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

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[[File:Compute-differentially-expressed-genes-Agilent-Tox-probes-workflow-overview.png|400px]]
 
[[File:Compute-differentially-expressed-genes-Agilent-Tox-probes-workflow-overview.png|400px]]
 
== Description ==
 
== Description ==
This workflow is designed to identify up-regulated, down-regulated 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 Affymetrix experiment and control Normalize Affymetrix experiment and control].
  
As input, normalized data with Agilent probeset IDs can be submitted. Such normalized files are the output of the “Normalize data” procedure.
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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.
  
In the next step, p-values for up- and down- regulated probes are calculated for all probes using the “Up and Down Identification”'' ''analysis,. This analysis applies Student’s T-test for p-value calculation, thus the number of data points should be at least three for each 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 Affymetrix Tox probeset IDs. The filtering criteria are set as follows:
  
Simultaneously, the log fold change is calculated for each probeset ID, resulting in  a table in which both log fold changes and p-values are assigned to each probeset ID. A histogram with log fold change distribution is calculated and generated as one of the output files.
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'''For up-regulated probes: LogFoldChange>0.5 and -log_P_value_>3.
 
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In addition, this table is filtered by several conditions in parallel applying the “Filter table” method, to identify up-regulated, down-regulated, and non-changed Agilent probeset IDs. The filtering criteria are set as follows:
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For up-regulated probes: LogFoldChange>0.5 and -log_P_value_>3.
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For down- 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
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For non-changed genes : LogFoldChange<0.002 and LogFoldChange>-0.002'''
  
The resulting tables of the up-regulated, down-regulated, and non-changed Agilent probeset IDs are converted into a gene set via the “Convert table” method and annotated with additional information, gene descriptions, gene symbols, and species via “Annotate table”. Two tables are produced, with Ensembl Gene IDs and with Entrez IDs. 
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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.  
  
A new folder is generated as output containing Ensemble and Entrez gene tables for up-regulated, down-regulates, up- and down-regulated together, and non-changed genes. After completion of the workflow, a script generates a report which gives the summary of the workflow output files. 
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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.
  
 
== Parameters ==
 
== Parameters ==

Latest revision as of 16:34, 12 March 2019

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

[edit] Workflow overview

Compute-differentially-expressed-genes-Agilent-Tox-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 Affymetrix experiment and control Normalize Affymetrix 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 Affymetrix Tox 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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