Noi/NOTES/2012-6-13

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Regression analysis of UCLA SZ data (additional different age cutoff of grouping of data on LDA)[edit]

  • Suggestions from lab meeting on 2012.05.16
    • LDA: randomly choose samples for training and (have UCLA-SZ and UCSD-GA on training and testing data sets)
    • LDA: Change age cutoff from 40 to different number (50, 60 or 70) and see how accurate the samples can be classified at different age cutoff.
    • PCA: I also performed PCA and changed the color of each sample in scatter plot based on the age cutoff.
  • Training and testing data set
    • list 1-96 =>UCSD-SZ, 97-169 => UCSD-GA
    • trainAge <- c(1:40, 91:132)
    • testAge <- c(41:90, 133:169)

Regression analysis on age : LDA and PCA[edit]

Scripts:
(Note: used the same template, but change color code based on age cutoff and labeling)
LDA:   lda_age-40_10FDR.txt 
PCA:   plot_pca_age-40_10FDR.txt,  plot_pca_age-40_label-age_10FDR.txt
Color code:  color.code_age_cutoff-40-70.txt
PC scores (PC1-3) and labeling:  Age-40_PCscores_Labeling.txt,  Age-50_PCscores_Labeling.txt,  Age-60_PCscores_Labeling.txt,  Age-70_PCscores_Labeling.txt

Tables of raw data

Age cutoff 40 (10% FDR) Predicted\Truth Adult Young adult Correct FALSE Total
Training Adult 54 2 77 5 82
Young adult 3 23
Testing Adult 59 1 85 2 87
Young adult 1 26
Cross validation Adult 112 3 161 8 169
Young adult 5 49


Age cutoff 50 (10% FDR) Predicted\Truth Adult Young adult Correct FALSE Total
Training Adult 51 4 76 6 82
Young adult 2 25
Testing Adult 58 0 85 2 87
Young adult 2 27
Cross validation Adult 110 4 162 7 169
Young adult 3 52


Age cutoff 60 (10% FDR) Predicted\Truth Adult Young adult Correct FALSE Total
Training Adult 32 7 72 10 82
Young adult 3 40
Testing Adult 40 13 71 16 87
Young adult 3 31
Cross validation Adult 72 22 141 28 169


Age cutoff 70 (10% FDR) Predicted\Truth Adult Young adult Correct FALSE Total
Training Adult 14 4 70 12 82
Young adult 8 56
Testing Adult 16 8 68 19 87
Young adult 11 52
Cross validation Adult 30 12 138 31 169
Young adult 19 108


Table of accuracy and misclassification rate

Age cutoff Training (Accuracy of allocation) Misclassicication rate Testing (quality of model) Misclassicication rate Cross validation Misclassicication rate
40 77 of 82 6.10% 85 of 87 2.30% 161 of 169 4.73%
50 76 of 82 7.32% 85 of 87 2.30% 162 of 169 4.14%
60 72 of 82 12.20% 71 of 87 18.39% 141 of 169 16.57%
70 70 of 82 14.63% 68 of 87 21.84% 138 of 169 18.34%


PCA plots[edit]

Note: For the PCA plot labeled with age numbers. The number after dot(.) like 22.1 or 22.2 represent the duplication of sampleIDs with the same age.

 Age cutoff: 40 
File:UCLA-GA age-cutoff40 10FDR pcaplot1.png  File:UCLA-GA age-cutoff40 10FDR pcaplot2.png
Additional plots:
 UCLA-GA_age-cutoff40_10FDR_pca-label-age_plot1
 UCLA-GA_age-cutoff40_10FDR_pca-label-age_plot2
 UCLA-GA_age-cutoff40_10FDR_pcaplot1
 UCLA-GA_age-cutoff40_10FDR_pcaplot2
 Age cutoff: 50 
File:UCLA-GA age-cutoff50 10FDR pcaplot1.png  File:UCLA-GA age-cutoff50 10FDR pcaplot2.png
Additional plots:
 UCLA-GA_age-cutoff50_10FDR_pca-label-age_plot1
 UCLA-GA_age-cutoff50_10FDR_pca-label-age_plot2
 UCLA-GA_age-cutoff50_10FDR_pcaplot1
 UCLA-GA_age-cutoff50_10FDR_pcaplot2
 Age cutoff: 60 
File:UCLA-GA age-cutoff60 10FDR pcaplot1.png  File:UCLA-GA age-cutoff60 10FDR pcaplot2.png
Additional plots:
 UCLA-GA_age-cutoff60_10FDR_pca-label-age_plot1
 UCLA-GA_age-cutoff60_10FDR_pca-label-age_plot2
 UCLA-GA_age-cutoff60_10FDR_pcaplot1
 UCLA-GA_age-cutoff60_10FDR_pcaplot2
 Age cutoff: 70 
File:UCLA-GA age-cutoff70 10FDR pcaplot1.png  File:UCLA-GA age-cutoff70 10FDR pcaplot2.png
Additional plots:
 UCLA-GA_age-cutoff70_10FDR_pca-label-age_plot1
 UCLA-GA_age-cutoff70_10FDR_pca-label-age_plot2
 UCLA-GA_age-cutoff70_10FDR_pcaplot1
 UCLA-GA_age-cutoff70_10FDR_pcaplot2
The distribution and fraction of UCLA-SZ and UCSD-GA' AGE
A<-read.table("UCLA-SZ_UCSD-GA.age.txt", sep=",")
age<-as.numeric(A)
length(age)
ageFact<-factor(age)
levels(ageFact)
summary(ageFact)
summary(ageFact)
Age        19 21 22 23 24 25 26 27 28 29 30 31 32 33 35 36 37 39 40 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 
number      1  3  3  4  4  2  3  6  5  2  4  3  2  1  4  2  1  1  1  2  2  2  3  4  3  6  3  4  5  2  3  1  4  3  2  4  5  2  2  4    
   
Age        70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86
number      2  5  2  3  3  1  2  3  2  3  5  7  5  2  3  1  2

Summary