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Tutorial Expression. Survival

Ralonso edited this page Mar 22, 2015 · 47 revisions

General

Tutorial

Analysis tools

Worked examples

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INPUT


#### STEPS [1. Select your data](tutorial-expression.-survival#select-your-data)
[2. Select time and series variables](tutorial-expression.-survival#select-time-and-series-variables)
[3. Select test](tutorial-expression.-survival#select-test)
[4. Choose multiple-test correction](tutorial-expression.-survival#choose-multiple-test-correction)
[5. Define a threshold for adjusted p-value](tutorial-expression.-survival#define-a-threshold-for-adjusted-p-value)
[6. Fill information job](tutorial-expression.-survival#fill-information-job)
[7. Press *Launch job* button](tutorial-expression.-survival#press-launch-job-button)

#### OUTPUT - [Input parameters](tutorial-expression.-survival#input-parameters) - [Output files](tutorial-expression.-survival#output-files) - [Significant results](tutorial-expression.-survival#significant-results) - [Continue processing](tutorial-expression.-survival#continue-processing)




INPUT

#####Input data Input data should be a raw counts matrix upload as the data type Data matrix expression. See data types [here](Data Types). #####Online example 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: Survival demo (Cox test).


### STEPS #####Select your data First step is to select your data to analyze.

#####Select time and series variables In this section, we have to select variables relative to the analysis:

  • Time variable This variable contains a value for the survival time associated to each array in your expression data. This values have to be a positive number.
  • Censored variable Censored variable contains indication of whether the time associated to each array has been right-censored or not. This variable can only contain values 0 (meaning censored) or 1 (meaning uncensored).

#####Select test

  • The implemented test is Cox proportional hazards regression model.
  • See [Survival](Differential Expression for arrays) 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](Differential Expression for arrays) 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.


### OUTPUT #### Input parameters In this section you will find a reminder of the parameters or settings you have used to run the analysis.

Output files

  • 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.

Significant results

  • List of genes and heatmap including only significant 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.

Continue processing

You can redirect the output data to other Babelomics tools to continue with your specific analysis pipeline. Concretely, you can:

  • Redirect files to the Single enrichment analysis. For more information about the Single enrichment tool please visit Single Enrichment Tool. For specific information about how to use the tool, see the Single Enrichment page of the tutorial. You can redirect the file of the most UP-regulated genetic features vs. the whole genome, the file of the most DOWN-regulated genetic features vs. the whole genome and the files of the most UP-regulated and DOWN-regulated genetic features.

  • Redirect the file with the statistics to the Gene set enrichment tool. For further information on the Gene set enrichment tool, see Gene Set Enrichment Tool. For specific information about how to use the tool, see the Gene Set Enrichment page of the tutorial.

  • Redirect files to the Network enrichment analysis. For more information about the Network enrichment tool please visit Network Enrichment. For specific information about how to use the tool, see the Network Enrichment (SNOW) page of the tutorial. You can redirect the file of the most UP-regulated genetic features vs. the whole genome, the file of the most DOWN-regulated genetic features vs. the whole genome and the files of the most UP-regulated and DOWN-regulated genetic features.

  • Redirect the file with the statistics to the Gene set network enrichment tool. For further information on the Gene set network enrichment tool, see Gene Set Network Enrichment. For specific information about how to use the tool, see the Functional Gene Set Network Enrichment page of the tutorial.

  • Redirect the truncated data matrix of the significant genetic features to the Clustering tool. For further information on the Clustering tool, see Clustering. For specific information about how to use the tool, see the [Clustering](Tutorial Clustering) page of the tutorial.


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