Noi/NOTES/2012-5-12: Difference between revisions
Jump to navigation
Jump to search
>Noi No edit summary |
>Noi |
||
Line 23: | Line 23: | ||
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]] |
Revision as of 17:16, 12 May 2012
Regression analysis of UCLA SZ data, applying LDA function
- Links
- 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 status regression analysis, I will not apply multiple test correction since I didn't get any signal. However with minimum t statistic of 2.5 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
- plot_age_10FDR-2.R, plot_sex_10FDR-2.R, plot_status_p0.015-2.R
- I generate PCA score (PC1-PC3)
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