Tutorial: Transcriptome Clustering
This brief tutorial uses a cancer cell microarray expression dataset to demonstrate AutoSOME transcriptome clustering.
1) If you have not already done so,
Launch AutoSOME
.
2) Download and save the example
cancer line expression dataset
.
(primary dataset: Alizadeh et al. (2000) Nature, 403:503)
3) Select the
Input
button to launch a file browser. Load the example dataset.
4) After loading the dataset, expand
Basic Fields
and change the
Cluster Analysis
field to
columns
. AutoSOME is now configured to cluster transcriptomes rather than individual gene probes. Next, expand
Input Adjustment
and select
Unit Variance
normalization.
5) Since AutoSOME clusters transcriptomes by first generating an all-against-all similarity matrix, the GUI provides three commonly used distance metrics for comparing transcriptomes to one another. Expand
Advanced Fields
and locate the
Fuzzy Cluster Networks
panel. Although Euclidean distance is selected by default, one can also use Pearson's or Uncentered correlation metrics (Let's keep the default for this tutorial). See
FAQ
for an overview of the distance metrics.
6) Run AutoSOME with all remaining parameters set to their default values (i.e. No. Ensemble Runs = 50; P-Value <= 0.1).
7) All clusters in the output tree are shown below using the heat map display. We see that the heat map shows a clustered similarity matrix of cell lines (shown here using a contrast-adjusted rainbow heat map).
8) The necessary output files to create
Fuzzy Cluster Networks
(Edges and Nodes text files) are now available and can be viewed by selecting them from the
Output Files
table.
9) This concludes the mini-tutorial on transcriptome clustering.