getWRValuesForVolcano: get p-values of wilcoxon rank sum test for volcano

In quantable: Streamline Descriptive Analysis of Quantitative Data Matrices

Description

get p-values of wilcoxon rank sum test for volcano

Usage

getWRValuesForVolcano(x, y, paired = FALSE, adjust = TRUE)

Arguments

x	- one data matrix
y	- second data matrix
paired	a logical indicating whether you want a paired t-test.
adjust	pvalues using Benjamin Hochberg

Value

list with two fields fchange (fold change) and pval

Examples

a <- t(replicate(200,rnorm(20,runif(1,-3,3),1)))
b <- a[1:100,]
a <- a[101:200,]
boxplot(t(a[1:20,]))
boxplot(t(b[1:20,]))
res <- getWRValuesForVolcano(a,b)
volcanoplot(res$fchange , res$pval)

Example output

$upsubset
   regulation foldchange        pvals
3          up   4.154217 1.116068e-10
8          up   1.443059 2.567667e-04
11         up   1.114186 1.317168e-02
13         up   2.684724 1.872262e-06
16         up   3.232857 1.450889e-09
18         up   4.401687 1.116068e-10
19         up   1.233243 2.567667e-04
20         up   1.306340 2.207622e-03
30         up   1.685397 4.647065e-05
31         up   1.113760 3.994054e-04
34         up   2.271698 7.794646e-06
35         up   4.530477 1.934519e-10
41         up   1.907179 1.686258e-05
43         up   1.016159 2.887232e-02
44         up   3.502032 1.934519e-10
45         up   3.588645 9.672593e-10
46         up   1.480205 2.641647e-05
51         up   1.547229 8.851613e-06
55         up   3.432109 1.116068e-10
63         up   1.140473 4.647065e-05
66         up   3.421160 1.116068e-10
67         up   1.144922 3.994054e-04
74         up   2.606714 8.275249e-08
82         up   3.171429 1.116068e-10
89         up   4.797036 1.116068e-10
90         up   1.026942 5.590642e-04
91         up   2.054482 1.313335e-06
94         up   1.081371 2.872509e-04
95         up   3.563284 2.967727e-09

$downsubset
    regulation foldchange        pvals
1         down  -4.221855 1.116068e-10
2         down  -1.635735 7.794646e-06
6         down  -2.668083 2.072698e-09
7         down  -4.711558 1.116068e-10
9         down  -2.119034 7.057124e-07
10        down  -2.866951 5.864009e-09
17        down  -1.262795 2.636991e-03
23        down  -1.817108 1.250786e-04
25        down  -3.465967 1.116068e-10
28        down  -1.763562 2.226816e-06
32        down  -1.114963 5.196722e-03
42        down  -1.208220 5.590642e-04
47        down  -2.810001 1.517853e-08
48        down  -2.605168 3.627222e-10
49        down  -1.485495 5.300452e-05
52        down  -2.534328 1.131693e-08
53        down  -1.701204 1.227274e-05
57        down  -1.503305 2.337457e-04
58        down  -1.299945 4.647065e-05
59        down  -3.706115 1.116068e-10
60        down  -1.327725 1.946751e-05
62        down  -4.308547 1.116068e-10
65        down  -2.082901 6.865275e-06
68        down  -1.962082 1.410044e-04
69        down  -1.197515 7.613419e-04
70        down  -3.956183 1.116068e-10
75        down  -1.259387 6.101736e-04
76        down  -1.884328 1.569907e-06
78        down  -3.999213 5.974248e-10
80        down  -3.236460 1.116068e-10
81        down  -1.471163 2.085273e-04
83        down  -2.134782 7.057124e-07
84        down  -1.027524 1.458465e-03
86        down  -1.912489 2.072698e-09
96        down  -2.025030 8.851613e-06
97        down  -2.377019 5.864009e-09
98        down  -1.217442 3.647699e-04
100       down  -2.300522 3.675585e-08

quantable documentation built on May 2, 2019, 4:05 p.m.