Difference between revisions of "Compute differentially expressed genes (Affymetrix probes) (workflow)"
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[[File:Compute-differentially-expressed-genes-Affymetrix-probes-workflow-overview.png|400px]] | [[File:Compute-differentially-expressed-genes-Affymetrix-probes-workflow-overview.png|400px]] | ||
== Description == | == Description == | ||
| − | This workflow is designed to identify | + | 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]. |
| − | + | 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 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 | + | 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. | |
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== Parameters == | == Parameters == | ||
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;Control normalized | ;Control normalized | ||
;Species | ;Species | ||
| + | ;Probe type | ||
;Results folder | ;Results folder | ||
Latest revision as of 16:34, 12 March 2019
- Workflow title
- Compute differentially expressed genes (Affymetrix probes)
- Provider
- geneXplain GmbH
[edit] Workflow overview
[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 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
- Probe type
- Results folder
