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Last data update: 2014.03.03

R: ROR

HAP.ror

R Documentation

ROR

Description

perform ROR for sequence-based association analysis

Usage

HAP.ror(case.sub, ctl.sub, lib.sub, lib.sub.names, alpha = 0.01, ref.level = NA, display.proc = TRUE)

Arguments

`case.sub`	case subjects, two columns for two haplotypes
`ctl.sub`	control subjects, two columns for two haplotypes
`lib.sub`	the alleles library contains allele sequences for those only appear in the case and control samples
`lib.sub.names`	the corresponding names of the alleles (mapping of full name in the library and short name in samples)
`alpha`	significance level
`ref.level`	name of the reference allele, "NA" use the most common allele as reference, can also specify allele name, for DRB1, it is "101"
`display.proc`	display the grouping process or not? default is TRUE

Details

This function performs ROR for sequence-based association analysis

Value

`dev.list`	deviances for all steps of ROR
`AIC.list`	AICs for all steps of ROR
`df.list`	degree of freedom for all steps of ROR
`records`	the record of the whole ROR process
`deleted.snps.ls`	the history of SNP deletions for all steps of ROR
`deleted.snps`	the final vector of deleted SNPs
`grp.result`	the final grouping result
`model.summary`	the GLM model summary for the final grouping

Author(s)

Lue Ping Zhao and Xin Huang
Maintainer: Xin Huang <xhuang.fhcrc@gmail.com>

References

Zhao, L.P. and Huang, X. Recursive organizer (ROR): an analytic framework for sequence-based association analysis. Human Genetics, 2013

Examples

library("HAP.ROR")
data(case.sub)
data(ctl.sub)
data(lib.sub)
data(lib.sub.names)
ror.res <- HAP.ror(case.sub, ctl.sub, lib.sub, lib.sub.names, alpha=0.01, ref.level="101");

# grouping result:
round(ror.res$dev.list, 2);
round(ror.res$AIC.list, 2);
ror.res$df.list;
ror.res$deleted.snps;
ror.res$grp.result;
ror.res$significant;
# model summary:
ror.res$model.summary;

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(HAP.ROR)
Loading required package: hash
hash-2.2.6 provided by Decision Patterns

Loading required package: ape

Attaching package: 'HAP.ROR'

The following object is masked from 'package:stats':

    AIC

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HAP.ROR/HAP.ror.Rd_%03d_medium.png", width=480, height=480)
> ### Name: HAP.ror
> ### Title: ROR
> ### Aliases: HAP.ror
> ### Keywords: ~kwd1 ~kwd2
> 
> ### ** Examples
> 
> library("HAP.ROR")
> data(case.sub)
> data(ctl.sub)
> data(lib.sub)
> data(lib.sub.names)
> ror.res <- HAP.ror(case.sub, ctl.sub, lib.sub, lib.sub.names, alpha=0.01, ref.level="101");
deleting: 49 50 51 61 68 70 76 78 80 81 
deleting: 49 50 51 61 68 70 76 78 80 81 26 40 41 48 55 56 60 67 77 79 
deleting: 49 50 51 61 68 70 76 78 80 81 26 40 41 48 55 56 60 67 77 79 12 23 25 29 52 57 59 62 63 65 66 69 71 72 
deleting: 49 50 51 61 68 70 76 78 80 81 26 40 41 48 55 56 60 67 77 79 12 23 25 29 52 57 59 62 63 65 66 69 71 72 17 18 22 24 35 37 42 47 53 54 92 
deleting: 49 50 51 61 68 70 76 78 80 81 26 40 41 48 55 56 60 67 77 79 12 23 25 29 52 57 59 62 63 65 66 69 71 72 17 18 22 24 35 37 42 47 53 54 92 1 2 4 7 13 14 15 16 39 83 85 86 89 90 
deleting: 49 50 51 61 68 70 76 78 80 81 26 40 41 48 55 56 60 67 77 79 12 23 25 29 52 57 59 62 63 65 66 69 71 72 17 18 22 24 35 37 42 47 53 54 92 1 2 4 7 13 14 15 16 39 83 85 86 89 90 9 10 11 20 30 31 32 33 34 36 43 64 91 
deleting: 49 50 51 61 68 70 76 78 80 81 26 40 41 48 55 56 60 67 77 79 12 23 25 29 52 57 59 62 63 65 66 69 71 72 17 18 22 24 35 37 42 47 53 54 92 1 2 4 7 13 14 15 16 39 83 85 86 89 90 9 10 11 20 30 31 32 33 34 36 43 64 91 8 58 74 75 87 88 
deleting: 49 50 51 61 68 70 76 78 80 81 26 40 41 48 55 56 60 67 77 79 12 23 25 29 52 57 59 62 63 65 66 69 71 72 17 18 22 24 35 37 42 47 53 54 92 1 2 4 7 13 14 15 16 39 83 85 86 89 90 9 10 11 20 30 31 32 33 34 36 43 64 91 8 58 74 75 87 88 3 5 21 27 28 44 45 46 82 84 
deleting: 49 50 51 61 68 70 76 78 80 81 26 40 41 48 55 56 60 67 77 79 12 23 25 29 52 57 59 62 63 65 66 69 71 72 17 18 22 24 35 37 42 47 53 54 92 1 2 4 7 13 14 15 16 39 83 85 86 89 90 9 10 11 20 30 31 32 33 34 36 43 64 91 8 58 74 75 87 88 3 5 21 27 28 44 45 46 82 84 6 19 38 73 
There were 12 warnings (use warnings() to see them)
> 
> # grouping result:
> round(ror.res$dev.list, 2);
[1]   0.00   0.02   4.29   3.51   3.75   0.21   4.90   3.32 -91.25
> round(ror.res$AIC.list, 2);
[1] 0.95 0.88 0.04 0.06 0.05 0.65 0.03 0.07 0.00
> ror.res$df.list;
[1]   1   1   1   1   1   1   1   1 -75
> ror.res$deleted.snps;
 [1]  1  2  3  4  5  7  8  9 10 11 12 13 14 15 16 17 18 20 21 22 23 24 25 26 27
[26] 28 29 30 31 32 33 34 35 36 37 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
[51] 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 74 75 76 77 78 79
[76] 80 81 82 83 84 85 86 87 88 89 90 91 92
> ror.res$grp.result;
                   grp
 [1,] "DRB1*01:01" "1"
 [2,] "DRB1*03:01" "1"
 [3,] "DRB1*04:01" "1"
 [4,] "DRB1*04:04" "1"
 [5,] "DRB1*07:01" "1"
 [6,] "DRB1*08:01" "1"
 [7,] "DRB1*09:01" "1"
 [8,] "DRB1*13:01" "1"
 [9,] "DRB1*13:02" "1"
> ror.res$significant;
[1] 1
> # model summary:
> ror.res$model.summary;

Call:
glm(formula = grp.sample[, 1] ~ allele.factor, family = binomial)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.435  -1.435   0.940   0.940   0.940  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)  
(Intercept)      0.5878     0.2494   2.356   0.0185 *
allele.factor  -18.2099  1819.5131  -0.010   0.9920  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 104.539  on 76  degrees of freedom
Residual deviance:  91.246  on 75  degrees of freedom
AIC: 95.246

Number of Fisher Scoring iterations: 17

> 
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> 
> dev.off()
null device 
          1 
>