Dinh/Dinh 2013/NOTES/2013-1-16: Difference between revisions
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# Try to merge this to the next position if they are <= 2kb apart. | # Try to merge this to the next position if they are <= 2kb apart. | ||
# If either one of the CpGs have fewer than 10 reads, then expand the first one left and the second right ("borrows" information from sites nearby that are <=250bp away) until the minimum read depth is achieved. | # If either one of the CpGs have fewer than 10 reads, then expand the first one left and the second right ("borrows" information from sites nearby that are <=250bp away) until the minimum read depth is achieved. | ||
# Compare the methylation levels of the two using either | # Compare the methylation levels of the two using either Chi-Square test (soft thresholding) or minimum difference (hard thresholding) if the threshold for merging is passed, then merge the two CpG positions. | ||
# After the merging decisions have been made, create merged windows & label. | # After the merging decisions have been made, create merged windows & label. | ||
# Merge unlabeled CpGs to the left and right of each windows if they are within 250bp & are > 2kb from the next nearest window | # Merge unlabeled CpGs to the left and right of each windows if they are within 250bp & are > 2kb from the next nearest window | ||
# Merge two similarly labeled windows together if they are within 2kb of each other. | # Merge two similarly labeled windows together if they are within 2kb of each other. | ||
=== Annotation === | === Annotation === | ||
# Label a window FMR if <=0.2 distance from 1 and LMR if >0.2 from 1. | # Label a window FMR if <=0.2 distance from 1 and LMR if >0.2 from 1. |
Latest revision as of 01:48, 19 January 2013
N37 - Genome segmentation[edit]
- Since the previous models did not work well for genome segmentation, we should re-do our assumptions
- Based on previous high-coverage data, we see that LMR sites are clustered together. This means that we can and should perform smoothing.
- Our samples are from tissues and not cell lines, the measurements that we get are averages from a population of largely heterogenous cells. This may mean that we can only consider a two states model, where one is fully methylated and the other is low to unmethylated.
- This mean that we should use any valley/"peak" finding method to find LMR: loess, poisson model, two-states hidden markov models, etc.
Binning with a sliding window approach[edit]
- Start with the first measured CpG position in the genome
- Try to merge this to the next position if they are <= 2kb apart.
- If either one of the CpGs have fewer than 10 reads, then expand the first one left and the second right ("borrows" information from sites nearby that are <=250bp away) until the minimum read depth is achieved.
- Compare the methylation levels of the two using either Chi-Square test (soft thresholding) or minimum difference (hard thresholding) if the threshold for merging is passed, then merge the two CpG positions.
- After the merging decisions have been made, create merged windows & label.
- Merge unlabeled CpGs to the left and right of each windows if they are within 250bp & are > 2kb from the next nearest window
- Merge two similarly labeled windows together if they are within 2kb of each other.
Annotation[edit]
- Label a window FMR if <=0.2 distance from 1 and LMR if >0.2 from 1.