Noi/NOTES/2012-4-6

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  • Link to calendar: [[1]]

Regression analysis of UCLA SZ data set[edit]

  • Continued from http://genome-tech.ucsd.edu/LabNotes/index.php/Noi/NOTES/2012-4-1 and after getting comments from Dr. Zhang during lab meeting (2012_04_04)
  • From the list of CpG sites from regression analysis. I performed PCA analysis again by adding the GA samples from Kang Zhang's lab as control (only the 73 good quality samples).
  • Note: I will add more information about analysis and PCA analysis result after removing CpG sites with less confident.
A=read.table("UCLA-GA_age_t2.5_methylMatrix.txt", header=TRUE,row.names=1)
B=na.omit(A)
B$STDEV=NULL
B$min_RD=NULL
B$mean_RD=NULL
str(B)
'data.frame':	1120 obs. of  169 variables:
pca_sites <- prcomp(B, scale=TRUE)
summary(pca_sites)
Importance of components:
                         PC1    PC2    PC3     PC4     PC5     PC6     PC7
Proportion of Variance  0.672 0.0344 0.0117 0.00694 0.00625 0.00558 0.00461
write.table(file="age_pca_sites.orig.rotations", pca_sites$rotation) 
pca1 <- prcomp(t(B), scale=TRUE)
summary(pca1)
Importance of components:
                         PC1    PC2    PC3    PC4    PC5    PC6    PC7   PC8
Proportion of Variance  0.118 0.0535 0.0246 0.0198 0.0165 0.0153 0.0137 0.013
File:UCLA-GA age t2.5 p.png 
A=read.table("UCLA-GA_disease_t2.5_methylMatrix.txt",header=TRUE,row.names=1)
B=na.omit(A)
B$STDEV=NULL
B$min_RD=NULL
B$mean_RD=NULL
str(B)
'data.frame':	98 obs. of  169 variables:
pca_sites <- prcomp(B, scale=TRUE)
summary(pca_sites)
Importance of components:
                         PC1    PC2    PC3    PC4    PC5    PC6    PC7    PC8
Proportion of Variance  0.669 0.0290 0.0219 0.0183 0.0165 0.0133 0.0107 0.0097
write.table(file="disease_pca_sites.orig.rotations", pca_sites$rotation)
pca1 <- prcomp(t(B), scale=TRUE)
summary(pca1)
Importance of components:
                         PC1    PC2    PC3    PC4    PC5    PC6    PC7    PC8
Proportion of Variance 0.0722 0.0409 0.0384 0.0314 0.0302 0.0269 0.0246 0.0237
write.table(file="disease_pca_indiv.orig.x", pca1$x)
File:UCLA-GA disease t2.5 p.png 

A=read.table("UCLA-GA_sex_t2.5_methylMatrix.txt",header=TRUE,row.names=1)
B=na.omit(A)
B$STDEV=NULL
B$min_RD=NULL
B$mean_RD=NULL
str(B)
'data.frame':	887 obs. of  169 variables:
pca_sites <- prcomp(B, scale=TRUE)
summary(pca_sites)
Importance of components:
                         PC1    PC2    PC3    PC4    PC5     PC6     PC7
Proportion of Variance  0.631 0.0226 0.0185 0.0117 0.0111 0.00943 0.00844
write.table(file="sex_pca_sites.orig.rotations", pca_sites$rotation)
pca1 <- prcomp(t(B), scale=TRUE)
summary(pca1)
Importance of components:
                         PC1    PC2    PC3    PC4    PC5    PC6    PC7    PC8
Proportion of Variance 0.0958 0.0378 0.0309 0.0236 0.0231 0.0201 0.0168 0.0158
File:UCLA-GA sex t2.5 p.png