Dinh/Dinh 2014/NOTES/2014-1-23: Difference between revisions

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* This analysis promisingly shows that we can positively identify many of the cancer sample reads in blood and reject most reads from blood samples in blood. It is also able to do this using fewer than 1000 cancer sample reads.
* This analysis promisingly shows that we can positively identify many of the cancer sample reads in blood and reject most reads from blood samples in blood.  


* Since 2-3/267 blood reads (or 1.1%) were identified as non-blood, we need the level of cancer to be greater than >>1.1% to make a cancer call. We can also score the identified non-blood read by comparing them to cancer haplotypes to increase confidence.
* Next, I decreased the number of reads to <=200
{| class="wikitable" cellpadding=2
| align="center" style="background:#f0f0f0;"|'''MaxScoreFilter'''
| align="center" style="background:#f0f0f0;"|'''BLOOD'''
| align="center" style="background:#f0f0f0;"|'''BE2C'''
| align="center" style="background:#f0f0f0;"|'''U87MG'''
| align="center" style="background:#f0f0f0;"|'''BXPC3'''
| align="center" style="background:#f0f0f0;"|'''PANC1'''
| align="center" style="background:#f0f0f0;"|'''T98G'''
|-
| 0||3||12||12||15||14||15
|-
| 0.1||7||41||66||79||96||74
|-
| 0.15||7||47||73||88||106||88
|-
| 0.2||7||57||88||106||113||99
|-
| Total Reads Analyzed||172||180||172||175||168||175
|}
 
* Cancer sample reads were identified from less than 200 total cancer reads.  
 
* Since 1-4% of blood reads were identified as non-blood, we need the level of cancer to be greater than >>1.1% to make a cancer call. We can also score the identified non-blood read by comparing them to cancer haplotypes to increase confidence.


=== Calculate the r2 between pairs of CpGs ===
=== Calculate the r2 between pairs of CpGs ===
*
*

Revision as of 00:11, 25 January 2014

GP1 Haplotype Analysis

SAM record parser

  • In order to perform haplotype analysis, I needed to write a SAM record parser so that I can analyze each read and determine the CG positions and their methylation status. This works like samtools pileup but since the current version of samtools mpileup requires a bam file as input and a reference fasta file as input, it is probably more efficient to skip using samtools mpileup.
  • The SAM parser needs to identify the position of each read base, and the mismatch positions. Since there are INDELS and H/S clippings, it needs to integrate both the CIGAR and MD:Z fields.
  • Script for finding mismatch positions in SAM file: File:DdSAMparse.txt
  • Script for counting the CpG locus alleles (also makes BED): File:CgLocusAlleles.txt
  • To compare the parsing results, use sam parser to generate a BED format file for CpG positions & compare with results obtained from samtools pileup -> methylFreq -> BED
NP-GP1V6-Ind1-16-Dec26-Ind10_S10.BED.txt : cor = 0.9999383
NP-GP1V6-Ind1-16-Dec26-Ind11_S11.BED.txt : cor = 0.9999235
NP-GP1V6-Ind1-16-Dec26-Ind12_S12.BED.txt : cor = 0.9999451
NP-GP1V6-Ind1-16-Dec26-Ind13_S13.BED.txt : cor = 0.9999285
NP-GP1V6-Ind1-16-Dec26-Ind14_S14.BED.txt : cor = 0.9998338
NP-GP1V6-Ind1-16-Dec26-Ind15_S15.BED.txt : cor = 0.9999977
NP-GP1V6-Ind1-16-Dec26-Ind16_S16.BED.txt : cor = 0.9999913
NP-GP1V6-Ind1-16-Dec26-Ind9_S9.BED.txt   : cor = 0.9999657
  • There are some positions where in the SAM record parsing produced slightly different results from samtools pileup.
An example of non-concordance call:
samtools pileup: 
chr9	100610863	100610864	'9/19'	19	+	100610863	100610864	90,150,0
sam parser: 
chr9	100610863	100610864	'9/49'	49	+	100610863	100610864	30,210,0
  • Fixed an error with extractMethyl.pl, the Regex for indels can't be used to remove them.
 Regex for indels: \+[0-9]+[ACGTNacgtn]+' \-[0-9]+[ACGTNacgtn]+' 
 Example:
 ...TTTT.T.T..TTT-1GTT-1GT-1GTTTTT-1GTTTTT-1GT-1GTT-1GTTTTTTTTTTTTTT..T
                 ---------------------------------------------------
                 ***  *** ***     ***     *** ***  *** 
 The positions marked with dashes at the bottom were removed using the regex for indels.
 However, only the positions marked with stars should be removed.
  • Also, samtools pileup always convert 64 to 33 (even when the base quality is already in 33).
  • Re-generate BED files from extractMethyl & calculated the correlation again. They are improved slightly and even have perfect correlation for some data files.

