Tutorial Expression. Correlation
Clone this wiki locally
[WORKED EXAMPLES AND EXERCISES]
Input data should be a raw counts matrix upload as the data type Data matrix expression. See data types here.
Here you can load a small dataset 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: correlation.txt.
Select your data
First step is to select your data to analyze.
Select the class to analyse
- First step is to select your data and the variable relative to the analysis.
- This continuous variable provides values associated to each array.
Select the test you want to perform:
- Pearson's correlation
- Spearman correlation
See Correlation section for detailed information about methods.
Choose multiple-test correction
- Several methods are implemented to adjust p.values for multiple statistical tests. This is a significance adjustment when many genes are tested in the same.
- You can select between FDR (False Discovery Rate), Holmm, Hochberg, Bonferroni and BY (Benjamini and Yekutieli).
- See Differential Expression section for detailed information about methods.
Define a threshold for adjusted p-value
You can choose an adjusted p-value between 0 and 1.
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.
In this section you will find a reminder of the parameters or settings you have used to run the analysis.
In the output file link you will find a text file with results of the analysis for all genes. In general this file will have a first column of gene identifiers followed by some more columns of estimate statistics, their respective p-values, raw and corrected (see multiple testing section) and some other results. Since each particular statistical method reports different parameters, the exact layout of the results file depends on the method that you applied to your data.
The way genes are ordered in the results files is thought to be statistically meaningful according to the method used in the analysis. It also tries to be most meaningful for the biological interpretation of the results.
List of genes and heatmap including only significative results.
In any analysis you run, we will provide a grid image representing your data. Each gene is represented in a row and each condition or array is represented in a column. High intensity measurements of gene expression are represented in red colors while blue colors represent lower measurements.
Genes are sorted according to their expression patterns in the same order as they are in the output file. Experimental conditions or arrays are ordered depending on their labels.
Network viewer. Cell Maps visualization of the protein network of significant results. You can choose the number of significant UP- or DOWN-regulated genes to show in the Select number of nodes in the top (resp. bottom) list of the differential expression result box. Colored nodes represent the significant results, whereas not colored ones represent nodes connected to them directly. You can choose different options to visualize in the tool bar of the embedded application. For further information about how to use Cell Maps, visit the Cell Maps User Manual.
|Go back to the Expression page|
|Go back to the Home page|