Gfold 1.1.1 Difference Expression
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Please use Gfold 1.1.1 Count before using Gfold 1.1.1 Difference Expression
gfold - Generalized fold change for ranking differentially expressed genes from RNA-seq data.
GFOLD is especially useful when no replicate is available.
"GFOLD is especially useful when no replicate is available. GFOLD generalizes the fold change by considering the posterior distribution of log fold change, such that each gene is assigned a reliable fold change. It overcomes the shortcoming of p-value that measures the significance of whether a gene is differentially expressed under different conditions instead of measuring relative expression changes, which are more interesting in many studies. It also overcomes the shortcoming of fold change that suffers from the fact that the fold change of genes with low read count are not so reliable as that of genes with high read count, even these two genes show the same fold change."
Source: Feng J, Meyer CA, Wang Q, Liu JS, Liu XS, Zhang Y. GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data. Bioinformatics 2012
Test data for this app appears directly in the Discovery Environment in the Data window under Community Data -> iplantcollaborative -> example_data -> Gfold
The following files will be found:
- To use Gfold 1.1.1 Difference Expression, please have the following files available:
- sample_A.read_cnt and sample_B.read_cnt generated from Gfold 1.1.1 Count
Example Test Data
In this example, we will be using the Sample Data from the RNA-Seq Tutorial.
The RNA-Seq reads that we will be working is from Arabidopsis thaliana.
In this example, please use the following files to generate the count files:
1) Please select WT_rep1.read_cnt for Sample 1 (file will be generated from Gfold 1.1.1 Count Step B 3a)
2) Please select hy5_rep1.read_cnt for Sample 2 (file will be generated from Gfold 1.1.1 Count Step B 3b)
3) Output file name: WT_vs_hy5.diff
Significant cut off value for fold change?
-The Default option is 0.01.
Please keep value at 0.01 for this example.
Expect a Sample1_vs_Sample2_file_name.diff as output. For the test case, the output files that will be generated as WT_vs_hy5.diff.