Noi/NOTES/2012-4-6: Difference between revisions

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* 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).
* 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.
* 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| 800px]]


[[File:UCLA-GA_age_t2.5_p.png| 800px]]
  A=read.table("UCLA-GA_disease_t2.5_methylMatrix.txt",header=TRUE,row.names=1)
  A=read.table("UCLA-GA_disease_t2.5_methylMatrix.txt",header=TRUE,row.names=1)
  B=na.omit(A)
  B=na.omit(A)
Line 24: Line 42:
                           PC1    PC2    PC3    PC4    PC5    PC6    PC7    PC8
                           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
  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| 800px]]  
  [[File:UCLA-GA_disease_t2.5_p.png| 800px]]  
[[File:UCLA-GA_sex_t2.5_p.png| 800px]]
[[File:UCLA-GA_sex_t2.5_p.png| 800px]]

Revision as of 19:02, 17 April 2012

  • Link to calendar: [[1]]

Regression analysis of UCLA SZ data set

  • 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 

File:UCLA-GA sex t2.5 p.png