---
title: "Differential Co-Expression and Differential Expression Analysis"
author: "Thomas Lui"
date: "Sunday, July 12, 2015"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{DECODE }
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

### Package description
Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (**D**iffer**E**ntial **CO**-expression and **D**ifferential **E**xpression) algorithm. Given a set of gene expression data and functional gene set data, the program will return a table summary of the selected gene sets with high differential co-expression and high differential expression (HDC-HDE).

### Reference
Lui, TWH, Tsui, NBY, Chan, LWC, SP Siu, PM, Wong, C, Yung, BYM. (2015) DECODE: an integrated differential co-expression and differential expression analysis of gene expression data. BMC Bioinformatics, May 31;16:182.
http://www.biomedcentral.com/1471-2105/16/182?fmt_math=yes&fmt_math_check=on

### Input gene expression data format

**Data format:**

* Columns:
    + Columns are tab separated
    
    + Column 1: Official gene symbol

    + Column 2: Probe ID

    + Starting from column 3: Expression for different samples

* Rows:
    + Row 1 (starting from column 3): Sample class ("1" indicates control group; "2" indicates case group)
    
    + Row 2: Sample id
    
    + Starting from row 3: Expression for different genes

* An example for data format:

geneName | probeID | 2 | 2 | 2 | 1 | 1 | 1
-------- | ------- | - | - | - | - | - | - 
- | - | Case Sample 1 | Case Sample 2 | Case Sample 3 | Control Sample 1 | Control Sample 2 | Control Sample 3
7A5 | ILMN_1762337 | 5.12621 | 5.19419 | 5.06645 | 5.40649 | 5.51259 | 5.38700
A1BG | ILMN_2055271 | 5.63504 | 5.68533 | 5.66251 | 5.37466 | 5.43955 | 5.50973
A1CF | ILMN_2383229 | 5.41543 | 5.58543 | 5.43239 | 5.49634 | 5.62685 | 5.36962
A26C3 | ILMN_1653355 | 5.56713 | 5.55470 | 5.59547 | 5.46895 | 5.49622 | 5.50094
A2BP1 | ILMN_1814316 | 5.23016 | 5.33808 | 5.31413 | 5.30586 | 5.40108 | 5.31855
A2M | ILMN_1745607 | 7.65332 | 6.56431 | 8.20163 | 9.19837 | 9.04295 | 10.1448
A2ML1 | ILMN_2136495 | 5.53532 | 5.93801 | 5.33728 | 5.36676 | 5.79942 | 5.13974
A3GALT2 | ILMN_1668111 | 5.18578 | 5.35207 | 5.30554 | 5.26107 | 5.26536 | 5.28932
A4GALT | ILMN_1735045 | 6.34751 | 5.56750 | 6.92335 | 7.49523 | 7.12119 | 6.54748
A4GNT | ILMN_1680754 | 5.26417 | 5.28596 | 5.27560 | 5.28830 | 5.08440 | 5.44869


### Input gene set data format

**Data format:**

* Rows:
    + Each row consists of a gene set (including gene set names, id, and genes)

* Columns:
    + Columns are tab separated

    + Column 1: Name of gene set

    + Column 2: Gene set ID (e.g. GO ID)

    + Starting from column 3: Genes (using official gene symbols) in the gene set 


* An example for data format:

Column 1 | Column 2 | Column 3, 4, ...
-------- | ------- | - 
positive regulation of epidermal growth factor-activated receptor activity | GO 0045741 | EREG	FBXW7	EPGN	ADAM17	ADRA2C	ADRA2A	TGFA	EGF
pyrimidine-containing compound salvage | GO 0008655 | UPP1	TYMP	TK1	UPP2	UCKL1	CDA	TK2	UCK1	DCK


### To load the package:
```{r}
library(decode)
```

### Example 1:
Running a larger set of gene expression data with 1400 genes.
It will take ~16 minutes to complete. (Computer used: An Intel Core i7-4600 processor, 2.69 GHz, 8 GB RAM)

```
path = system.file('extdata', package='decode')
geneSetInputFile = file.path(path, "geneSet.txt")
geneExpressionFile = file.path(path, "Expression_data_1400genes.txt")
runDecode(geneSetInputFile, geneExpressionFile)
```



### Example 2:
A smaller set of gene expression data with 50 genes to satisfy CRAN's submission requirement. (No results will be generated)
```{r}
path = system.file('extdata', package='decode')
geneSetInputFile = file.path(path, "geneSet.txt")
geneExpressionFile = file.path(path, "Expression_data_50genes.txt")
runDecode(geneSetInputFile, geneExpressionFile)

```