Table of Contents

Introduction

Different genes have different expression levels according to their specific function at each condition. Biclustering identifies groups of genes with similar expression patterns under a specific subset of conditions. These conditions may correspond to different time-points, for example in times series expression data.

Input data

The general structure of biclustering input data is a numerical matrix of gene expression measurements. Measurements of the same gene are supposed to be in the same row while measurements under the same experimental condition (same time point for instance) are assumed to be in the same column.

The first column of the data file must have some gene identifier as for example the gene name, and the first row of your data must have some time point identifier. Note that the word GENES should be in the first column and the first row without any comment symbol.

An example of biclustering input data file with the above mentioned structure would look like this: 

    GENES    condition1    condition2    condition3    condition4    condition 5
    YAL001C  -0.19         -0.77         -0.17         -0.19         0.13    
    YAL002W  0.83          -0.01         -0.77         -0.62         0.14   
    YAL003W  -0.36         -0.22         0.22          -0.28         0.41
    YAL004W  1.64          1.14          0.88          -0.07         0.03 
    YAL005C  1.55          1.58          1.34          0.01          0.53 
    ...      ...           ...           ...           ...           ...

Methods

Params

You can select different options of how biclustering analysis should deal with missing values.

Within type of missing values list there are the following options:

You can also compute patterns of opposite sign with 'compute sign changes' option. By default, this option is not selected and your pattern results would be as the following image,

compute_sign_changes_false

Computing sign changes would give you patterns with both signs as you can see in the following example,

compute_sign_changes_true

Sorting

Biclustering results would be sorted by different criteria:

Filter

Biclustering results could be filtered by different criteria:

Worked Examples

Example 1. Cell cycle data

  1. Go to the Babelomics page and select Biclustering analysis from the Expression menu.
  2. Press Online Examples, select the example number 1 and you will see how the parameters and form fields are now filled. As you can notice, this example is prepared to perform a biclustering analysis with default parameters but filters. Selected filters are a minimum of 5 conditions and a maximum p-value of 0.05.
  3. Press run, and wait for your job to be finished.
  4. When the process finishes, a new green job is shown at the right side of the web page. Press it to check your results.

Results

Visualization of the results would be as the following image,

biclustering_example_1_results

Biclusters file has information about all results, including a numerical matrix of expression measurements corresponding to the selected genes in each bicluster.

In the main box of the visualization tool, you would see obtained biclusters. Within the top menu you could change how to visualize them,

matrix_view

expression_view

pattern_view

trend_view

At the menu on the right there is a section with general information about the biclustering analysis, a section with information of a selected bicluster, a section where you can manage different filters and a visualization section.

Pattern coding is the following,

UDN
UpDownNo change

Questions

Run the same example with different options from the Babelomics interface. Compare the results.

Example 2. Heat stress data

  1. Go to the Babelomics page and select Biclustering analysis from the Expression menu.
  2. Press Online Examples, select the example number 2 and you will see how the parameters and form fields are now filled. As you can notice, this example is prepared to perform a biclustering analysis with remove missing values, compute sign changes and filter biclusters with a minimum of 5 conditions and a maximum p-value of 0.05.
  3. Press run, and wait for your job to be finished.
  4. When the process finishes, a new green job is shown at the right side of the web page. Press it to check your results.

Questions

References