Ns126:Calendar/NOTES/2015-6-26: Difference between revisions

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Similarly, we can get the shared high correlated HDRs in cancer samples [[2015-7-10-002-code]]
Similarly, we can get the shared high correlated HDRs in cancer samples [[2015-7-10-002-code]]


Here, I want to show that these 2421 regions were no specially selected in 11 cancers. Suppose, these 2421 regions were derived from a same population, then the frequency of each regions occurred in 11 samples would have binomial distribution. therefore, we test each regions one by one and no any one of the regions significantly deviate from the theoretical [[distribution]]. In this analysis, P=0.40
Here, I want to show that these 2421 regions were no specially selected in 11 cancers. Suppose, these 2421 regions were derived from a same population, then the frequency of each regions occurred in 11 samples would have binomial distribution. therefore, we test each regions one by one and no any one of the regions significantly deviate from the theoretical [[distribution]]. In this analysis, P=0.399


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Revision as of 18:03, 14 August 2015

Methylation Block in HM450 microarray

Motivation

Dr.Zhang has defined methylation haplotype and methylation blocks based on GWBS data. and we are sure that these regions must have special biological, genomic or evolution functions. Therefore, I want to do the similar analysis in HM450K array. I will compare the regions identified in HM450 with the regions disovered by Dr. Zhang. In our hypothesis, the regions in my analysis would be a sutset of the regions from Dr. Zhang.

Materials and Method

I collected pair-wised samples of 11 cancers from TCGA project.

Method

HDR: High CpG density region. A intuitive method adopted from Takai and Jones's sliding-window algorithm were applied to identify the HDR region within methylation 450K microarray as the following steps.

Set a 100-base window in the beginning of genomic position of the array and shift the window to the right when there are least 2 probes in the windows. Calculate the total probes in extent regions until the last window does not meet the criteria. All the regions in which at least 4 probes were collected and be defined as high CpG density regions and calculate the average Pearson correlation among all the probes in cancer and normal samples, respectively. Please check the code.

Materials and Dataset

TCGA Dataset

PBMC dataset

Download the data with the R code

  • GSE53045:Epigenome analysis of smoking in peripheral blood mononuclear cells (PBMC) samples:50 smokers and 61 non-smokers.
  • GSE32148:DNA methylation in peripheral blood (PBMC) from individuals with 17 Crohns' disease, 11 ulcerative colitis and 20 normal controls
  • GSE35069:Differential DNA Methylation in Purified Human Blood Cells (CD4+,CD8+,CD19+)
  • GSE36054:Methylation Profiling of Blood DNA from Healthy Children
  • GSE36064:Methylation Profiling of Blood DNA from 78 Healthy Children (peripheral blood leukocytes from healthy children, Harvard Age Study)
  • GSE41169:Blood DNA methylation profiles in a Dutch population (62 schizophrenia and 33 normal )
  • GSE44132:Antenatal Blood DNA Methylation Profiles (Depression or Bipolar Disorder)
  • GSE42861:Differential DNA methylation in the PBMC from 354 Rheumatoid arthritis and 337 normal

iPS and ES dataset

Download the data with the R code

  • GSE54115: Aberrant DNA methylation reprogramming during iPS cell generation is dependent on the choice of reprogramming factors
  • GSE56851: Increased Risk of Genetic and Epigenetic Instability in Human Embryonic Stem Cells Associated with Specific Culture Conditions
  • GSE54769: Tissue- and Aging-specific DNA-Methylation Patterns are erased in Mesenchymal Stromal Cells derived from Induced Pluripotent Stem Cells.
  • GSE61461: Coding mutations and loss-of-imprinting in human pluripotent cells derived by nuclear transfer and defined factors

Result

High density CpG regions in 450K array

For different cancer dataset, the number of the high density CpG region (HDR) were slightly different because some probes might totally or majority missed in the dataset and these probes were removed and then to find the HDR. From the HDR analysis, you could find that there is not significant difference for the number of the HDR in different cancer dataset. the number of the HDR in all the 11 dataset ranged from 4858 to 5290. The average length of the HDR is 171bp (IQR=117bp) and the average distance between neighbor CpG site is about 27bp(IQR=13bp). the average number of the CpG site in the HDR is 6 (range from 5 to 50).


Correlation of high density CpG regions in 450K array

  • It is very interesting result: 1) cancer have two peak 2) cancer have more higher correction regions

Comparing the correlation of HDR, we could find the similarity of the correlation between cancer and normal were interrupted. the cancer tissues had more high correlated HDRs than the normal tissues. What's more, obviously, there is only one peak in the density plot of correlations in the normal tissues while there is an extra peaks in the cancer tissues.


Based on the Figure 3, I think, the region whose average correlation is large than 0.5 (cut-off) can be defined as methylation block. However, the performance of cut-off of 0.4, 0.5, 0.6 were analysis in the following section. The result can be found in the below figure. Although the number of the conservative regions were quite different (left), the relative proportion of the conservative regions were almost same (right). I don't know how to set a unique threshold to define methylation block, In additional, there is no such unique threshold to define LD in population genetics, therefore, I think an arbitrary cut-off should be set here.

As the obove figure showed, different cut-off of the definition of methythylation block were almost similar when average correlation is larger than 0.4, 2015-7-10-001-code

Similarly, we can get the shared high correlated HDRs in cancer samples 2015-7-10-002-code

Here, I want to show that these 2421 regions were no specially selected in 11 cancers. Suppose, these 2421 regions were derived from a same population, then the frequency of each regions occurred in 11 samples would have binomial distribution. therefore, we test each regions one by one and no any one of the regions significantly deviate from the theoretical distribution. In this analysis, P=0.399

' Observe Expectation
1 602 968.4
2 402 387.36
3 255 154.944
4 215 61.9776
5 140 24.79104
6 126 9.916416
7 138 3.9665664
8 95 1.5866266
9 198 0.6346506
10 100 0.2538602
11 150 0.1015441

Methylation Haplotype based on PBMC dataset


  • Shared High HDRC regions by CD4+ T cells, CD8+ T cells, CD56+ NK cells, CD19+ B cells, CD14+ monocytes, neutrophils, and eosinophils cells were attached in File:GSE35069.Share.High.HDRC.0.6.bed.txt. Binomial test showed there is no any region was over-preferred, indicating these regions were conservative in different elements of the whole blood cells. In this analysis, P=0.34, in another way, the probability for each region in each sample was 0.34.

Methylation Haplotype based on iPS and ES dataset



  • Conclusion: Obviously, 1) cancer obtain some extra high correlated methylation blocks compared with normal. 2) PBMC shared large number of methylation block with normal tissues compared with cancer samples.