Differential Expression Analysis





Input a tab delimited text file with headers. The first row is sample names and the first column is gene IDs. Values in the table are gene count numbers (read numbers)
Gene expression table file: If you have performed RNA-seq in Gene denovo, you can also input the gene expression table file (all.genes.expression.xls) which contains gene annotation information (e.g. Nr, GO, KEGG), so that there will be gene annotation information in the resulting differentially expressed gene table. If you have performed RNA-seq in Gene denovo, you can also input the gene expression table file (all.genes.expression.xls) which contains gene annotation information (e.g. Nr, GO, KEGG), so that there will be gene annotation information in the resulting differentially expressed gene table.
Select a file Example

Input a tab delimited text file where a row represents a comparison with the first column versus the second column.
Select a file Example

Input a tab delimited text file where sample names in the first column and group names in the second column. Note that this grouping file is also necessary for experiments without biological replicates.
*


You can choose P value or Q value for test value. P value is calculated by negative binomial distribution. Q value (false discovery rate, FDR) is calculated by FDR correction of P value.


If you have performed RNA-seq in Gene denovo, you can also input the gene expression table file (all.genes.expression.xls) which contains gene annotation information (e.g. Nr, GO, KEGG), so that there will be gene annotation information in the resulting differentially expressed gene table.


Threshold for P value or Q value. Normally, the threshold for differential expression analysis is 0.05. You can also set the threshold according to your experiment, like 0.01 or 0.001.


The expression fold change of genes between two groups. Normally, the threshold of fold change is 2. We defined genes with a fold change over the threshold (e.g 2) and test value lower than the threshold (e.g 0.05) as differentially expressed genes.


In RNA-seq, the expression of a gene (RPKM) is measured as the ratio of this gene’s expression and total RNAs’ expression. However, due to the possible difference of total RNA counts between different samples, the estimation of RPKM may lead to error. Hence, edgeR software can use NormFactors to correct this kind of error. Default: No.
Coefficient of Variation is used to describe the degree of dispersion across biological replicates. For experiments with replicates, CV value will be calculated according to the biological replicates within a group and you don’t need to set the CV value here. For experiments without replicates, you need to set a CV value here. We recommend that for human samples 0.4, for model organisms with similar genetic backgrounds 0.1, others 0.01.

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The use of variance analysis tool and interpret detailed tutorial

Function:
Differential expression analysis tool is used to find out differentially expressed genes between two groups of data. In general, the thresholds of significantly differential expression are expression fold change >2 and P value/Q value <0.05. This tool refers to edgeR software (http://www.bioconductor.org/packages/release/bioc/html/edgeR.html)to analyze differentially expressed genes between two groups.


Input:

①Input a tab delimited text file with headers. The first row is sample names and the first column is gene IDs. Values in the table are gene count numbers (read numbers)

Input a tab delimited text file with headers. The first row is sample names and the first column is gene IDs. Values in the table are gene count numbers (read numbers)
Gene expression table file: If you have performed RNA-seq in Gene denovo, you can also input the gene expression table file (all.genes.expression.xls) which contains gene annotation information (e.g. Nr, GO, KEGG), so that there will be gene annotation information in the resulting differentially expressed gene table.

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②Input a tab delimited text file where a row represents a comparison with the first column versus the second column.

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③Input a tab delimited text file where sample names in the first column and group names in the second column. Note that this grouping file is also necessary for experiments without biological replicates.

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Parameters:

①Test value: you can choose P value or Q value for test value. P value is calculated by negative binomial distribution. Q value (false discovery rate, FDR) is calculated by FDR correction of P value.

②Threshold for P value or Q value. Normally, the threshold for differential expression analysis is 0.05. You can also set the threshold according to your experiment, like 0.01 or 0.001.

③The expression fold change of genes between two groups. Normally, the threshold of fold change is 2. We defined genes with a fold change over the threshold (e.g 2) and test value lower than the threshold (e.g 0.05) as differentially expressed genes.

④Coefficient of Variation is used to describe the degree of dispersion across biological replicates. For experiments with replicates, CV value will be calculated according to the biological replicates within a group and you don’t need to set the CV value here. For experiments without replicates, you need to set a CV value here. We recommend that for human samples 0.4, for model organisms with similar genetic backgrounds 0.1, others 0.01.

⑤NormFactors:In RNA-seq, the expression of a gene (RPKM) is measured as the ratio of this gene’s expression and total RNAs’ expression. However, due to the possible difference of total RNA counts between different samples, the estimation of RPKM may lead to error. Hence, edgeR software can use NormFactors to correct this kind of error. Default: No.


Output:
1. A-vs-B.count.tmp:Gene ID and read count number of the comparison group
2. A-vs-B.DE.volcano.pdf:Differentially expressed genes volcano plot (vector figture)
3. A-vs-B.DE.volcano.png:Differentially expressed genes volcano plot (scalar figure)
4. A-vs-B.edgeR.all.xls:Differentially expressed genes table
5. A-vs-B.edgeR.filter.xls:Significantly differentially expressed genes table
6. edgeR.stat.h.png:Differentially expressed genes histogram between comparison groups (horizontal)
7. edgeR.stat.v.png:Differentially expressed genes histogram between comparison groups (vertical)
8. edgeR.stat.xls:Differentially expressed genes table of all comparison groups

Example: Gene count table file   Comparison file   Grouping file
Output:

1、Results folder all the files:

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2、Results all the comparison group of differentially expressed genes in the folder histogram (vertical):

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