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

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* I repeated regression analysis again by changing some criteria to select the candidate significant CpG sites, based on FDR or with raw p-value cutoff. For regression on status, I will not apply multiple test correction since I didn't get any significant signals. However with minimum t statistic of 2.5 (p-value ~0.014) from the previous analysis I could separate out the normal and affected samples. Dr. Zhang suggested that this should be fine. In addition, I will change the color of the control samples in PCA analysis plots. LDA will be used then the number of samples in the two groups classified by the line will be counted.
* I repeated regression analysis again by changing some criteria to select the candidate significant CpG sites, based on FDR or with raw p-value cutoff. For regression on status, I will not apply multiple test correction since I didn't get any significant signals. However with minimum t statistic of 2.5 (p-value ~0.014) from the previous analysis I could separate out the normal and affected samples. Dr. Zhang suggested that this should be fine. In addition, I will change the color of the control samples in PCA analysis plots. LDA will be used then the number of samples in the two groups classified by the line will be counted.
=== 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| 600px]]  [[File:UCLA-GA_age_10FDR_pcaplot2.png| 600px]]  [[File:UCLA-GA_sex_10FDR_pcaplot1.png| 600px]]  [[File:UCLA-GA_sex_10FDR_pcaplot2.png| 600px]]  [[File:UCLA-GA_status_pval0.015_pcaplot1.png| 600px]]  [[File:UCLA-GA_status_pval0.015_pcaplot2.png| 600px]]
[[File:UCLA-GA_age_10FDR_pcaplot1.png| 600px]]  [[File:UCLA-GA_age_10FDR_pcaplot2.png| 600px]]  [[File:UCLA-GA_sex_10FDR_pcaplot1.png| 600px]]  [[File:UCLA-GA_sex_10FDR_pcaplot2.png| 600px]]  [[File:UCLA-GA_status_pval0.015_pcaplot1.png| 600px]]  [[File:UCLA-GA_status_pval0.015_pcaplot2.png| 600px]]
* 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.  
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  3  6.3925767  0.5246122  0.9405213 young adult
  3  6.3925767  0.5246122  0.9405213 young adult
  4  6.0525615  2.0565664  4.4626373 young adult
  4  6.0525615  2.0565664  4.4626373 young adult
* [[Media:lda_age_10FDR.txt| lda_age_10FDR.R]], [[Media:lda_sex_10FDR.txt| lda_sex_10FDR.R]], [[Media:lda_status_10FDR.txt| lda_status_10FDR.R]]
 
[[Media:lda_age_10FDR.txt| lda_age_10FDR.R]]  
[[Media:lda_sex_10FDR.txt| lda_sex_10FDR.R]]
[[Media:lda_status_10FDR.txt| lda_status_10FDR.R]]
  [[Media:plot_LD1_age_10FDR.pdf | plot_LD1_age_10FDR]]
  [[Media:plot_LD1_age_10FDR.pdf | plot_LD1_age_10FDR]]
  [[Media:plot_LD1_sex_10FDR.pdf | plot_LD1_sex_10FDR]]
  [[Media:plot_LD1_sex_10FDR.pdf | plot_LD1_sex_10FDR]]
  [[Media:plot_LD1_status_p.015.pdf | plot_LD1_status_p.015]]
  [[Media:plot_LD1_status_p.015.pdf | plot_LD1_status_p.015]]

Revision as of 04:01, 14 May 2012

Regression analysis of UCLA SZ data, applying LDA function

PCA analysis, plot the data in scatter plot

 plot_age_10FDR-2.R 
 plot_sex_10FDR-2.R
 plot_status_p0.015-2.R

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
 lda_age_10FDR.R 
 lda_sex_10FDR.R
 lda_status_10FDR.R
 plot_LD1_age_10FDR
 plot_LD1_sex_10FDR
 plot_LD1_status_p.015