| Title: | Differential Co-Expression and Differential Expression Analysis |
| Version: | 1.2 |
| 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. |
| Depends: | R (≥ 3.1.2) |
| License: | GPL-3 |
| LazyData: | false |
| VignetteBuilder: | knitr |
| Suggests: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2015-07-13 12:23:17 UTC; BB |
| Author: | Thomas Lui [aut, cre] |
| Maintainer: | Thomas Lui <tlui27@yahoo.com> |
| Repository: | CRAN |
| Date/Publication: | 2015-07-14 20:40:34 |
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()