Dinh 2011/NOTES/2011-11-28
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HL110 read 2 resynthesis[edit]
- Using Read 2 Cluster Resynthesis Kit
- Thaw content of kit on ice
- Prepare HP3 (#19)
- 2.85 mL PW1 + 150 ul of 2 N NaOH
- Prepare HP2 (#16)
- Content is 3 mL
- Add: 15 ul of each: SolSeqV6.3.2r, N2RevSeq2, and Nextera2
Family background substraction with UCLA samples[edit]
- The ANOVA is a generalization of the two sample t-test to find out whether multiple populations have equal means
- If different families have different population means at a CpG site then it is influenced by family background
- The ANOVA test requires a normality assumption, where the individual in each group is picked from a normal distribution
- This is not true since there are affected/unaffected members in each family so it may be skewed.
- The Kruskal-Wallis test can be applied to test where we test the null hypothesis that the samples come from populations such that the probability of a random observation from one group is greater than a random observation from another group is 0.5
- Be careful using this test when the different groups have different shapes or variances.
- The p value of the Kruskal-Wallis test follows a chi-square distribution, with degrees of freedom = the number of groups minus 1.
- It does not follow chi-square if N in each group is < 5.
- Script to perform test: File:CpgMatrixKrushalWallis.txt
- Used only families with at least 5 members as groups to find the statistics.
- Filter out sites where the p-value < 0.10
Hierarchical clustering[edit]
Example of how the h-clust were each generated
> A =read.table("sites_KW_signif0.10", header=TRUE, row.names=1) > A$STDEV=NULL > A$KW_pvalue=NULL > A$min_RD=NULL > A$mean_RD=NULL > B=as.matrix(A) > dissim=1-abs(cor(B,method="pearson", use="pairwise.complete.obs")) > nrow(A) > distance=as.dist(dissim) > plot(hclust(distance, method="average"), main="Kruskal-Wallis significant sites, n=") > pdf("Kruskal-Wallis significant sites 0.10") > plot(hclust(distance, method="average"), main="Kruskal-Wallis significant sites, n=") > dev.off()
Sample identity check[edit]
- Took *.snp file generated by the mapping pipeline
- Used bisSnpFilterHg19.pl to filter the SNPs
- Count homozygous [ATGC] calls and heterozygous [^AGTC] calls for chromosome X
Comments | Data_ID | Number of heterozygous X SNP | Number of homozygous X SNP | %het | %hom | Sample_ID | Sex |
Mislabeled as female | GK0210-002 | 2 | 62 | 3% | 97% | GK0210-002 | Female |
Mislabeled as female | GK0277-002 | 2 | 45 | 4% | 96% | GK0277-002 | Female |
GK0178-003 | 57 | 70 | 45% | 55% | GK0178-003 | Female | |
GK0100-003 | 47 | 48 | 49% | 51% | GK0100-003 | Female | |
GK0050-003 | 54 | 55 | 50% | 50% | GK0050-003 | Female | |
GK0050-002 | 54 | 54 | 50% | 50% | GK0050-002 | Female | |
GK0100-002 | 67 | 65 | 51% | 49% | GK0100-002 | Female | |
GK0277-004 | 58 | 56 | 51% | 49% | GK0277-004 | Female | |
GK0344-003 | 68 | 65 | 51% | 49% | GK0344-003 | Female | |
GK0271-003 | 70 | 66 | 51% | 49% | GK0271-003 | Female | |
GK0359-004 | 59 | 54 | 52% | 48% | GK0359-004 | Female | |
GK0005-002 | 76 | 69 | 52% | 48% | GK0005-002 | Female | |
GK0123-005 | 62 | 56 | 53% | 47% | GK0123-005 | Female | |
GK0167-006 | 56 | 50 | 53% | 47% | GK0167-006 | Female | |
GK0122-003 | 51 | 45 | 53% | 47% | GK0122-003 | Female | |
GK0091-003 | 72 | 63 | 53% | 47% | GK0091-003 | Female | |
GK0391-002 | 54 | 46 | 54% | 46% | GK0391-002 | Female | |
GK0271-004 | 74 | 62 | 54% | 46% | GK0271-004 | Female | |
GK0365-002 | 62 | 51 | 55% | 45% | GK0365-002 | Female | |
GK0123-003 | 64 | 51 | 56% | 44% | GK0123-003 | Female | |
GK0241-004 | 51 | 40 | 56% | 44% | GK0241-004 | Female | |
GK0167-003 | 48 | 37 | 56% | 44% | GK0167-003 | Female | |
GK0295-002 | 55 | 42 | 57% | 43% | GK0295-002 | Female | |
GK0396-002 | 75 | 57 | 57% | 43% | GK0396-002 | Female | |
GK0187-002 | 62 | 47 | 57% | 43% | GK0187-002 | Female | |
GK0281-002 | 59 | 43 | 58% | 42% | GK0281-002 | Female | |
