Kun:LabNotes/CpgSeq/2011-9-27: Difference between revisions
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This implies that doing subtraction within the families could be effective to identify disease-specific methylation sites/patterns. | This implies that doing subtraction within the families could be effective to identify disease-specific methylation sites/patterns. | ||
[[Image:UCLA_RO_SZ_94_blood_samples_BSPP_Hierarchical_clustering.png]] | [[Image:UCLA_RO_SZ_94_blood_samples_BSPP_Hierarchical_clustering.png]] | ||
===PCA analysis=== | |||
A=read.table("UCLA_RO_BSPP_20110921_methylMatrix_minSTD_0.1.txt",header=TRUE,row.names=1) | |||
B=na.omit(A) | |||
B$STDEV=NULL | |||
B$min_RD=NULL | |||
B$mean_RD=NULL | |||
pca1 <- prcomp(t(B), scale=TRUE) | |||
plot(pca1$x, type="n") | |||
text(pca1$x, rownames(pca1$x), cex=0.7) | |||
There are clearly two groups separated by PC2. Why is that? Is that due to data quality, gender, age, disease or other factors? | |||
[[Image:UCLA_RO_SZ_94_blood_samples_BSPP_PCA_plot.png]] |
Revision as of 19:24, 28 September 2011
Analysis of the BSPP data from UCLA RO 96 blood samples
Mid-parent offspring (MPO) analysis
- The purpose of this analysis is to quickly identify the CpG sites that exhibit heritable patterns on DNA methylation.
- I first combined all the BED files Noi generated with allBED2Matrix.pl. I filtered out sites that has less than 40 valid methylation values or STD<0.1.
- I then wrote a script for MPO analysis: mpo.pl, which yielded 2921 sites that could potentially be heritable. Note that the criteria are relatively loose. I only require r>=0.2 and chi-square >1.
- Then I wrote a second script to extract the methylation values for individual CpG sites, so that I can carefully examine how well does MPO work.extract_trios_methyl_values.pl.
- I picked a few sites that have the highest r values, and drew the scatter plots with MS-Excel. It seems to me that the correlation is modest at best even with these sites. Also when I looked up these sites on UCSC genome browser, I found that these sites are all CpG-SNPs. This sort of validated the MPO analysis. However, to identify heritable CpG sites that are not on SNPs, we will have to filter out the SNP sites prior to MPO analysis.
- After filtering out CpG-SNPs based on snp130, the list was reduced to 351. Two examples are shown below.
File:Chr17 4559596 MPO.png File:Chr1 145016206 MPO.png
- I also plotted all the 351 sites along the chromosomes. There are 46 clusters of CpG sites (>1 sites in a <2kb window).
File:Heritable Cpg sites ideogram.png
Hierarchical clustering
A=read.table("UCLA_RO_BSPP_20110921_methylMatrix_minSTD_0.1.txt",header=TRUE,row.names=1) B=na.omit(A) B$STDEV=NULL B$min_RD=NULL B$mean_RD=NULL B.cor=cor(B,use="pairwise.complete.obs",method="pearson") B.d = as.dist(1-B.cor) B.h = hclust(B.d,"complete") plot(B.h)
A quick observation is that most data sets cluster based on the genetic relatedness, not the disease status. This implies that doing subtraction within the families could be effective to identify disease-specific methylation sites/patterns. File:UCLA RO SZ 94 blood samples BSPP Hierarchical clustering.png
PCA analysis
A=read.table("UCLA_RO_BSPP_20110921_methylMatrix_minSTD_0.1.txt",header=TRUE,row.names=1) B=na.omit(A) B$STDEV=NULL B$min_RD=NULL B$mean_RD=NULL pca1 <- prcomp(t(B), scale=TRUE) plot(pca1$x, type="n") text(pca1$x, rownames(pca1$x), cex=0.7) There are clearly two groups separated by PC2. Why is that? Is that due to data quality, gender, age, disease or other factors? File:UCLA RO SZ 94 blood samples BSPP PCA plot.png