Athurva Gore: Difference between revisions

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**See if any other combinations of factors produce better fits in R.
**See if any other combinations of factors produce better fits in R.
**Implement algorithm to select probes in Perl.
**Implement algorithm to select probes in Perl.
*'''R Analysis of Cosmic Data Set'''
**Tried taking the log of all factors aside from Tm, checked fit.
**Fit is still not very good
***Very low R^2 value
***Checked by plotting in Octave; fit does not match up to data well at all.
**Perhaps a different, non-linear, non-logarithmic model is needed?
**This would makes sense, as several of the data pieces seem to resemble a bell-curve shape...
***Intuitively, this also makes sense; if a factor is too low or too high, performance is decreased.

Revision as of 19:59, 28 August 2008

Lab Rotation

  • Will add daily entries once schedule settles down a bit.

August 4th - August 10th

  • Received CpG island probe code from Dr. Zhang.
  • Need to make modifications discussed in meeting:
  • Necessary modifications:
    • Remove bisulfite conversion function - DONE
      • Create dummy function that simply does not do a replacement, but creates fwd and rev strands
      • Commented code to find/replace
      • Question: parts we are looking at; do they have N's in them? Probably shouldn't.
    • Remove CG score penalty - DONE
      • in function getProbeList
      • remove section starting with "my $CG_penalty"
      • set CG_penalty as always zero for quick fix, remove CG finding code later
    • Add function to check sequence uniqueness in human genome (build hash table ahead of time, check Dr. Zhang's functions)
      • Looks like function oligoFreq does this; currently just uses log of each side
      • Modify probe score calculation to favor segment B being more unique
      • See function getProbeList
    • Modify score calculation so that Tm of segment A should be high and Tm of segment B should be low
      • At the moment, Tm score is used in p_score calculation
      • Simply modify to adjust so that A has high Tm, B has low Tm
      • See function getProbeList
      • Specific thresholds?
    • Exon database
      • Ask about our current UCSC data; it comes in format on PGP server, or is this modified?
      • Found Exon-Intron Database; uses GenBank
      • Show Dr. Zhang the readme

August 11th - August 17th

  • Will copy from notebook

August 18th - August 24th

  • Will copy from Notebook

August 27th

  • Have code to read and analyze Cosmic set data; file is "readCosmic.pl"
  • Comparison of Cosmic set data to Dr. Li's data (Harvard Collaborator)
    • Dr. Li's data showed that the last base pair of the ligation arm was important.
    • Average expression was calculated using the "makeHist.pl" file.
      • This file averages the abundance of the probes that used each base.
    • The results were slightly different.
      • For the COSMIC set, it was found that for the proximal ligation arm, G was preferred, followed by C, A, and T.
      • This makes intuitive sense, as the bond between G and C is stronger than the bond between A and T.
      • The results were also much less pronounced than Dr. Li's results; the difference in base performance is much smaller.
Cosmic Set Results
Base: A T G C
Proximal Extension Arm: 98.2276826484018 105.4814504701790 97.8751164168995 105.1120477176380
Distal Extension Arm: 104.9745183977170 107.4126226040130 106.5526921141540 88.8525735059634
Proximal Ligation Arm: 95.1374247734938 83.7531848897751 135.360909000299 107.876484056988
Distal Ligation Arm: 103.1547820103830 113.0664396095970 96.5690024678530 96.7110820814283
  • File:Proximal Ligation Base.png
    • Analyze these results later; perhaps add in a preference on the Proximal Ligation Arm to favor G or C over A or T in the score.
  • Implementation of new probe set algorithm.
    • Algorithm is summarized above.
    • Implement in Perl using hash table in getCpGIslands.pl file.
  • Use R to analyze Cosmic Data Set
    • Downloaded and installed GNU R and R-commander (GUI)
      • Can load CSV and tab-separated files.
    • lm command can do a linear model fit to the data.
      • May need to take the log of several pieces of data however in order to improve fit (if data is exponentially correlated).
      • Initial lm results for Cosmic set show a very poor correlation...probably need to take log of several pieces of data set.
    • Earlier, Dr. Zhang's analysis on CES22K set used the following factors:
      • ProbeDist: distribution of oligo abundance determined by Solexa sequencing. - Don't have this for the Cosmic set
      • H1_Tm, H2_Tm: H1 (ligation arm), H2 (extension arm), 12bp sub-sequences of H1 and H2. - Normalized for the Cosmic set; very close to 62 C in all cases
      • H1R_dist, H2L_dist: K-mer distribution of H1&H2 12bp sub-sequences in the human genome. - Have this
      • Core_25bp_genomic_homology: weighted score of the 25bp homologous sequences in the human genome, mapped by SOAP with <=3bp mismatches. - Not sure what this is...ask Dr. Zhang

August 28th

  • TO DO TODAY:
    • Take log of various factors in R, see if this affects lm results.
    • See if any other combinations of factors produce better fits in R.
    • Implement algorithm to select probes in Perl.
  • R Analysis of Cosmic Data Set
    • Tried taking the log of all factors aside from Tm, checked fit.
    • Fit is still not very good
      • Very low R^2 value
      • Checked by plotting in Octave; fit does not match up to data well at all.
    • Perhaps a different, non-linear, non-logarithmic model is needed?
    • This would makes sense, as several of the data pieces seem to resemble a bell-curve shape...
      • Intuitively, this also makes sense; if a factor is too low or too high, performance is decreased.