Recursive Organizer (ROR)

Description

functions to perform ROR for sequence-based association analysis

Details

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$model.summary;
# output tables and figures used for ror result
data(proteinf)
ODS.ror(case.sub=case.sub, ctl.sub=ctl.sub, lib.sub=lib.sub, lib.sub.names=lib.sub.names, records=ror.res$records, dev.list=ror.res$dev.list, AIC.list=ror.res$AIC.list, deleted.snps.ls=ror.res$deleted.snps.ls, proteinf=proteinf, locus="DRB1*", ref.level="101");

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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> 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-package.Rd_%03d_medium.png", width=480, height=480)
> ### Name: HAP.ROR-package
> ### Title: Recursive Organizer (ROR)
> ### Aliases: HAP.ROR-package HAP.ROR
> 
> ### ** 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

> # output tables and figures used for ror result
> data(proteinf)
> ODS.ror(case.sub=case.sub, ctl.sub=ctl.sub, lib.sub=lib.sub, lib.sub.names=lib.sub.names, records=ror.res$records, dev.list=ror.res$dev.list, AIC.list=ror.res$AIC.list, deleted.snps.ls=ror.res$deleted.snps.ls, proteinf=proteinf, locus="DRB1*", ref.level="101");
Warning messages:
1: glm.fit: fitted probabilities numerically 0 or 1 occurred 
2: glm.fit: fitted probabilities numerically 0 or 1 occurred 
3: glm.fit: fitted probabilities numerically 0 or 1 occurred 
4: glm.fit: fitted probabilities numerically 0 or 1 occurred 
5: glm.fit: fitted probabilities numerically 0 or 1 occurred 
6: glm.fit: fitted probabilities numerically 0 or 1 occurred 
7: glm.fit: fitted probabilities numerically 0 or 1 occurred 
8: glm.fit: fitted probabilities numerically 0 or 1 occurred 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>