Difference between revisions of "Compute differentially expressed genes (Illumina probes) (workflow)"

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[[File:Compute-differentially-expressed-genes-Illumina-probes-workflow-overview.png|400px]]
 
[[File:Compute-differentially-expressed-genes-Illumina-probes-workflow-overview.png|400px]]
 
== Description ==
 
== Description ==
This workflow is designed to identify upregulated, downregulated and non-changed genes for experimental data with three and more data points for each experiment and control. 
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This workflow is designed to identify differentially expressed genes from an experiment data set compared to a control data set.  Normalized data with Affymetrix probeset IDs can be submitted as input. Such normalized files are the output of the workflow [http://test.genexplain.com/bioumlweb/#de=analyses/Methods/Data normalization/Normalize Illumina experiment and control Normalize Illumina experiment and control].
  
As input, the normalized data with Illumina probe IDs or Illumina gene IDs can be submitted, both types of IDs can be treated.
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In the first step, the up- and down-regulated probes are identified and log fold change values are calculated for all probes using the ''Up and Down Identification ''analysis. This analysis applies Student’s T-test and calculates p-values, thus the number of data points should be at least three for each experiment data set and control data set. A histogram with the log fold change distribution from the whole experiment is drawn and given output image file.
  
Such normalized files are resulting from the output of the “Normalize data” procedure under  “analyses/Methods/Data normalization/Normalize Illumina experiment and control”.
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In addition the results are filtered by different conditions in parallel applying the ''Filter table'' method, to identify up-regulated, down-regulated, and non-changed Illumina probeset IDs. The filtering criteria are set as follows:
  
At the next step, p-value is calculated for up-and down-regulated Illumina IDs. This workflow applies Student T-test for p-value calculation, and therefore the number of data points should be at least three for each experiment and control.
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'''For up-regulated probes: LogFoldChange>0.5 and -log_P_value_>3.
  
Simultaneously, log fold change is calculated for Illumina IDs, and as the result of this step, a table is produced in which both LogFoldChange and p-value are assigned to each row.
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For down- regulated probes: LogFoldChange<-0.5 and -log_P_value_<-3.
  
Further, this table is filtered by several conditions in parallel, to identify upregulated, downregulated, and non-changed Agilent IDs.
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For non-changed genes : LogFoldChange<0.002 and LogFoldChange>-0.002'''
  
The filtering criteria are set as the following.
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The resulting tables of up-regulated, down-regulated, and non-changed Affymetrix probeset IDs are converted into a gene table with the ''Convert table'' method and annotated with additional information (gene descriptions, gene symbols, and species) via ''Annotate table'' method.  
  
For upregulated probes: LogFoldChange>0.5 and -log_P_value_>3.
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A result folder is generated and automatically named corresponding to the experiment data set name. This resulting folder contains all tables, the histogramm and a summary HTML report.
 
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For downregulated probes: LogFoldChange<-0.5 and -log_P_value_<-3.
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For non-changed probes: LogFoldChange<0.01 and LogFoldChange>-0.01
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Resulting tables of the upregulated, downregulated, and non-changed Illumina IDs are annotated with additional information, gene description, gene symbols, species.
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Finally, these tables are converted into the tables of genes. Two tables are produced, with Ensembl Gene IDs and with Entrez IDs.
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== Parameters ==
 
== Parameters ==
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[[Category:Workflows]]
 
[[Category:Workflows]]
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[[Category:GeneXplain workflows]]
 
[[Category:Autogenerated pages]]
 
[[Category:Autogenerated pages]]
[[Category:geneXplain workflows]]
 

Latest revision as of 16:34, 12 March 2019

Workflow title
Compute differentially expressed genes (Illumina probes)
Provider
geneXplain GmbH

[edit] Workflow overview

Compute-differentially-expressed-genes-Illumina-probes-workflow-overview.png

[edit] Description

This workflow is designed to identify differentially expressed genes from an experiment data set compared to a control data set.  Normalized data with Affymetrix probeset IDs can be submitted as input. Such normalized files are the output of the workflow normalization/Normalize Illumina experiment and control Normalize Illumina experiment and control.

In the first step, the up- and down-regulated probes are identified and log fold change values are calculated for all probes using the Up and Down Identification analysis. This analysis applies Student’s T-test and calculates p-values, thus the number of data points should be at least three for each experiment data set and control data set. A histogram with the log fold change distribution from the whole experiment is drawn and given output image file.

In addition the results are filtered by different conditions in parallel applying the Filter table method, to identify up-regulated, down-regulated, and non-changed Illumina probeset IDs. The filtering criteria are set as follows:

For up-regulated probes: LogFoldChange>0.5 and -log_P_value_>3.

For down- regulated probes: LogFoldChange<-0.5 and -log_P_value_<-3.

For non-changed genes : LogFoldChange<0.002 and LogFoldChange>-0.002

The resulting tables of up-regulated, down-regulated, and non-changed Affymetrix probeset IDs are converted into a gene table with the Convert table method and annotated with additional information (gene descriptions, gene symbols, and species) via Annotate table method.

A result folder is generated and automatically named corresponding to the experiment data set name. This resulting folder contains all tables, the histogramm and a summary HTML report.

[edit] Parameters

Experiment normalized
Control normalized
Species
Results folder
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