Noi/NOTES/2012-5-12: Difference between revisions

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=== PCA analysis, plot the data in scatter plot ===
=== PCA analysis, plot the data in scatter plot ===
* [[Media:plot_age_10FDR-2.txt| plot_age_10FDR-2.R]], [[Media:plot_sex_10FDR-2.txt| plot_sex_10FDR-2.R]], [[Media:plot_status_p0.015-2.txt| plot_status_p0.015-2.R]]
* [[Media:plot_age_10FDR-2.txt| plot_age_10FDR-2.R]], [[Media:plot_sex_10FDR-2.txt| plot_sex_10FDR-2.R]], [[Media:plot_status_p0.015-2.txt| plot_status_p0.015-2.R]]
[[File:UCLA-GA_age_10FDR_pcaplot1.png]] [[File:UCLA-GA_age_10FDR_pcaplot2.png]]
[[File:UCLA-GA_age_10FDR_pcaplot1.png| 600px]] [[File:UCLA-GA_age_10FDR_pcaplot2.png| 600px]]  [[File:UCLA-GA_sex_10FDR_pcaplot1.png]]  [[File:UCLA-GA_sex_10FDR_pcaplot2.png]]  [[File:UCLA-GA_status_pval0.015_pcaplot1.png]]  [[File:UCLA-GA_status_pval0.015_pcaplot2.png]]
* I generate PC score (PC1-PC3). The reason is that the three components seem to contribute to largest variance and the slope change in the scree plot occurs at component 3 (age)  as the picture below. Actually, I should try to analyze with different number of component and see if the classification change or not. Note the plot of status seem to be different from age because the slope change occur at component4 or 5.  
* I generate PC score (PC1-PC3). The reason is that the three components seem to contribute to largest variance and the slope change in the scree plot occurs at component 3 (age)  as the picture below. Actually, I should try to analyze with different number of component and see if the classification change or not. Note the plot of status seem to be different from age because the slope change occur at component4 or 5.  
[[File:age_pca_var.png| 200px]]
[[File:age_pca_var.png| 200px]]

Revision as of 04:10, 13 May 2012

Regression analysis of UCLA SZ data, applying LDA function

PCA analysis, plot the data in scatter plot

File:UCLA-GA age 10FDR pcaplot1.png File:UCLA-GA age 10FDR pcaplot2.png File:UCLA-GA sex 10FDR pcaplot1.png File:UCLA-GA sex 10FDR pcaplot2.png File:UCLA-GA status pval0.015 pcaplot1.png File:UCLA-GA status pval0.015 pcaplot2.png

  • I generate PC score (PC1-PC3). The reason is that the three components seem to contribute to largest variance and the slope change in the scree plot occurs at component 3 (age) as the picture below. Actually, I should try to analyze with different number of component and see if the classification change or not. Note the plot of status seem to be different from age because the slope change occur at component4 or 5.

File:Age pca var.png

A=read.table("10%FDR_UCLA-GA_age_methylMatrix.txt",header=TRUE,row.names=1)
B=na.omit(A)
B$STDEV=NULL
B$min_RD=NULL
B$mean_RD=NULL
pca1 <- prcomp(t(B), scale=TRUE)
summary(pca1)
v<-data.frame(pca1$x[,1],pca1$x[,2],pca1$x[,3])
names(v)<-c("PC1","PC2","PC3")
  • Then add the labels (age, sex or disease status) to each sample in the last column and used this data as the input for LDA
          V1         V2         V3          V4
1  0.8641308  3.1103773 -1.2768520       adult
2 -7.3738694 -3.7825118  2.4297650       adult
3  6.3925767  0.5246122  0.9405213 young adult
4  6.0525615  2.0565664  4.4626373 young adult