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 probability (soft thresholding) or minimum difference (hard thresholding) if the threshold for merging is passed, then merge the two CpG positions.
# 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.
===  Probability approach #1:===
#Compare a window with it's non-overlapping upper and lower neighbors using Chi-square statistics & requiring a minimum 0.2 difference.
=== Brute force #1:===
# We define a threshold for where the borders are going to be set. If there is not point above or below the threshold, we merge the windows together.
=== 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
  1. Based on previous high-coverage data, we see that LMR sites are clustered together. This means that we can and should perform smoothing.
  2. 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.
  3. 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]

  1. Start with the first measured CpG position in the genome
  2. Try to merge this to the next position if they are <= 2kb apart.
  3. 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.
  4. 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.
  5. After the merging decisions have been made, create merged windows & label.
  6. Merge unlabeled CpGs to the left and right of each windows if they are within 250bp & are > 2kb from the next nearest window
  7. Merge two similarly labeled windows together if they are within 2kb of each other.

Annotation[edit]

  1. Label a window FMR if <=0.2 distance from 1 and LMR if >0.2 from 1.