt-only"
    • Saved Q-Plot as "QPlot_20081014_wt-only"
    • Saved Histograms as "PvalHistogram_20081014_wt-only"

Just in case we want the data, the total data set was ran without strain classification:

  • 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"
    • 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 9 minutes.
  • Results: (Saved in 2008-10-14 Results)
    • 998 Genes Called Significant (Cutoff Q Value 0.00475)
    • Saved total list of genes as "GeneList_20081014_wt-and-dCIN5-together"
    • Saved Q-Plot as "QPlot_20081014_wt-and-dCIN5-together"
    • Saved Histograms as "PvalHistogram_20081014_wt-and-dCIN5-together"

--Kevin C. Entzminger 16:54, 14 October 2008 (EDT)


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