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

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* [[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]]
* I generate PC score (PC1-PC3). The reason is that the three component contribute to largest variance and the slope in the scree plot occurs at component 3 as the picture below.
* I generate PC score (PC1-PC3). The reason is that the three component contribute to largest variance and the slope in the scree plot occurs at component 3 as the picture below.
[[File:age_pca_var.png]]
[[File:age_pca_var.png| 200px]]
  A=read.table("10%FDR_UCLA-GA_age_methylMatrix.txt",header=TRUE,row.names=1)
  A=read.table("10%FDR_UCLA-GA_age_methylMatrix.txt",header=TRUE,row.names=1)
  B=na.omit(A)
  B=na.omit(A)

Revision as of 17:37, 12 May 2012

Regression analysis of UCLA SZ data, applying LDA function

PCA analysis, plot the data in scatter plot

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