Difference between revisions of "Compute differentially expressed genes using Limma (workflow)"

Latest revision as of 16:34, 12 March 2019

Workflow title

Compute differentially expressed genes using Limma

Provider

geneXplain GmbH

[edit] Workflow overview

[edit] 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.

[edit] 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