Compute differentially expressed genes using Limma (workflow)

Workflow title

Compute differentially expressed genes using Limma

Provider

geneXplain GmbH

Workflow overview

Description

This workflow is designed to identify differentially expressed genes from several experimental conditions applying Limma statistics. Normalized data can be generated from Affymetrix (normalization/Affymetrix normalization Affymetrix normalization), Agilent (normalization/Agilent normalization Agilent normalization) or Illumina (normalization/Illumina normalization Illumina normalization) raw data and submitted as input. Also un-normalized count data derived from RNA-seq experiment can be used as input for this workflow. Please note that the Limma method requires two or more replicates for each condition. It is necessary to provide a unique name for each condition.

The workflow compares up to five conditions / groups in one run. All possible comparisons between the input conditions are calculated in one workflow run. The first step of the workflow is a quality control of the input data and gives out a density boxplot and a density plot. The primary result of the Limma method is filtered by several conditions in parallel, applying the Filter table method to identify up- and down-regulated probeset IDs for each comparision.

Filteration criterion used is as follows:

Upregulated: logFC>0.5 && adj_P_Val <0.05

Down regulated: logFC<-0.5 && adj_P_Val<0.05

Non-changed genes logFC<0.002 && logFC>-0.002

The output folder contains gene tables as well as the images of the density boxplots and density plots.

Reference: Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and 68 RNA-seq Computational Biology Solutions using R and Bioconductor. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, 2005.

Parameters

Input table

Input table with all normalized files

Probe type

Species

Condition_1

Please enter condition or group name 1

1_Columns

Select columns for condition 1

Condition_2

Please enter condition or group name 2

2_Columns

Select columns for condition 2

Condition_3

Please enter condition or group name 3

3_Columns

Condition_4

Please enter condition or group name 4

4_Columns

Condition_5

Please enter condition or group name 5

5_Columns

Results folder