Dahlquist:Notebook/Microarray Data Analysis/2008/10/14: Difference between revisions
From OpenWetWare
Jump to navigationJump to search
Line 72: | Line 72: | ||
** 157 Genes Called Significant (Cutoff Q Value 0.1) | ** 157 Genes Called Significant (Cutoff Q Value 0.1) | ||
** Saved total list of genes as "GeneList_20081014_dCIN5-only" | ** Saved total list of genes as "GeneList_20081014_dCIN5-only" | ||
** Saved Q-Plot as " | ** Saved Q-Plot as "QPlot_20081014_dCIN5-only" | ||
** Saved Histograms as " | ** 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" | |||
<!-- ##### DO NOT edit below this line unless you know what you are doing. ##### --> | <!-- ##### DO NOT edit below this line unless you know what you are doing. ##### --> |
Revision as of 13:19, 14 October 2008
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> </html>Next entry<html><img src="/images/5/5c/Resultset_next.png" border="0" /></html> |
Today's WorkflowFirst compared dCIN5 and wt data following the same procedure as outlined on 10/02/2008:
savePlot(filename = "PvalHistogram_wt-vs-dCIN5", type = c("png"), device = dev.cur())
Then dCIN5 dataset was ran on its own:
Then wildtype dataset was ran on its own:
|