Dahlquist:Notebook/Microarray Data Analysis/2008/10/21: Difference between revisions

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*** wt-vs-dCIN5_Edge_covariates_20081021.txt
*** wt-vs-dCIN5_Edge_covariates_20081021.txt


* Then for an additional test, the difference between dCIN5 and wt at an individual timepoint was tested:
''' Reran the dCIN5-vs-wt data with the updated covariate file:'''
** Files in Desktop "Data analysis 2008-10-02"
* 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 gene file "wt-dCIN5_consolidated_Edge_genes-indexonly_20080715.txt"
** Used covariate file "wt-dCIN5_consolidated_Edge_covariates_20080710.txt"
** Used covariate file "wt-dCIN5_consolidated_Edge_covariates_20081021.txt
* Load both into an Edge session.
* 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:
* Select "Impute Missing Data" from the menu.  Calculate Percent Missing Data by clicking on the button.  The results are:
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* Selected "Identify Differentially Expressed Genes"
* Selected "Identify Differentially Expressed Genes"
** Class Variable is: Strain
** Class Variable is: Strain
** Differential Expression Type is: Static (standard, non-time course sampling)
** Differential Expression Type is: Time Course
** Number of null iterations, set to 1000
** Number of null iterations, set to 1000
** Choose a seed for reproducible results, set to 47
** Choose a seed for reproducible results, set to 47
** click "Go"
** Choose Time Course Settings
** 1000 permutations looks like it will take about 1h 35min. Because it was taking so long and because it may not have produced the results we wanted, I aborted to do the analysis stated below.
** Covariate giving time points is: Timepoint
** This computation will identify genes with a significant difference in expression between wt and dCIN5 without respect to time. To determine the difference between individual timepoints, the genes-indexonly files will have to be changed to show only the timepoint of interest.  
** Covariate corresponding to individuals is: Flask
 
** Choose spline type, accepted default of Natural Cubic Spline, dimension 4
* Results: (Saved in )
** Click "Apply" and then click "Go"
** # significant genes under these settings.
** 1000 permutations looks like it will take about 9 minutes.
* Results: (Saved in 2008-10-14 Results)
** 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: ""
*** 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 ""
** Saved Q-Plot as "QPlot_20081014_wt-vs-dCIN5"
** Saved Histograms as "
** 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"




Revision as of 13:54, 21 October 2008

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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:

  • 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)
    • 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"



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