Package 'decode'

Title: Differential Co-Expression and Differential Expression Analysis
Description: Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.
Authors: Thomas Lui [aut, cre]
Maintainer: Thomas Lui <[email protected]>
License: GPL-3
Version: 1.2
Built: 2024-12-08 07:02:02 UTC
Source: CRAN

Help Index


Calculate the p-value between selected genes and functional gene set

Description

Calculate the p-value between selected genes and functional gene set

Usage

getAssoGeneSetPValue(geneList, geneSet, multipleTestCount, MaxGene)

Arguments

geneList

Selected genes

geneSet

Functional gene set

multipleTestCount

Number of multiple testing

MaxGene

Number of genes in expression data

Value

The adjusted p-value for the associated gene set


Get best associated functional gene sets for partitions of gene i

Description

Get best associated functional gene sets for partitions of gene i

Usage

getBestAssociatedGeneSet(pathway, all8Partitions, onePartition, MaxGene,
  minSupport)

Arguments

pathway

All functional gene sets

all8Partitions

All eight possible partitions for gene i

onePartition

The partition to be associated with the functional gene set

MaxGene

Number of genes in expression data

minSupport

Minimum support for functional gene set

Value

The adjusted p-values for the best associated gene set of the input partition


Adjust p-value by Bonferroni correction

Description

Adjust p-value by Bonferroni correction

Usage

getBonferroniPValue(pValues)

Arguments

pValues

Unadjusted p-values

Value

Adjusted p-values


Perform chi-square optimization

Description

Perform chi-square optimization

Usage

getDE_DC_OptimalThreshold(t_result, MaxGene, d_r, minSupport)

Arguments

t_result

The t-statistics

MaxGene

Number of genes in expression data

d_r

DC measures

minSupport

The minimum expected frequency in contingency table

Value

The optimal threshold information


Adjust p-value by Benjamini and Hochberg method

Description

Adjust p-value by Benjamini and Hochberg method

Usage

getFDR(pValues)

Arguments

pValues

Unadjusted p-values

Value

Adjusted p-values


Get gene index of 8 partitions for gene i

Description

Get gene index of 8 partitions for gene i

Usage

getPartitionIndex(gene_i, t_result, optimalCutOff, abs_r)

Arguments

gene_i

Gene i index

t_result

t-statistics

optimalCutOff

Optimal thresholds

abs_r

Matrix consisting of absolute values of all differential co-expression measures

Value

The selected genes for each partition in index


read functional gene sets

Description

read functional gene sets

Usage

getPathway(inputFile, geneName, minSupport)

Arguments

inputFile

Input file name

geneName

Gene name lists

minSupport

Minimum support

Value

Functional gene set


Open file to write result

Description

Open file to write result

Usage

openFileToWrite(filename)

Arguments

filename

file name Output: Results in text file


Differential Co-Expression and Differential Expression Analysis

Description

Given a set of gene expression data and functional gene set data, the program will return a table summary for the selected gene sets with high differential co-expression and high differential expression (HDC-HDE). User need to specify the input paths for the gene expression data and functional gene set data.

Usage

runDecode(geneSetInputFile, geneExpressionFile)

Arguments

geneSetInputFile

Path for functional gene set data

geneExpressionFile

Path for gene expression data

Input: (1) gene expression data

(2) functional gene set data

Output: Table summary for the selected HDC-HDE gene sets, 'out_summary.txt'

Data format for gene expression data (Columns are tab-separated):

Column 1: Official gene symbol

Column 2: Probe ID

Starting from column 3: Expression for different samples

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

Example:

geneName probeID 2 2 2 1 1 1

- - Case1 Case2 Case3 Control1 Control2 Control3

7A5 ILMN_1762337 5.12621 5.19419 5.06645 5.40649 5.51259 5.387

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 5.5547 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

Data format for functional gene set data (Columns are tab-separated):

Column 1: Functional gene set name

Column 2: Other description such as gene set id

Starting from column 3: Official gene symbols for the functional gene set

Example:

B cell activation GO\GO:0042113 AKAP17A ZAP70 PFDN1 ...

apoptotic signaling pathway GO\GO:0097190 ITPR1 PTH DNAJC10 HINT1 ...

Details

The main program for DECODE algorithm

To run an example using expression data with 1400 genes.

runDecode("\extdata\geneSet.txt","\extdata\Expression_data_1400genes.txt")

or

runDecode("/extdata/geneSet.txt","/extdata/Expression_data_1400genes.txt")

The sample data with 1400 genes takes 16 minutes to complete. (Computer used: An Intel Core i7-4600 processor, 2.69 GHz, 8 GB RAM)

Examples

## Not run: 
path = system.file('extdata', package='decode')
geneSetInputFile = file.path(path, "geneSet.txt")
geneExpressionFile = file.path(path, "Expression_data_50genes.txt")
runDecode(geneSetInputFile, geneExpressionFile)

## End(Not run)

Summarize the functional gene set results into text file

Description

Summarize the functional gene set results into text file

Usage

sumResult_MinGain()