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]] | |||
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) | ||
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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$x) | |||
[[File:UCLA-GA_disease_t2.5_p.png| 800px]] | |||
A=read.table("UCLA-GA_sex_t2.5_methylMatrix.txt",header=TRUE,row.names=1) | |||
[[File:UCLA-GA_sex_t2.5_p.png| 800px]] | B=na.omit(A) | ||
B$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$rotation) | |||
pca1 <- prcomp(t(B), scale=TRUE) | |||
summary(pca1) | |||
Importance of components: | |||
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 | |||
Proportion of Variance 0.0958 0.0378 0.0309 0.0236 0.0231 0.0201 0.0168 0.0158 | |||
[[File:UCLA-GA_sex_t2.5_p.png| 800px]] | |||
* Continued on: http://genome-tech.ucsd.edu/LabNotes/index.php/Noi/NOTES/2012-4-17 |
Latest revision as of 20:43, 17 April 2012
- Link to calendar: [[1]]
Regression analysis of UCLA SZ data set[edit]
- Continued from http://genome-tech.ucsd.edu/LabNotes/index.php/Noi/NOTES/2012-4-1 and after getting comments from Dr. Zhang during lab meeting (2012_04_04)
- From the list of CpG sites from regression analysis. I performed PCA analysis again by adding the GA samples from Kang Zhang's lab as control (only the 73 good quality samples).
- Note: I will add more information about analysis and PCA analysis result after removing CpG sites with less confident.
A=read.table("UCLA-GA_age_t2.5_methylMatrix.txt", header=TRUE,row.names=1) B=na.omit(A) B$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