metevalue.RNA_general

A general method to calculate the e-value for RNA-seq data.

In the omics data association studies, it is common to conduct the p-value corrections to control the false significance. Beyond the P-value corrections, E-value is recently studied to facilitate multiple testing correction based on V. Vovk and R. Wang (2021) <doi:10.1214/20-AOS2020>. This package provides E-value calculation for DNA methylation data and RNA-seq data. Currently, five data formats are supported: DNA methylation levels using DMR detection tools (BiSeq, DMRfinder, MethylKit, Metilene and other DNA methylation tools) and RNA-seq data. The relevant references are listed below: Katja Hebestreit and Hans-Ulrich Klein (2022) <doi:10.18129/B9.bioc.BiSeq>; Altuna Akalin et.al (2012) <doi:10.18129/B9.bioc.methylKit>.

Yifan Yang

metevalue

E-Value in the Omics Data Association Studies

Xiaoqing Pan

Haoyuan Liu

metevalue.RNA_general function

<dl><dt>rna</dt>
<dd>data.frame: A data.frame object of RNAseq data. For example:
<table class="table">
<tr><td>TAG</td><td>treated1fb</td><td>treated2fb</td><td>untreated1fb</td><td>untreated2fb</td></tr>
<tr><td>TAG1</td><td>4.449648</td><td>4.750104</td><td>4.392285</td><td>4.497514</td></tr>
<tr><td>TAG2</td><td>8.241116</td><td>8.302852</td><td>8.318125</td><td>8.488796</td></tr>
<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>
</table></dd>
Row names (TAG1 and TAG2 in the above example) is also suggested.
<dt>group1_name</dt>
<dd>charactor: The name (pattern) of the first group. For example, "treated" in the above example.
For example `treated_abc` and `treated` will be considered as the same group if `group1_name = "treated"`. Use this with care in practice.</dd>
<dt>group2_name</dt>
<dd>charactor: The name (pattern) of the second group. For example, "untreated" in the above example.
For example `untreated_abc` and `untreated` will be considered as the same group if `group2_name = "untreated"`. Use this with care in practice.</dd></dl>

Arguments

A general method to calculate the e-value for RNA-seq data. — metevalue.RNA_general

<dl>

<dt>rna</dt>
<dd>data.frame: A data.frame object of RNAseq data. For example:
<table class='table'>
<tr><td>TAG</td><td>treated1fb</td><td>treated2fb</td><td>untreated1fb</td><td>untreated2fb</td></tr>
<tr><td>TAG1</td><td>4.449648</td><td>4.750104</td><td>4.392285</td><td>4.497514</td></tr>
<tr><td>TAG2</td><td>8.241116</td><td>8.302852</td><td>8.318125</td><td>8.488796</td></tr>
<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>
</table>


Row names (TAG1 and TAG2 in the above example) is also suggested.</dd>


<dt>group1_name</dt>
<dd>charactor: The name (pattern) of the first group. For example, "treated" in the above example.
For example `treated_abc` and `treated` will be considered as the same group if `group1_name = "treated"`. Use this with care in practice.</dd>


<dt>group2_name</dt>
<dd>charactor: The name (pattern) of the second group. For example, "untreated" in the above example.
For example `untreated_abc` and `untreated` will be considered as the same group if `group2_name = "untreated"`. Use this with care in practice.</dd>

</dl>

metevalue.RNA_general: A general method to calculate the e-value for RNA-seq data.

Description

Usage

Value

Arguments

Examples

TAG	treated1fb	treated2fb	untreated1fb	untreated2fb
TAG1	4.449648	4.750104	4.392285	4.497514
TAG2	8.241116	8.302852	8.318125	8.488796
...	...	...	...	...