Count locus alleles

  • Count the different alleles produced by four consecutive CpGs on the same read. There are 16 different possibilities.
  • Examples of the locus count:
chr1:1475064:1475086:1475088:1475095    2UMMM,1MMUM,28MMMM,     31
chr1:1475086:1475088:1475095:1475100    1MUMM,30MMMM,   31
chr1:1475095:1475100:1475108:1475130    32MMMM, 32
chr1:1475108:1475130:1475141:1475143    1MMUM,1MMMU,30MMMM,     32
chr1:1475130:1475141:1475143:1475162    1MMUM,1MMMU,29MMMM,     31
chr1:1475141:1475143:1475162:1475164    1MUMM,1MMUM,29MMMM,     31
  • Generated a locus count table for each sample. The locus count table contains the haplotype information for each sample. Consider only locus that are covered by at least 10 reads.
  • For any read, calculate the probability of seeing a particular allele at a locus in blood sample using the haplotype information for blood:
  • Example 1:
 M01186:73:000000000-A20K5:1:1101:15873:5723:1:N:0:3_AGCCTT:R
 chr4:172734347:172734362:172734393:172734395  UUMM  0 <--- This means that no blood sample read have this haplotype
 chr4:172734362:172734393:172734395:172734426  UMMM  0
 chr4:172734393:172734395:172734426:172734431  MMMU  0
 chr4:172734395:172734426:172734431:172734435  MMUM  0
 chr4:172734426:172734431:172734435:172734442  MUMU  0
 chr4:172734431:172734435:172734442:172734445  UMUM  0
 chr4:172734435:172734442:172734445:172734453  MUMM  0
 chr4:172734442:172734445:172734453:172734455  UMMM  0
 chr4:172734445:172734453:172734455:172734459	MMMM  0
 Average probability score is 0, meaning that this read have no chance of being originated from a blood sample. (This is a non-blood read).
  • Example 2:
 M01186:73:000000000-A20K5:1:2102:6845:17522:1:N:0:3_CCTTAG:F	  
 chr2:26407679:26407682:26407684:26407689, UUUU 0.966
 chr2:26407682:26407684:26407689:26407691, UUUU 0.967
 chr2:26407684:26407689:26407691:26407697, UUUU 0.975
 chr2:26407689:26407691:26407697:26407713, UUUU 0.975
 chr2:26407691:26407697:26407713:26407721, UUUU 0.979
 chr2:26407697:26407713:26407721:26407723, UUUU 0.982
 chr2:26407713:26407721:26407723:26407725, UUUU 0.980
 chr2:26407721:26407723:26407725:26407728, UUUU 0.979
 chr2:26407723:26407725:26407728:26407732, UUUU 0.971
 chr2:26407725:26407728:26407732:26407741, UUUU 0.966
 chr2:26407728:26407732:26407741:26407743, UUUU 0.968
 The average probability is high, meaning that his read might have also originated from a blood sample. (This cannot be distinguished from blood).
  • It is almost certain that 0 average scores are not from blood DNA, but it is harder to determine whether a 0.2 or even a 0.9 average score are not from blood. We may improve the confidence by increasing the haplotype size to include more than 4 consecutive CpG sites.
  • To test specificity, I randomly sampled 1/1000 of each cancer BAM files (for read 1 of V4 capture ONLY), and calculate the probability score of finding each read in a blood sample (Indx6). As a control, I sampled 1/1000 of another blood sample to compare (Indx8).
MaxScoreFilter BLOOD BE2C U87MG BXPC3 PANC1 T98G
0 0 45 44 108 52 71
0.1 2 171 201 457 202 200
0.15 3 194 225 515 215 221
0.2 3 224 246 552 243 254
Total Reads Analyzed 267 576 475 899 365 402
  • This analysis promisingly shows that we can positively identify many of the cancer sample reads in blood and reject most reads from blood samples in blood.
  • Next, I decreased the number of reads to <=200
MaxScoreFilter BLOOD BE2C U87MG BXPC3 PANC1 T98G
0 3 12 12 15 14 15
0.1 7 41 66 79 96 74
0.15 7 47 73 88 106 88
0.2 7 57 88 106 113 99
Total Reads Analyzed 172 180 172 175 168 175
  • Cancer sample reads were identified from less than 200 total cancer reads.
  • Since 1-4% of blood reads were identified as non-blood, we need the level of cancer to be greater than >>1.1% to make a cancer call. We can also score the identified non-blood read by comparing them to cancer haplotypes to increase confidence.

Calculate the r2 between pairs of CpGs