To detect common expression patterns in samples and genes on the results of differential expression analysis that we had previously obtained, in the case-control experiment where we used RNA-Seq in mice, and in which we were interested in detecting genes differentially expressed between these 2 groups: wild type (WT) and treated with hormone T3.

1. In this file we have the expression data corresponding to a group of differentially expressed genes: dataset.
2. In Babelomics, from the Expression / Clustering module, you upload this data and select the clustering by samples. We chose a method of clustering and distance (to begin with, those that are by default). We assign a name to the job and execute it.
3. We perform the same process but with a clustering by genes.

1. Are there groups of individuals with a similar transcriptomic profile? How many groups appear?
2. Is there any sample that has an anomalous behavior with respect to other subjects?
3. Are genes with a common expression pattern detected?

1. Go to section Expression > Clustering and try several clustering strategies for samples & genes:
   • UPGMA + Euclidean (square)
   • UPGMA + Correlation coeff. (Spearman)
   • Which distance parameter is better for proper clustering?
2. Repeat the analysis using the same distance parameters and SOTA method.
   • SOTA + Euclidean (square)
   • SOTA + Correlation coeff. (Spearman)
   • Do the results change based on the method or the distance parameter?
3. Try to cluster your samples with K-means.
   • Set k-value 6 and use Correlation coeff. (Spearman)
   • Repeat the same analysis with k-value 3.
   • Check the results of K-means.
   • Are the results acceptable?
   • Is the dendrogram representing any hierarchy between the samples?
4. Other strategy with K-means.
   • Set k-value 2 and use Correlation coeff. (Spearman).
   • Can we say that K-means is good to distinguish T3 from WT?