Dinh/Dinh 2012/NOTES/2012-3-21
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Sequence dependent test on HAPMAP data[edit]
- Working directory (genome-miner): /home/nplongth/Noi_scratch/ASM_HAPMAP1362-1454_2012_02_03/DD_SequenceDependentTest
- Discussed this with Noi and Dr. Zhang
Sequence dependent means that there is a consistent preference of methylation on one allele versus another across multiple individuals.
- Binomial test
- Procedure for the binomial test:
1. Using all data from *cpg.txt file generated from Robert's pipeline (ignoring p-value) 2. Ignore data where the two alleles have exactly the same methylation fraction (no preference) 3. Identify the favored allele in each individual, and increment count for the favored allele at a SNP:CpG set. 4. Do not consider sites where there are more than 2 different favored alleles 5. Let x = the count for the least favored allele 6. Use Math::CDF qw(pbinom) with x, n = number of data, p = probability of preference on allele = 0.5 Null hypothesis is that the sample prefer allele 1 and allele 2 both with probability 0.5 Reject null if the p-value is low (more samples prefer one allele over the other) 7. Accept null if p value is greater than 0.05 (5%) 8. Resulted in 117 SNP:CPG (Sequence dependent ASM) / Tested 71,441 SNP:CPG
IMPORTANT UPDATE: As set up before, the binomial test would return p-value = 1 for when all the sites have preference toward 1 allele. Added in line: $p_value = 0 if($p_value == 1); Now the analysis for the Binomial test needs to be repeated. 8. Resulted in 45,022 SNP:CPG (Sequence dependent ASM) / Tested 71,441 SNP:CPG
- Command:
dinh@genome-miner:/home/nplongth/Noi_scratch/ASM_HAPMAP1362-1454_2012_02_03$ DD_SequenceDependentTest/asmBinomialTest.pl MFASM.QValues-cpg.list.txt > DD_SequenceDependentTest/binomialtest.SequenceDependent
- Noi suggested the T-test
- Procedure for the t-test:
1. Using all data from *cpg.txt file generated from Robert's pipeline (ignoring p-value) 2. Get the difference in methylation level between the two alleles, making sure that the difference is always relative to the same allele. 3. Require minimum 5 data points 4. Calculate the t-statistic: (mean difference)/SEM (use Statistics::Descriptive) Null hypothesis is that the average of the differences is 0. Reject null if the p-value is low (the average of the differences is not 0) 5. Calculate the p-value using Math::CDF qw(pt) with t-stat, df = number of data - 1, and centered at 0. 6. Accept null if p value is greater than 0.05 (5%) 7. Resulted in 1244 SNP:CPG (Sequence dependent ASM) / Tested 17,525 SNP:CPG
- Command:
dinh@genome-miner:/home/nplongth/Noi_scratch/ASM_HAPMAP1362-1454_2012_02_03$ DD_SequenceDependentTest/asmTTest.pl MFASM.QValues-cpg.list.txt > DD_SequenceDependentTest/ttest.SequenceDependent
Gene ontology: GREAT[edit]
- Used awk and sort -u commands to get BED files of unique CpG sites with sequence dependent ASM.
binomialTest.cpgs = 113 (background have 64,030 CpGs) tTest.cpgs = 1187 (background have 16,430 CpGs)
- changed chr23/24/25 to chrX/Y/M with sed
- Uploaded BED files to GREAT (default settings) and checked biological process.
- Binomial test GO (NOTE: after repeating with bigger 45,022 SNP:CPG set, there are no significant enrichment!
- TTest GO
File:TtestGO.png
Discussion[edit]
- Binomial test is too stringent, higher number of data is required for counting while we cannot use data with difference = 0. The t-test allows us to use data with difference = 0.
- Ttest is more powerful but have higher false discovery (type I error plus bias when there are outliers)
- Binomial test seems to have lower type I error but also lower power.
- A larger sample size will perform better with the binomial test.
- A larger sample size will allow more sites to pass minimum 5 data points for the t-test.
Double check Binomial test with OpenOffice Math[edit]
10:127584276:127584265 | 3:2796414:2796339 | 17:21220301:21220322 |
0.271|0.591 | 0.786|0.417 | 0.865|0.000 |
0.228|0.629 | 0.562|0.468 | 0.733|0.000 |
0.296|0.439 | 0.906|0.825 | 0.816|0.027 |
NA | 0.850|0.040 | 0.455|0.000 |
0.267|0.613 | 0.632|0.340 | 0.600|0.000 |
0.427|0.513 | 0.644|0.246 | 0.125|0.146 |
0.178|0.534 | 0.661|0.769 | 0.688|0.000 |
0.436|0.548 | 0.475|0.452 | 0.784|0.013 |
0.302|0.706 | NA | NA |
0.145|0.681 | 0.532|0.529 | 1.000|0.074 |
0.411|0.403 | NA | 0.194|0.000 |
0.214|0.695 | 0.842|0.577 | 0.827|0.012 |
p=0.005859375 | p=0.0107421875 | p=0.005859375 |
- First and last SNP:CPG have n=11 and k=1 (n is number of data and k is the least favored allele count)
- Middle SNP:CPG have n=10 and k=1
' | n | 11 | 10 |
k | 1 | 1 | |
P(k=0) | 0 | 0.0004882813 | 0.0009765625 |
P(k=1) | 1 | 0.0053710938 | 0.009765625 |
SUM | --- | 0.005859375 | 0.0107421875 |
- probabilities were calculated using: =(FACT(n)/(FACT(k)*FACT(n-k)))*0.5^k*(0.5)^(n-k)
Double check Ttest with OpenOffice Math[edit]
4:7319795:7319781 | 14:95874397:95874396 | 13:38445196:38445195 |
NA | NA | 0.000|0.714 |
0.000|0.303 | 0.000|0.952 | 0.000|0.700 |
NA | NA | NA |
0.750|0.975 | 0.009|0.981 | 0.000|0.933 |
NA | 0.005|0.950 | 0.048|0.867 |
0.292|0.851 | NA | 0.000|0.970 |
NA | 0.000|0.947 | 0.000|0.361 |
NA | NA | NA |
NA | NA | NA |
0.718|0.930 | NA | NA |
0.571|1.000 | 0.000|0.977 | NA |
NA | NA | NA |
p=0.039493696 | p=1.71390437762721e-07 | p=0.009553314 |
- Calculate the differences then t-statistics and pvalue
' | 4:7319795:7319781 | 14:95874397:95874396 | 13:38445196:38445195 |
diff | 0.303 | 0.952 | 0.714 |
diff | 0.225 | 0.972 | 0.7 |
diff | 0.559 | 0.945 | 0.933 |
diff | 0.212 | 0.947 | 0.819 |
diff | 0.429 | 0.977 | 0.97 |
diff | 0.361 | ||
mean | 0.3456 | 0.9586 | 0.7495 |
SEM | 0.1472270356 | 0.0148425065 | 0.2198933833 |
T-stat | 2.3473949515 | 64.584778741 | 3.4084699999 |
P-value | 0.0393695698 | 1.72149891022281E-007 | 0.0095374811 |