GK0027-003 | 66 | 46 | 59% | 41% | GK0027-003 | Female | |
GK0359-003 | 63 | 42 | 60% | 40% | GK0359-003 | Female | |
GK0241-002 | 76 | 50 | 60% | 40% | GK0241-002 | Female | |
GK0290-002 | 64 | 42 | 60% | 40% | GK0290-002 | Female | |
GK0005-004 | 0 | 53 | 0% | 100% | GK0005-004 | Male | |
GK0091-001 | 0 | 64 | 0% | 100% | GK0091-001 | Male | |
GK0123-001 | 0 | 56 | 0% | 100% | GK0123-001 | Male | |
GK0178-004 | 0 | 77 | 0% | 100% | GK0178-004 | Male | |
GK0187-001 | 0 | 83 | 0% | 100% | GK0187-001 | Male | |
GK0277-005 | 0 | 62 | 0% | 100% | GK0277-005 | Male | |
GK0281-001 | 0 | 51 | 0% | 100% | GK0281-001 | Male | |
GK0290-001 | 0 | 65 | 0% | 100% | GK0290-001 | Male | |
GK0290-004 | 0 | 60 | 0% | 100% | GK0290-004 | Male | |
GK0295-003 | 0 | 64 | 0% | 100% | GK0295-003 | Male | |
GK0344-002 | 0 | 70 | 0% | 100% | GK0344-002 | Male | |
GK0344-004 | 0 | 53 | 0% | 100% | GK0344-004 | Male | |
GK0391-001 | 0 | 70 | 0% | 100% | GK0391-001 | Male | |
GK0005-001 | 1 | 80 | 1% | 99% | GK0005-001 | Male | |
GK0271-001 | 1 | 79 | 1% | 99% | GK0271-001 | Male | |
GK0365-003 | 1 | 79 | 1% | 99% | GK0365-003 | Male | |
GK0391-003 | 1 | 77 | 1% | 99% | GK0391-003 | Male | |
GK0210-003 | 1 | 75 | 1% | 99% | GK0210-003 | Male | |
GK0277-003 | 1 | 72 | 1% | 99% | GK0277-003 | Male | |
GK0123-004 | 1 | 71 | 1% | 99% | GK0123-004 | Male | |
GK0344-001 | 1 | 70 | 1% | 99% | GK0344-001 | Male | |
GK0396-003 | 1 | 70 | 1% | 99% | GK0396-003 | Male | |
GK0187-004 | 1 | 69 | 1% | 99% | GK0187-004 | Male | |
GK0100-001 | 1 | 67 | 1% | 99% | GK0100-001 | Male | |
GK0281-003 | 1 | 65 | 2% | 98% | GK0281-003 | Male | |
GK0100-006 | 1 | 64 | 2% | 98% | GK0100-006 | Male | |
GK0122-001 | 1 | 63 | 2% | 98% | GK0122-001 | Male | |
GK0295-004 | 1 | 63 | 2% | 98% | GK0295-004 | Male | |
GK0396-001 | 1 | 62 | 2% | 98% | GK0396-001 | Male | |
GK0100-004 | 1 | 61 | 2% | 98% | GK0100-004 | Male | |
GK0178-002 | 1 | 60 | 2% | 98% | GK0178-002 | Male | |
GK0359-006 | 1 | 60 | 2% | 98% | GK0359-006 | Male | |
GK0167-002 | 1 | 59 | 2% | 98% | GK0167-002 | Male | |
GK0241-005 | 1 | 59 | 2% | 98% | GK0241-005 | Male | |
GK0122-004 | 1 | 56 | 2% | 98% | GK0122-004 | Male | |
GK0365-001 | 1 | 56 | 2% | 98% | GK0365-001 | Male | |
GK0359-001 | 1 | 54 | 2% | 98% | GK0359-001 | Male | |
GK0005-003 | 1 | 51 | 2% | 98% | GK0005-003 | Male | |
#1 | GK0123-002 | 1 | 49 | 2% | 98% | GK0123-002 | Male |
GK0167-001 | 1 | 48 | 2% | 98% | GK0167-001 | Male | |
GK0091-002 | 2 | 92 | 2% | 98% | GK0091-002 | Male | |
GK0241-003 | 1 | 46 | 2% | 98% | GK0241-003 | Male | |
GK0277-001 | 1 | 42 | 2% | 98% | GK0277-001 | Male | |
GK0091-004 | 2 | 82 | 2% | 98% | GK0091-004 | Male | |
GK0027-004 | 2 | 79 | 2% | 98% | GK0027-004 | Male | |
GK0178-001 | 2 | 77 | 3% | 97% | GK0178-001 | Male | |
GK0210-004 | 2 | 76 | 3% | 97% | GK0210-004 | Male | |
GK0391-004 | 2 | 76 | 3% | 97% | GK0391-004 | Male | |
GK0295-001 | 2 | 74 | 3% | 97% | GK0295-001 | Male | |
GK0271-002 | 2 | 70 | 3% | 97% | GK0271-002 | Male | |
GK0290-003 | 2 | 68 | 3% | 97% | GK0290-003 | Male | |
GK0167-004 | 2 | 67 | 3% | 97% | GK0167-004 | Male | |
GK0365-004 | 2 | 65 | 3% | 97% | GK0365-004 | Male | |
GK0271-005 | 2 | 64 | 3% | 97% | GK0271-005 | Male | |
GK0241-001 | 2 | 60 | 3% | 97% | GK0241-001 | Male | |
GK0359-002 | 2 | 58 | 3% | 97% | GK0359-002 | Male | |
GK0027-001 | 2 | 56 | 3% | 97% | GK0027-001 | Male | |
GK0050-004 | 3 | 66 | 4% | 96% | GK0050-004 | Male | |
#2 | GK0123-002 | 2 | 44 | 4% | 96% | GK0123-002 | Male |
GK0027-002 | 3 | 63 | 5% | 95% | GK0027-002 | Male | |
GK0122-002 | 3 | 60 | 5% | 95% | GK0122-002 | Male | |
GK0187-003 | 3 | 57 | 5% | 95% | GK0187-003 | Male | |
GK0050-001 | 4 | 63 | 6% | 94% | GK0050-001 | Male | |
Mislabeled as male | GK0396-004 | 62 | 70 | 47% | 53% | GK0396-004 | Male |
Mislabeled as male | GK0210-001 | 63 | 59 | 52% | 48% | GK0210-001 | Male |
Mislabeled as male | GK0050-005 | 63 | 57 | 53% | 48% | GK0050-005 | Male |
No data | GK0281-004 | XX | XX | XX | XX | GK0281-004 | Male |