Tutorial Affymetrix Expression Microarray Normalization
INPUT
#### STEPS [1. Select your data](tutorial-affymetrix-expression-microarray-normalization#select-your-data)
[2. Select analysis](tutorial-affymetrix-expression-microarray-normalization#select-analysis)
[3. Fill information job](tutorial-affymetrix-expression-microarray-normalization#fill-information-job)
[4. Press *Launch job* button](tutorial-affymetrix-expression-microarray-normalization#press-launch-job-button)
#### OUTPUT - [Input parameters](tutorial-affymetrix-expression-microarray-normalization#input-parameters) - [Output results](tutorial-affymetrix-expression-microarray-normalization#output-results)
INPUT
#####Input data Input data should be a matrix upload as the data type Microarray One chnanel Affymetrix data type. See data types [here](Data Types).
Using Babelomics you can process "Affymetrix expression arrays" 3' Gene Expression Analysis Arrays (the old style chips) and Whole-Transcript Expression Exon and Gene Level Arrays (the newer microarrays). At the moment Whole-Transcript Affymetrix arrays are only processed at gene Level in Babelomics.
In this section of the form you can select the dataset you want to normalize. There are two options to upload your data:
- from this form: Select your data / File browser / Upload
- previously using the Upload Menu in Babelomics and tagged it with the Microarray One chnanel Affymetrix data type. Datasets that are not tagged as Microarray One chnanel Affymetrix data type cannot be normalized using the Affymetrix Normalization Tool.
#####Online example Here you can load small datasets from our server. You can use them to run this example and see how the tool works. Click on the links to load the data: preprocessing a two classes matrix or Affy sample 1.
### STEPS #####Select your data First step is to select your data to analyze.
#####Select analysis Select the analysis you want to perform. More than one option can be selected here so several analyses can be processed at a time. Despite of this, the methods work independently hence, the results you will get will be the same running all of them at a time or one after the other one.
- RMA and PIER are proper normalization methods that will yield continuous intensity measurements.
- Present-absent calls will provide a classification of the genes as present or absent within each sample.
See [microarray normalization methods](Main areas. Processing) section for details on the algorithms.
#####Fill information job
- Select the output folder
- Choose a job name
- Specify a description for the job if desired.
#####Press Launch job button
Press launch button and wait until the results is finished. A normal job may last approximately few minutes but the time may vary depending on the size of data. See the state of your job by clicking the jobs button in the top right at the panel menu. A box will appear at the right of the web browser with all your jobs. When the analysis is finished, you will see the label "Ready". Then, click on it and you will be redirected to the results page.
### OUTPUT #### Input parameters In this section you will find a reminder of the parameters or settings you have used to run the analysis.
Output results
Here you will find result data files containing either normalized data or present-absent calls.
If you did run a normalization (RMA or PIER) Babelomics will also provide some plots to asses its performance.
Files
- After RMA or PLIER you will get a link to the normalized expression matrix file. This file contains the normalized intensities for all the genes and samples (arrays) of your dataset.
- After Present-absent calls you will get links to two files. The firs one contains the present-absent matrix file. The second one, the p-values of the "Wilcoxon’s rank test": http://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test used to derive the calls. See [microarray normalization methods](Main areas. Processing).
Plots
For each of the normalization method you did use (RMA or/and PIER) Babelomics will provide:
- Box-Plots representing the normalized intensity distribution for each of the samples (arrays).
- MA-Plots representing the normalized intensity distribution of each sample against a consensus mean sample.
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