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Tour: runDE
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runDE
runDE | Batch | R-scripts | References

runDE adds differential expression (DE) to a singleTCW database. It uses JRI (Java-R interface) to R methods. TCW supplies edgeR1, EDSeq22 and GOseq3, or the user can supply an R-script to a method.

1. Select one condition from group 1 and one condition from group 2.

2. Select a method.

3. Only select Fixed Dispersion if no replicates. Only select Apply FDR if using an R-script that does not provide FDR adjusted p-values.

4. Click Save results in p-value column to save the results in the database. If the two conditions have been selected, it will provide a default name (that can be over-written).

5. Group 1 - Group 2 will run the DE method and (if Save selected) add the results to the database.

6. When all columns have been added, click on the Select p-value column until it says All p-value columns, then select Execute GOseq.

On the terminal window, it will be left in R so that you can execute R commands. The output to terminal suggests some commands to view graphs of the results.

(Click to see larger)

Batch mode

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All Pairs for Group 1: All conditions in Group 1 will be paired for DE analysis. For example, if Rhiz, Root, and Stem are selected, then DE columns RhRo, RhSt and RoSt will be computed.

All Pairs from File: A file will be read where the first column is Group 1, the second is Group 2 and the third is the column name. All entries will be computed and added to the database.

R-scripts

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A script of R commands can be provided, which uses the variables created by runDE to compute DE. RunDE writes the data into the R environment (e.g. the count data is written to a matrix called countData), executes the R script using the source command, and reads the results from the variable called results. Two scripts are supplied as example.

References

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  1. edgeR - Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26: 139-140.
  2. DESeq2 - Love MI, Huber W and Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15:550.
  3. GOSeq - Young MD, Wakefield MJ, Smyth GK, Oshlack A (2010) Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology 11: R14.
Email Comments To: tcw@agcol.arizona.edu